QuanCon25 Keynote: The Role of Decision Intelligence in the Intelligent Enterprise

Keynote: QuanCon25

00:50-00:58

Put your hands together for Quantexa’s

00:52-00:58

Founder and CEO Vishal Maria

01:06-01:11

Thank you everyone for coming tonight

01:09-01:12

and thank you to everyone who is streaming

01:12-01:18

in before we kick off I just wanted to

01:16-01:21

do another round of applause to the Lord

01:18-01:25

Mayor of London Alderman Alistair King for a

01:21-01:25

great opening to our QuanCon

01:30-01:38

So Alistair took a bit of my thunder but

01:34-01:42

that's fine as you all know yesterday we

01:38-01:45

publicly announced our Series F funding

01:42-01:45

round

01:48-01:55

[cheers] I'm delighted by my newest

01:52-01:58

investor, my newest partner is here

01:55-02:00

tonight in the room so a big thank you

01:58-02:03

to Ontario Teachers’ Pension Plan and Ara

02:00-02:03

who's sitting right

02:08-02:14

here this is a great moment for everyone

02:11-02:19

who has been involved in the Quantexa

02:14-02:23

journey. Today nine years ago, nine years

02:19-02:26

four days ago, I submitted papers to

02:23-02:29

company house to start the Quantexa

02:26-02:30

Business. A lot of people said what was

02:29-02:34

that moment like

02:30-02:37

it was nerve-wracking, we knew the vision,

02:34-02:41

we knew what we wanted to do and the

02:37-02:43

co-founders of Quantexa, we had the right

02:43-02:49

starting point, it was

02:45-02:51

nervous but I stand here, nine years from then

02:51-02:58

supporting 800+ employees

02:54-03:02

15,000 plus users and more

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importantly on the same vision

03:02-03:09

That vision which is all around connecting data

03:09-03:16

When people think about data,

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They think about mass amount of data,

03:16-03:22

They think about siloed data, they think about

03:19-03:24

swamps of data but when we look at data

03:24-03:30

We see opportunity, we see the value that can be

03:27-03:33

created from data

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and for many engineers, this is prime time.

03:36-03:43

For many engineers who've been working in labs

03:39-03:45

for years and years, this is prime time.

03:43-03:48

And we are on this journey with many of

03:45-03:50

our clients and many of our partners who

03:48-03:52

are here today so before we go to the

03:50-03:53

next piece I just wanted to give a round of

03:53-03:57

Applause, a big round of applause, to each

03:55-04:00

and every one of you here tonight – thank you

04:05-04:14

So we will also celebrate this year

04:10-04:18

being the first British Tech unicorn to be a member of the innovator

04:18-04:25

community at the World Economic Forum

04:21-04:28

This is again another significant

04:25-04:31

testament to each and every one of us

04:28-04:34

who are part of the Quantexa ecosystem

04:31-04:37

What does this provide us?

04:34-04:40

This provides us with another range of sets

04:37-04:43

of innovation that we can do here at

04:40-04:47

Quantexa on behalf of our clients

04:43-04:49

On behalf of our partners or together as a collective ecosystem.

04:49-04:56

this was a substantial moment when it came down to

04:52-05:00

the growth within this year of

04:56-05:04

Quantexa last year. I also announced that

05:00-05:08

we surpassed 100 million annual

05:04-05:11

recurring revenue of our software

05:08-05:11

another big round of applause

05:13-05:19

another major testament to our journey

05:16-05:21

another major testament to your

05:19-05:24

trust when it comes down to the deployment of our platform

05:26-05:34

15,000 users in nine years

05:31-05:34

another round of applause

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But like I always say and my team get fed up of me saying this

05:41-05:49

plenty done but plenty more to do

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15,000 in nine years, we should be 30,000 in two years

05:49-05:56

that's the ambition that we collectively

05:53-05:59

as a company, as an ecosystem have for our products and capabilities

06:00-06:04

Now we are on this transformation

06:04-06:07

this transformation around connecting data

06:07-06:12

and bringing together this once-in-a-lifetime

06:09-06:15

opportunity of both human intelligence

06:12-06:17

and machine intelligence together

06:15-06:20

we're going to hear today from some of the experts

06:17-06:22

who are working on the platform

06:20-06:27

who are working with this ability to

06:22-06:29

bring both AI and human intelligence as well as

06:27-06:30

trusted data to make better trusted decisions

06:30-06:37

This is a once in a-lifetime

06:34-06:40

opportunity and as an ecosystem we are

06:37-06:44

pioneering this journey but the North Star

06:40-06:47

has not changed since we started Quantexa

06:44-06:50

we have been on that mission

06:47-06:53

the ability to curate trusted graph data

06:50-06:56

that's what we do and then we solve the

06:53-06:59

hardest problems with our clients that

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North Star is absolutely as strong as it

06:59-07:06

was when I started to where we stand today

07:02-07:10

Last year when I stood up here

07:06-07:13

I said to everyone Quantexa in 25 is going

07:10-07:15

to have the most amount of innovation

07:13-07:19

We have ever had as a business

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and at that time both my CTO and co-founder Jamie

07:22-07:28

as well as my CPO Dan both looked at me and said

07:25-07:31

that's a lot we're going to do

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What I'm delighted to say as we stand here today

07:31-07:38

we didn't just do it but we did it with our partners with our clients

07:38-07:45

And I'm delighted to say we launched Quantexa Unify on Microsoft Fabric

07:45-07:50

We stood here last year with that huge

07:47-07:53

partnership and we announced it at QuanCon24

07:50-07:56

and I stand here today with my partners at Microsoft and we launched it

07:56-08:00

A huge round of applause

08:04-08:09

Last year we mentioned Q Cloud

08:07-08:10

The ability to take our platform and run it as a cloud

08:10-08:18

service working with our collective ecosystem

08:13-08:18

We have launched Q Cloud

08:21-08:29

Today two and a half years ago I had a

08:25-08:30

Very, very exciting meeting with one of my clients

08:30-08:36

it was a very large insurer who said to me

08:33-08:39

Vish you know you do that entity resolution and that graphing (yes)

08:39-08:44

I want to talk to it. I want to ask it a question

08:44-08:49

I want to ask it a question in natural language

08:46-08:52

and I want that data to talk to me back

08:49-08:54

we took that idea we went to another partner

08:54-08:59

who's going to present later on today, they said the

08:57-09:01

same question - I want to be able able to

08:59-09:03

talk to trusted data

09:03-09:11

we huddled, we innovated and today we launch Q Assist.

09:14-09:20

Now we are supporting over 100 clients globally on that mission

09:22-09:27

of connecting data and today I am delighted that many of those thought leaders

09:27-09:32

are going to be presenting their part of the story today in front of all of us

09:34-09:38

so I want to say again: a big thank you to all of our

09:36-09:40

guest speakers who are coming out tonight to

09:38-09:41

talk about their experiences of the platform

09:40-09:45

as well as areas around the platform on using better trusted data

09:51-10:00

Now, AI is only a sharp as the data you feed it

09:55-10:03

AI, generative or predictive, is only as sharp as the data you feed it

10:03-10:08

And it's so critical that when you are

10:05-10:09

building these data products that they can be

10:09-10:17

seamlessly connected to any form of AI

10:13-10:21

We saw last year that a number of projects failed on the first cycle

10:21-10:26

that first cycle could have been in experimental form in a PO

10:26-10:32

or some form of use case, they failed

10:30-10:34

and the common theme as we talk to those

10:32-10:37

clients or talk to those partners who talk to those prospects

10:34-10:38

they failed because of data

10:38-10:44

When we started Q, we worked in a regulated environment

10:44-10:50

we built our platform in that regulated mode

10:47-10:52

Being able to take an AI model through model

10:50-10:55

risk management, model governance is not easy

10:52-10:56

Any of our compliance professionals in the room will be nodding

10:56-11:01

very hard right now

10:59-11:05

it's not easy so that ability to take predictive

11:01-11:07

AI and generative through MRM is something

11:05-11:10

that we have been working on for over

11:07-11:13

the last 18 months and it's critical

11:10-11:16

we're also seeing that many of

11:13-11:18

our clients are not just looking at the platform vertically

11:18-11:23

we need to break down those silos

11:21-11:26

one of our clients said we should

11:23-11:30

be able to take your data products and

11:26-11:32

fulfill the entire customer life cycle

11:30-11:34

if that's prospecting to clients if

11:32-11:36

that's onboarding clients if that's

11:34-11:38

managing risk or

11:36-11:41

offboarding we need to start making it

11:38-11:44

from the verticals to the

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horizontal and again in the last two

11:44-11:48

years especially in the last 18 months

11:46-11:50

and most definitely in the last 12

11:48-11:53

there's been a relentless focus at

11:50-11:56

Quantexa to get graph to get the

11:53-12:00

resolution horizontal and service a

11:56-12:00

number of use cases

12:01-12:09

Now where do we

12:03-12:12

stand with data management? The old gospel

12:09-12:16

The 20-year elephant in the room

12:12-12:20

Data management has now become a

12:16-12:22

process when it comes to data

12:20-12:24

democratization and we are working with

12:22-12:26

a number of you as you all know in

12:24-12:29

resolving entities, resolving graphs and

12:26-12:32

having them as a product, have them as a

12:29-12:35

utility and service a number of use

12:32-12:38

cases and again there's been a

12:35-12:40

tremendous amount of effort in R&D to

12:38-12:42

make that more seamless more

12:40-12:46

frictionless and most importantly to

12:42-12:50

reduce the time to value the second

12:46-12:53

piece, AI agents the ability to talk the

12:50-12:56

ability to automate automation has now

12:53-12:59

become a key aspect within AI that

12:56-13:03

ability once again to throw data in in a

12:59-13:06

trusted environment has now become not a

13:03-13:08

nice to have but almost critical to have

13:06-13:12

when it comes down to getting the

13:08-13:16

value and then I'll come to the third

13:12-13:18

aspect when we started we knew about

13:16-13:21

business intelligence (BI) we've been

13:18-13:24

working with a number of analysts right

13:21-13:28

across the globe for the transformation

13:24-13:32

from BI to DI - the Decision Intelligence

13:28-13:36

growth the quadrant, the TAM is

13:32-13:39

significant and we here as an ecosystem

13:36-13:39

are all part of that journey.

13:41-13:49

when I started Q we had a

13:45-13:52

phrase deliver to your promise

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last year we conducted a

13:52-13:56

survey with an independent analyst

13:54-13:58

Forester who went and spoke to a number

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of our clients, a number of our partners

13:59-14:04

around the use of Q

14:01-14:08

the results were

14:04-14:10

tremendous this is what you are saying

14:08-14:12

significant millions of saves

14:10-14:15

significant millions of identified

14:12-14:17

opportunity as well as better risk

14:15-14:21

management and control a significant

14:17-14:21

part to the success of our

14:22-14:30

ecosystem so tonight is our night

14:27-14:32

We have closed series F

14:32-14:38

That’s yesterday. Tonight we are on this

14:35-14:40

transformation together we have got some

14:38-14:43

of the most smartest people in this room

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streaming in who are working with data

14:43-14:50

who get a kick out of data and most

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importantly who are transforming the way

14:50-14:54

people make decisions

14:52-14:57

I want to thank each and every

14:54-14:59

one of you for tonight for coming

14:57-15:04

tonight it means a tremendous amount to

14:59-15:07

me and my team. Q is nothing without

15:04-15:11

us and what I will say we've done a lot

15:07-15:13

to get to where we are but we're just

15:11-15:15

starting this revolution

15:21-15:29

Thank you. So without further ado

15:24-15:32

The man who's put the plan together

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the man who has come in gripped

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transformed the road map his team have been

15:38-15:43

pioneers, I'm not talking about

15:40-15:43

myself, there is only one Dan Higgins

15:43-15:48

Come on Dan

15:50-15:56

Thank you Vish

15:52-15:57

Thank you all, like Vish said I'm

15:56-16:00

the chief product officer here at

15:57-16:03

Quantexa, my team and I look after the

16:00-16:06

product strategy and the road map it's

16:03-16:06

great to be with you all here

16:12-16:17

tonight the operational revolution of

16:14-16:20

data is about making it easier to do

16:17-16:23

more with data and our focus here at

16:20-16:26

Quantexa is helping customers

16:23-16:30

transform and modernize the way they

16:26-16:33

manage data operationalize insights and

16:30-16:37

realize value from the data and AI

16:33-16:41

Investments they're making so what's the

16:37-16:43

secret putting data in context our

16:41-16:46

decision intelligence platform is

16:43-16:49

powered by a real world view of

16:46-16:52

connected contextual data enriched to

16:49-16:56

support transformational decision-making

16:52-16:59

we call this the contextual

16:56-17:02

fabric the contextual fabric

16:59-17:05

helps you connect data at scale build

17:02-17:09

more performant models activate trusted

17:05-17:13

data products and adopt transparent

17:09-17:16

AI here's how the platform works first

17:13-17:19

it unifies Silo and fragmented data

17:16-17:20

using our category leading entity

17:19-17:24

resolution and graph

17:20-17:27

technology next it contextualizes that

17:24-17:29

data to build real world representation

17:27-17:32

that becomes available on on the

17:29-17:35

contextual Fabric and finally users

17:32-17:37

leverage this fabric running contextual

17:35-17:39

analytics to understand the critical

17:37-17:41

decisions they need to make and

17:39-17:43

uncovering insights to arrive at the

17:41-17:46

best outcomes and

17:43-17:48

impact by activating the contextual

17:46-17:50

fabric organizations unlock the full

17:48-17:54

potential of

17:50-17:56

AI they drive smarter decisions and gain

17:54-17:59

a competitive advantage in an

17:56-18:03

increasingly datadriven world

17:59-18:05

so last year I stood on quanan stage and

18:03-18:06

walked you through our product road map

18:05-18:09

for the first

18:06-18:11

time I summarized our vision I talked

18:09-18:12

about our product strategy mentioned

18:11-18:15

some big

18:12-18:18

bets we've stayed laser focus over the

18:15-18:20

last 12 months and today I'm very proud

18:18-18:22

to be showing you the capabilities that

18:20-18:23

you can now access in the latest version

18:22-18:25

of the

18:23-18:27

platform after that I'm going to dive

18:25-18:29

into a couple of interesting exciting

18:27-18:32

Innovations we're work working on that

18:29-18:33

are become available this year so let's

18:32-18:35

get

18:33-18:39

started the first delivered Innovation I

18:35-18:42

want to talk about is Advanced language

18:39-18:45

parsers we know the challenges of

18:42-18:48

operationalizing data at scale this

18:45-18:50

becomes even harder as you expand data

18:48-18:53

Landscapes across different

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jurisdictions different geographies

18:53-18:59

handling language handling multiple

18:55-19:01

languages and scripts at huge volumes

18:59-19:04

that's why we're making it easier than

19:01-19:07

ever before to leverage multilang uh

19:04-19:08

multilingual data with our new Advanced

19:07-19:11

language

19:08-19:14

parsers these enable you to onboard

19:11-19:16

non-latin script data such as Japanese

19:14-19:18

Chinese and

19:16-19:21

Arabic as I'm sure you all agree the

19:18-19:23

evolving geopolitical landscape makes

19:21-19:24

this flexibility even more important

19:23-19:26

than

19:24-19:29

ever next

19:26-19:32

up quantex anology

19:29-19:35

graphs most of you will appreciate the

19:32-19:37

the promise and the power of graphs and

19:35-19:40

the impact that they have on unifying

19:37-19:43

data uncovering hidden risk and

19:40-19:46

opportunities and building contextual

19:43-19:49

views but as graphs grow in size and

19:46-19:51

complexity the computational resource

19:49-19:54

and the technical expertise required to

19:51-19:57

build manage and scale them can be

19:54-19:59

prohibitive so we've solved this for you

19:57-20:02

with quantex anology

19:59-20:03

graphs built on our category leading

20:02-20:05

entity

20:03-20:08

resolution our knowledge graphs connect

20:05-20:10

data internal and external and create

20:08-20:13

Dynamic views of the entities the

20:10-20:16

connections the relationship you care

20:13-20:18

most about across your entire data

20:16-20:20

population in fact a tier one global

20:18-20:24

bank we've been able to scale their

20:20-20:26

knowledge graphs to over a billion nodes

20:24-20:29

for any non- techies in the room that's

20:26-20:29

bloody impressive

20:29-20:34

our knowledge graph equip analysts and

20:32-20:37

data scientists with the ability to

20:34-20:39

understand and use the context

20:37-20:43

represented in the graphs with the

20:39-20:43

models they build and the decisions they

20:43-20:51

make we've also been working with entity

20:48-20:53

streaming our customers require

20:51-20:55

real-time decision-making and

20:53-20:59

streamlined access to the data and

20:55-21:00

insights they need when they need it

20:59-21:02

our entity streaming supports

21:00-21:05

event-based architectures for real-time

21:02-21:08

use cases like Master data management

21:05-21:11

fraud detection kyc

21:08-21:15

processes they're built to stream ultra

21:11-21:17

high version ultra high volumes of data

21:15-21:19

so you can ensure the most up-to-date

21:17-21:22

versions of the entities are easily

21:19-21:25

accessible by data teams and downstream

21:22-21:28

systems. To give you a couple examples

21:25-21:30

the world's largest retailer today is

21:28-21:32

using this capability to support fraud

21:30-21:35

detection at the point of

21:32-21:37

sale and in another part of the world an

21:35-21:40

APAC government agency focused on

21:37-21:43

strengthening National borders, is using

21:40-21:45

it to process over 2,000 verifications

21:43-21:45

per

21:45-21:51

minute. In addition to those new

21:49-21:54

Capabilities, we've been working on

21:51-21:57

improving the overall user

21:54-21:59

experience and what we've done here is a

21:57-22:02

complete overhaul of our search

21:59-22:04

functionality making it easier to find

22:02-22:07

exactly what you're looking for through

22:04-22:10

intuitive search and filter

22:07-22:12

capabilities and like many of you we've

22:10-22:15

been growing globally with our

22:12-22:18

customers so we know how important it is

22:15-22:20

to enable teams worldwide to interact

22:18-22:21

with our decision intelligence platform

22:20-22:24

in their native

22:21-22:26

language. So, to support this, we've

22:24-22:29

evolved our user interface to offer

22:26-22:31

multilingual support across all of our

22:29-22:34

Solutions and platform

22:31-22:36

deployments. This allows users to work

22:34-22:39

efficiently in familiar

22:36-22:42

languages leading to faster onboarding,

22:39-22:45

reducing training time, increasing

22:42-22:48

Engagement, smoother deployments and,

22:45-22:52

ultimately, a better user

22:48-22:52

experience. Lastly,

22:58-23:01

here we

23:01-23:07

Go, I think we're out of, here we

23:04-23:10

go. Lastly, as part of our ongoing

23:07-23:12

commitment to accelerate time to Value,

23:10-23:15

we've been focused on streamlining

23:12-23:17

updates. So, from Direct customer feedback,

23:15-23:19

I've had conversations with many of you,

23:17-23:21

from engagement with our quantexa

23:19-23:23

community, through feedback from our

23:21-23:26

teams in the field, and many of our

23:23-23:28

partners, we know that streamlining

23:26-23:30

updates and quicker access to the new

23:28-23:33

and latest features continually comes to

23:30-23:36

the top of your request list. We're

23:33-23:38

listening; we hear you. That's why we've

23:36-23:41

been running a program to lower your

23:38-23:44

total cost of ownership and remove

23:41-23:49

barriers to Innovation reducing time to

23:44-23:52

value and speeding up deployments and

23:49-23:54

upgrades. With automated migration and

23:52-23:56

updates, we've cut the time it takes our

23:54-24:00

customers to move to the latest version

23:56-24:00

of the platform by up to 75% -

24:00-24:05

it's pretty

24:02-24:08

good! It's very good. All right, so you can

24:05-24:10

see, so now you can access all this

24:08-24:12

innovation we've spoken about this

24:10-24:15

evening in the current version of the

24:12-24:17

platform available now. If you're

24:15-24:19

currently working on an older version

24:17-24:21

and you want access to the newest

24:19-24:24

capabilities, speak to your account

24:21-24:28

management rep - we'll make it

24:24-24:30

happen. All right, so, you can see we've

24:28-24:32

been quite busy this year but we're

24:30-24:36

not

24:32-24:39

stopping. A lot done a lot to do. I'm also

24:36-24:41

excited to share with you some amazing

24:39-24:44

new innovations we're currently working

24:41-24:45

on become later to become available later

24:44-24:48

this

24:45-24:51

year. First, we all know that unstructured

24:48-24:54

data is no longer a nice to have. It's a

24:51-24:57

strategic asset that enables

24:54-25:00

organizations to enhance risk management,

24:57-25:01

improve customers experience, and gain a

25:00-25:05

competitive

25:01-25:09

advantage. But processing this

25:05-25:13

data is inherently complex and can be

25:09-25:16

costly - enter Quantexa’s new NLP pipeline.

25:13-25:18

This makes it easier for customers to

25:16-25:21

process and analyze textual data at

25:18-25:23

scale in multiple languages all within

25:21-25:25

the Quantexa

25:23-25:28

Platform. This allows you to unlock

25:25-25:30

additional context by expanding your

25:28-25:33

Universal data to include unstructured

25:30-25:37

data from critical sources like internal

25:33-25:39

documents, intelligence reports, ENT data

25:37-25:43

news content, and much

25:39-25:45

more. In addition, our fine-tuning has

25:43-25:48

enabled us to offer the power and speed

25:45-25:49

of hyperscale LLMs at the cost of small

25:48-25:52

local

25:49-25:53

models. Currently a few early adopter

25:52-25:56

government agencies and regulatory

25:53-25:57

bodies are using this to supercharge

25:56-25:59

their investigation and monitoring

25:57-26:01

capabilities but there's a lot more to

25:59-26:01

come

26:01-26:06

here. Next, let's talk about workflow and

26:04-26:08

case

26:06-26:11

management. Your decision-making

26:08-26:14

processes need to be consistent,

26:11-26:17

collaborative, adaptable, and with

26:14-26:19

improved traceability to meet the needs

26:17-26:21

of multiple use cases especially in

26:19-26:24

regulated

26:21-26:26

environments. as part of our ongoing

26:24-26:29

innovation, we will be delivering

26:26-26:32

workflow and case management

26:29-26:34

in our platform and Cloud

26:32-26:37

Solutions. This is end-to-end workflow

26:34-26:40

builder and enhances collaboration

26:37-26:43

streamlines processes, improves

26:40-26:45

traceability and explainability of

26:43-26:48

decisions. These new workflow

26:45-26:49

capabilities will enable you to build

26:48-26:52

and

26:49-26:56

design flexible alerting and reporting

26:52-26:59

processes and handle things such as

26:56-27:02

escalation, suspicious activity report

26:59-27:05

filing, claims processing, and much more

27:02-27:05

all within the

27:06-27:11

Platform. Earlier Vish talked about the

27:10-27:15

fact that traditional data management is

27:11-27:17

no longer sufficient. I'll be more blunt:

27:15-27:21

Traditional data management is

27:17-27:24

dead. You need a contextual fabric that's

27:21-27:26

trusted current and accessible by all

27:24-27:28

those who need it in your

27:26-27:31

organization. We're evolving our data

27:28-27:33

management suite to support data teams

27:31-27:36

in understanding the quality of critical

27:33-27:39

source data with AI powered quality

27:36-27:41

assessments and automated root cause

27:39-27:44

analysis. Our modern data management

27:41-27:46

solution enables proactive management of

27:44-27:50

master data with intuitive stewardship

27:46-27:53

workflows and helps data teams monitor

27:50-27:55

enhance and maintain quality data while

27:53-27:57

also allowing changes to be published

27:55-28:00

back to source records in systems. And

27:57-28:02

with this trusted data foundation, you

28:00-28:05

can be more confident in the decisions

28:02-28:08

you make and gain the ability to create

28:05-28:10

accessible and scalable data products

28:08-28:12

that democratize data access across your

28:10-28:12

entire

28:13-28:18

organization. So, as you can see, we have

28:15-28:20

some amazing things on the way, but

28:18-28:22

that's not all, there's two big

28:20-28:24

announcements we haven't yet

28:22-28:27

discussed. The

28:24-28:29

first, our context aware generative AI

28:27-28:32

solution Q Assist - Vish referenced it

28:29-28:35

before - it's launching in

28:32-28:37

April. This powerful new capability helps

28:35-28:39

organizations augment trusted decision-

28:37-28:42

making across teams of frontline and

28:39-28:45

information workers by democratizing

28:42-28:47

access to trusted data in the contextual

28:45-28:50

fabric. It uses a natural language

28:47-28:51

interface you talk to your data and

28:50-28:54

allows you to tap into the power of any

28:51-28:55

open or commercially licensed generative

28:54-28:59

AI

28:55-29:01

model. Second, I'm excited to let you know

28:59-29:04

we are delivering Quantexa Cloud. It's a

29:01-29:07

comprehensive suite of native SaaS,

29:04-29:09

industry specific offerings delivered on

29:07-29:12

Microsoft

29:09-29:14

Azure. By combining the security

29:12-29:17

scalability and flexibility of Azure

29:14-29:19

with Quantexa’s flagship solutions,

29:17-29:21

Quantexa Cloud will empower

29:19-29:22

organizations to unlock the full

29:21-29:25

potential of their

29:22-29:27

data while accelerating time to value.

29:25-29:29

Our first solution, our first service in

29:27-29:32

the space.

29:29-29:34

We're going to debut it tonight: Quantexa

29:32-29:34

Cloud

29:35-29:42

AML. This is a transformational end-to-end

29:38-29:44

AML solution built with and for midsize

29:42-29:46

banks in the US. Tt's available today in

29:44-29:48

customer preview. But we're not just

29:46-29:49

going to talk about these two exciting

29:48-29:52

announcements - we're going to show them

29:49-29:53

to you. So, I'd like to bring up on the

29:52-29:56

stage to talk a bit more about our

29:53-29:59

strategy and platform and show you these

29:56-30:01

two new exciting things, Jamie Hutton, our

29:59-30:01

Chief Technology

30:07-30:11

Officer. Cool, thanks very much Dan. So,

30:09-30:13

I'm going to take you guys through the

30:11-30:15

technology showcase and as ever I'm

30:13-30:18

entrusted to do all the live demos,

30:15-30:19

nerve-wracking as they may be. So, the

30:18-30:23

first one we wanted to take you through

30:19-30:25

was Q assist. We've said, and we've

30:23-30:27

reiterated this a few times, to be able

30:25-30:30

to make good use of generative AI, we

30:27-30:32

believe, you must have a trusted data

30:30-30:35

foundation. We've talked about the

30:32-30:38

contextual fabric on which this is based

30:35-30:40

and that's a view of real-world entities

30:38-30:43

connected into a graph. And our view is,

30:40-30:45

if you can give that insight to a large

30:43-30:46

language model, it can fundamentally

30:45-30:49

answer much more interesting and

30:46-30:50

powerful questions. But we also

30:49-30:52

understand the importance of trust and

30:50-30:54

security. We didn't want to just create

30:52-30:57

another co-pilot that people use for a

30:54-30:59

few weeks and then leave in the dust.

30:57-31:01

So, to bring this to life, I'm going to

30:59-31:04

start you with a demo where I'm going to

31:01-31:07

essentially compare two parts. On the

31:04-31:09

left hand side we've got a traditional

31:07-31:12

large language model with RAG. So, what

31:09-31:14

that's doing is it's able to ask a

31:12-31:16

set of data to retrieve information as I

31:14-31:19

make my request and on the right we've

31:16-31:22

got a Quantexa one, which is the same

31:19-31:24

exact data, but underpinned by that

31:22-31:26

contextual fabric. So, what we're going to

31:24-31:28

do is, we're going to start - and forgive

31:26-31:30

me for not typing this live because no

31:28-31:32

one wants to see me type - I'm just going

31:30-31:35

to start a very simple question which is,

31:32-31:37

‘Search for businesses called Pringle and

31:35-31:39

Pratt’ on the left-hand side. Now, in a

31:37-31:41

traditional system, of course, imagine

31:39-31:43

you're a bank, you're going to have

31:41-31:45

many different systems that will have

31:43-31:47

potential references to a business

31:45-31:50

called Pringle and Pratt. So, it's going

31:47-31:52

to tell us, ‘We've got four records here

31:50-31:54

in the customer system’. You'll notice

31:52-31:56

Enterprises UK Property Services are all

31:54-31:58

clearly different businesses. We've also

31:56-32:01

got links to a corporate registry system

31:58-32:02

which has those four plus an extra one.

32:01-32:05

We've got a couple of records from the

32:02-32:08

Panama papers, and then of course you’ve

32:05-32:10

probably got tens, hundreds, maybe even

32:08-32:14

thousands of payments mentioning Pringle

32:10-32:16

and Pratt. Is this useful? Is this

32:14-32:20

interesting? Can I gain a lot of insight

32:16-32:22

from it? The answer is no. So, what we've

32:20-32:25

actually done in Quantexa is underpin the

32:22-32:28

exact same question but this time using

32:25-32:30

the knowledge graph that sits underneath

32:28-32:32

Quantexa. So instead of it coming back

32:30-32:35

with a whole load of disconnected

32:32-32:38

records, what it's now saying is actually

32:35-32:41

there are five entities, which one do you

32:38-32:43

want? Hopefully you can see that's much

32:41-32:45

more insightful much more understandable

32:43-32:47

from an end user perspective. But I want

32:45-32:49

to take that a step further? That's just

32:47-32:52

the entities. What if we wanted to

32:49-32:54

understand the graph itself? What I'm now

32:52-32:56

going to do is ask a much more

32:54-32:58

complicated question which is, ‘Describe

32:56-33:00

the corporate hierarchy of Pringle and Pratt

32:58-33:03

UK limited.’ So, I've selected one of the

33:00-33:05

businesses at the top and I'm asking it

33:03-33:07

to describe the hierarchy. Now this is

33:05-33:09

actually pretty complicated. First off,

33:07-33:11

it's got to pick out the one that I was

33:09-33:13

looking for, it's then got to use the

33:11-33:15

graph to expand and understand, ‘What are

33:13-33:17

the relationships between all of these

33:15-33:19

Entities?’ And let's see what it's

33:17-33:21

returned. So, here's Pringle and Pratt UK

33:19-33:23

Limited. You can see its parent company is

33:21-33:25

Accelerate Group. We've got some

33:23-33:27

shareholders. Then we can see all of the

33:25-33:29

subsidiaries of Pringle and Pratt. You

33:27-33:31

could only get this answer if you

33:29-33:34

underpin a large language model with

33:31-33:37

something like a Quantexa Knowledge

33:34-33:39

Graph. As I go forward, I'm now going to

33:37-33:41

ask, perhaps flipping it as though I'm

33:39-33:44

a relationship manager looking for new

33:41-33:46

business opportunities. So, we've got a

33:44-33:48

whole load of records here, which ones

33:46-33:50

of these are not customers? So, clearly,

33:48-33:52

from my customer source I saw a bit

33:50-33:53

earlier there were quite a few of them

33:52-33:55

that we already have a banking

33:53-33:57

relationship with but what we can see

33:55-33:59

here is there are two parts of this

33:57-34:01

corporate hierarchy that I currently

33:59-34:03

have no banking relationship with. So,

34:01-34:05

clearly these are, if we believe that

34:03-34:07

this is a good group of customers, these

34:05-34:08

are great targets for acquisition and

34:07-34:10

when we see the HSBC stuff that

34:08-34:12

is going to be on stage later this

34:10-34:15

is a great example of how they're

34:12-34:17

leveraging it. But we can also flip it

34:15-34:20

around. So, that's great that's all the

34:17-34:22

positives. But what are the risks associated

34:20-34:23

with Pringle and Pratt? So, what this is

34:22-34:25

going to do is flip the problem on its

34:23-34:26

head. It's going to say, okay, rather than

34:25-34:29

looking for the positives, let's look for

34:26-34:30

some of those negatives that might exist.

34:29-34:32

Now this leverages the Quantexa

34:30-34:34

scoring models under the hood so it's

34:32-34:36

using all of the risk indicators that

34:34-34:38

are built into the uh platform I'm not

34:36-34:39

going to go through all of these because

34:38-34:41

there's quite a lot of them but

34:39-34:43

ultimately there are a set of risk

34:41-34:45

indicators against each of these

34:43-34:47

companies. For example, you might see that

34:45-34:49

Pringle and Pratt Property Services

34:47-34:51

Limited made a loss of 13.6 million last

34:49-34:55

year. You can see some of them on the

34:51-34:56

offshore Panama papers Etc. But the genAI

34:55-34:58

system is of course very good at

34:56-35:00

summarizing. So, what it's saying is

34:58-35:02

across the entire corporate hierarchy of

35:00-35:04

this group of customers, we've got some

35:02-35:06

generic SIC codes, very generic business

35:04-35:07

types, we've got high risk jurisdictions

35:06-35:09

popping up, we've got a link to the

35:07-35:11

Panama Papers, and we've got those High

35:09-35:13

Financial losses. So, perhaps as a

35:11-35:14

relationship manager, I don't want to

35:13-35:16

give them a ring and try and acquire

35:14-35:18

them as a new customer. And the final

35:16-35:21

thing we could do if we wanted to you

35:18-35:23

can of course within Q assist

35:21-35:25

we can have templated reports. So in

35:23-35:26

this case, we can have a due diligence

35:25-35:28

report for Pringle and Pratt that we've

35:26-35:30

set up here. So what this is going to do

35:28-35:32

is summarize everything about Pringle

35:30-35:34

and Pratt UK limited that we've gleaned

35:32-35:36

from the both internal, the external dat,a

35:34-35:38

all of the payment information, all of

35:36-35:41

the risks that we looked at a little bit

35:38-35:43

earlier on, and obviously generating a

35:41-35:46

summary. So hopefully that was useful.

35:43-35:49

That was the first part of the demo Q

35:46-35:51

Assist. Just to remind everyone really

35:49-35:53

Q assist is only able to do and to

35:51-35:55

answer the questions we were just

35:53-35:56

looking at by being combined with that

35:55-35:58

contextual Fabric and having that

35:56-36:01

connected view of the world under the

35:58-36:04

hood. In the same way as you and your

36:01-36:07

teams need data to make decisions, so

36:04-36:08

does an LLM. So, by using that resolved view

36:07-36:10

of the world we were able to answer much

36:08-36:12

more powerful questions but also open

36:10-36:15

this up to a wider set of users, users

36:12-36:16

who want to be able to talk to their

36:15-36:18

data and this is driving

36:16-36:20

transformational change at our customers

36:18-36:22

across all sectors: Banking, Insurance,

36:20-36:24

Public Sector and

36:22-36:28

more. So that was Q

36:24-36:30

Assist. Next on to Quantexa Cloud AML.

36:28-36:32

So, the world's largest banks, and I know

36:30-36:35

there are many in this room today and

36:32-36:37

streaming live, many of you use

36:35-36:40

Quantexa to deliver the most advanced

36:37-36:42

AML detection available on the market

36:40-36:44

but we've always had the ambition that

36:42-36:45

we wanted to take this advanced

36:44-36:48

capability and make it available to

36:45-36:49

other segments of the market - after all

36:48-36:52

money launderers don't just target the

36:49-36:54

tier ones! And this is why we've worked

36:52-36:57

hand-in-hand with a set of community and

36:54-37:00

midsize banks to build Quantexa Cloud

36:57-37:02

AML. This enables them to fight financial

37:00-37:04

crime more efficiently and effectively

37:02-37:07

and it's an end-to-end Software as a

37:04-37:10

Service solution which brings innovative

37:07-37:12

new features including data

37:10-37:14

sharing. This allows the midsize banks to

37:12-37:16

be more efficient and effective in their

37:14-37:18

financial crime detection and I'm going

37:16-37:18

to give you a sneak

37:19-37:27

preview. So, switching over to the other

37:23-37:29

demo, Quantexa Cloud AML. So, for those

37:27-37:31

of you who've seen Quantexa before,

37:29-37:33

you'll never have seen the case

37:31-37:35

management and workflow that is now

37:33-37:37

fully integrated. And this is the screen

37:35-37:40

that you might be presented with if you

37:37-37:42

are a manager in a financial crime team.

37:40-37:43

A set of alerts have been produced. What

37:42-37:45

I can of course do is take those and

37:43-37:47

allocate them to my team as I see fit or

37:45-37:49

we can auto allocate them based on

37:47-37:50

what's coming back from the system. What

37:49-37:52

I'm going to do is drill into my own

37:50-37:55

work items. These are the ones that have

37:52-37:57

been assigned to me. Here we have

37:55-37:59

three items that I've been tasked

37:57-38:01

with with looking at and I'm going to go

37:59-38:02

into the top one which is Harmony Health

38:01-38:04

Spar

38:02-38:05

LLC. The first thing I want to do is just

38:04-38:07

get a quick summary you can see the

38:05-38:09

details of the business on the right

38:07-38:12

hand side and then we can see a set of

38:09-38:13

risk indicators. So, we can see the

38:12-38:15

classic ones that you would find in a

38:13-38:17

traditional money laundering solution -

38:15-38:20

things like round dollar amounts,

38:17-38:22

things like cash structuring - but we can

38:20-38:25

also see, interlaced with this, a set of

38:22-38:28

more advanced detection, for example,

38:25-38:29

having a cyclical flow of funds a

38:28-38:32

very interesting pattern that you can

38:29-38:35

only look for if you've got entity

38:32-38:37

resolution and graphing as your as your

38:35-38:39

baseline. So, what I can now do open up

38:37-38:41

this case. So, we've got the full scores

38:39-38:42

on the left I'll just expand some of

38:41-38:44

these so you can kind of see some of

38:42-38:47

them. Just to drill in very quickly,

38:44-38:49

rapid movement of funds so $100,000 very

38:47-38:51

round amount obviously has gone in

38:49-38:53

we've got that connection to cyclical

38:51-38:54

flow of funds, we've got an indirect

38:53-38:56

connection to watch list, so this is

38:54-38:58

looking multiple hops out from the

38:56-39:00

entity that that we're looking at,

38:58-39:03

this particular spa, and saying that it's

39:00-39:05

connected to a watch list.

39:03-39:08

Integrated into this solution is open

39:05-39:09

sanctions as out of the box so that's

39:08-39:12

how we can detect all of that. We've also

39:09-39:15

got cash structuring. So, this is where

39:12-39:17

we've got lots of small deposits all

39:15-39:19

below the thresholding limit that have

39:17-39:21

been added up to a larger amount so

39:19-39:23

they're trying to evade that sort of

39:21-39:25

auto reporting threshold and of course

39:23-39:26

round dollar amounts. Over on the right

39:25-39:28

hand side we've got things like the

39:26-39:31

subject, we've got the link parties, and

39:28-39:33

of course we've got the network. Now,

39:31-39:35

in many cases you will not need to

39:33-39:37

use the network if it's a simple case

39:35-39:39

but in this case we've got a cyclical

39:37-39:40

flow we've got something which is

39:39-39:42

network based so that clearly means we

39:40-39:44

want to take a look at it from a network

39:42-39:46

perspective. Now I'm not going to go

39:44-39:48

through the full detail of this case,

39:46-39:49

it's a super interesting example, but I

39:48-39:52

will tell you very briefly what's

39:49-39:55

happening here. On the bottom right we

39:52-39:58

have $50,000 deposited in cash across 18

39:55-39:59

transactions all below that thresholding

39:58-40:02

limit - so they're just trying to make

39:59-40:04

sure that none of those hit the levels.

40:02-40:06

That money lands in Harmony Health Spar

40:04-40:07

down the bottom and it's almost

40:06-40:10

immediately bounced, almost all of it,

40:07-40:13

over to A to Z Accounting and Tax

40:10-40:15

Services. Now in its own right, I guess, if

40:13-40:17

you look at some some of the more

40:15-40:19

traditional AML Solutions, it would be

40:17-40:21

very hard for the system to work out

40:19-40:24

whether that is a real or a bad

40:21-40:25

transaction but in this case actually

40:24-40:27

it's quite easy to work out that it's

40:25-40:30

pretty bad because what happens is that

40:27-40:32

money lands in A to Z Accounting and Tax

40:30-40:35

Services and is immediately bounced back

40:32-40:37

on to a person called Anthony Rose. Why

40:35-40:39

is that interesting? Well, Anthony Rose

40:37-40:42

has his home address which is exactly

40:39-40:43

the same address as this Spa. So what

40:42-40:45

they've done is they've bounced this

40:43-40:48

money through - in this case an

40:45-40:50

accountancy firm with some air quotes -

40:48-40:52

and the money is essentially ended up

40:50-40:53

at a guy who's heavily connected to the

40:52-40:54

original business. So, you’ve got to ask the

40:53-40:56

Question, why wouldn't they have just

40:54-40:58

transferred it direct? Doesn't make any

40:56-40:59

sense. So that's exactly what we're

40:58-41:01

seeing. We're also seeing, obviously, this

40:59-41:03

connection to the open sanctions one

41:01-41:06

that big red indicator connected

41:03-41:07

directly to Anthony Rose; so, that again,

41:06-41:11

is a really good indication that we've

41:07-41:12

got some risk associated with this.

41:11-41:14

Now I need to make a decision. I need to

41:12-41:16

take some kind of action and that's

41:14-41:18

where the suspicious activity reporting

41:16-41:20

comes in. Now for those of you in the

41:18-41:21

room who are financial crime

41:20-41:23

professionals, you'll know that filling

41:21-41:26

in SAR can be quite time consuming they

41:23-41:28

are massive forms with hundreds and

41:26-41:30

hundreds of fields. So, what Quantexa does is

41:28-41:32

fill in as much as it possibly can

41:30-41:34

automatically for you so all of the

41:32-41:36

different aspects of it around the

41:34-41:38

parties, who is linked to the case, all their

41:36-41:40

Details, all of the risks that are

41:38-41:42

associated with it, automatically filled

41:40-41:45

in, so what I can essentially do is go

41:42-41:48

through, build my narrative that we've

41:45-41:51

got here, generate my summary, and then

41:48-41:53

ultimately I can take this this SAR

41:51-41:55

and automatically submit it to the US

41:53-41:57

Regulator. Once I've completed that I can

41:55-42:01

then request a a review for from my

41:57-42:04

manager. So that takes the end to end

42:01-42:06

from alert right through to decision

42:04-42:08

underpinned by full workflow case

42:06-42:11

management and the advanced capabilities

42:08-42:13

that Quantexa brings around our

42:11-42:15

detection. So, to just quickly summarize

42:13-42:19

what I've shown you around Quantexa

42:15-42:22

Cloud AML, this demo that I showed you,

42:19-42:25

this network was in fact a real example

42:22-42:27

of a real life case of in fact what

42:25-42:30

turned out to be much more than money it

42:27-42:33

was in fact human and sex trafficking

42:30-42:34

and the criminals were using retail

42:33-42:37

Accounts, individual retail accounts, and

42:34-42:40

small business accounts at community and

42:37-42:43

midsize banks to launder those funds. So

42:40-42:46

with Quantexa Cloud AML we can detect

42:43-42:48

this complex risk we can serve it up and

42:46-42:49

essentially find things that traditional

42:48-42:53

systems would never have been able to

42:49-42:55

have found. At the same time, we

42:53-42:56

can also minimize false positives

42:55-42:58

because we obviously understand the

42:56-43:01

burden with which large numbers of

42:58-43:02

false positives will plague a team and

43:01-43:05

that makes teams more

43:02-43:07

efficient. It really is a game changer

43:05-43:09

for the community and the midsize banks

43:07-43:12

delivering enterprise decision

43:09-43:14

intelligence with embedded data sharing

43:12-43:17

in an end-to-end SaaS

43:14-43:19

solution. So that brings to the end

43:17-43:20

the technology showcase. If anyone

43:19-43:23

would like to see any more of this, we've

43:20-43:25

got demo booths downstairs, we've got

43:23-43:27

these demos but we've also got a host of

43:25-43:27

Others. So, thank you so much and hope you

43:27-43:34

enjoyed!

43:27-43:34

Applause

43:35-43:41

We are excited to announce Microsoft

43:39-43:43

and Quantexa have partnered to reduce the

43:41-43:46

complexity of industry-wide challenges

43:43-43:48

and enable scale through Azure. We're

43:46-43:51

building out a new app platform with

43:48-43:54

Fabric Workload development kit.

43:51-43:56

Introducing Quantexa Unify the AI

43:54-43:59

powered entity resolution workload for

43:56-44:01

Microsoft Fabric. Integrate with other

43:59-44:04

Microsoft products such as PowerBI and

44:01-44:04

interrogate your data with

44:07-44:10

44:11-44:16

co-pilot. Microsoft's vision for data and

44:14-44:18

AI is to empower every individual and

44:16-44:20

organization to achieve more.

44:18-44:22

Understanding data has always been the

44:20-44:26

key to unlock

44:22-44:30

value. Partnerships with the likes of Quantexa and

44:26-44:30

Microsoft are key to the future of the

44:31-44:37

bank. They are a strategic partner for

44:33-44:47

what we're doing in SaaS for midsize banks.

44:37-44:50

44:47-44:53

Please welcome back to the stage

44:50-44:55

Vishal Marria joined by Clare Barclay

44:53-44:58

President of Enterprise and Industry at

44:55-45:04

Microsoft.

44:58-45:04

Applause

45:05-45:12

So, 12 months ago, we announced our

45:09-45:15

strategic partnership with

45:12-45:19

Microsoft. 12 months down the road, what's

45:15-45:22

happened? We haven't just innovated. We

45:19-45:25

are disrupting technology. We are

45:22-45:27

disrupting processes right across the

45:25-45:30

enterprise and I'm delighted that today

45:27-45:31

Clare has taken time to come and talk

45:30-45:33

about some of this partnership. Well

45:31-45:35

Listen, it's

45:33-45:37

absolutely wonderful to be

45:35-45:40

here. It's great to see the innovation

45:37-45:45

and I was thinking, Vish, we probably

45:40-45:46

met about 18 months ago bizarrely it

45:45-45:49

was at number 10 that we met because we

45:46-45:52

were talking about how to advance AI for

45:49-45:54

the UK economy and so it's just

45:52-45:57

incredible to see the advancements that

45:54-45:58

we've made since then. I always knew

45:57-46:00

you had a kind of bee in your bonnet

45:58-46:03

around what the potential for this

46:00-46:04

partnership will be. The moment we met

46:03-46:07

you never let the people at

46:04-46:09

Microsoft off the hook. You were super

46:07-46:11

accountable and I think, you know, some

46:09-46:13

of the announcements that we've made

46:11-46:15

around the AML SaaS solution in

46:13-46:17

partnership with Microsoft, I think, is

46:15-46:20

a great example of that. But really this

46:17-46:21

is about unlocking value for the

46:20-46:23

partnership between Microsoft and

46:21-46:25

Quantexa and how that unlocks value for

46:23-46:26

all of our customers. So, it's just, I mean

46:25-46:28

just seeing some of those examples

46:26-46:30

you've shared, it's wicked and I would

46:28-46:32

say just congratulations to the whole

46:30-46:34

Quantexa team for the funding Series F

46:32-46:36

it's just super super impressive so

46:34-46:37

thank you. Thank you Clare and I'm going to

46:36-46:40

use ‘it's

46:37-46:41

Wicked'. We got that, right? ‘It's wicked.’

46:40-46:46

Now,

46:41-46:49

look, the partnership at the foundation

46:46-46:51

of trust, the foundation that we've put

46:49-46:53

together from our first engagement

46:51-46:57

has been fantastic and, you know, if I

46:53-46:59

look at the partnership on novobanco

46:57-47:02

but I'm going to just click slightly

46:59-47:04

to the left which is around Fabric

47:02-47:05

and I would love to hear your thoughts

47:04-47:07

about Fabric I would love to hear your

47:05-47:09

thoughts about how we partnered around

47:07-47:12

Fabric because from a Quantexa

47:09-47:14

perspective it was a mammoth amount of

47:12-47:17

work. A mammoth amount of work. And we

47:14-47:18

were the first to go on Fabric but I

47:17-47:20

would love to hear your thoughts. Yeah, I

47:18-47:22

mean listen firstly, thank you for the

47:20-47:24

commitment to the fabric platform and

47:22-47:26

I think we're seeing I mean both the

47:24-47:28

innovation that you have driven but if

47:26-47:30

we think about the message that we get

47:28-47:32

from so many customers when they think

47:30-47:34

about, you know, the importance of having

47:32-47:36

data and the like ubiquity of that data

47:34-47:38

in the platform and able to make sure that

47:36-47:40

it's transferred in the right way, it's

47:38-47:42

not wasted, and that you can unlock the

47:40-47:44

AI benefit on the back of that I think

47:42-47:47

the work that you've done is incredible

47:44-47:49

so that's unlocking real value, as

47:47-47:51

was demonstrated in the demos earlier on,

47:49-47:54

for customers. So, I would just say

47:51-47:56

congratulations the good news is

47:54-47:58

customers around the world are all very

47:56-48:00

interested in Fabric and what that

47:58-48:02

will do - particularly the data leaders

48:00-48:04

within organizations when they're

48:02-48:06

thinking about time and efficiency and

48:04-48:08

what that kind of innovation will unlock.

48:06-48:10

So, I think you made a wise investment if

48:08-48:11

I could just say that. Thank you. The

48:10-48:14

investors I think are in the room so that's

48:11-48:18

great to hear! And what I would

48:14-48:19

also add is when we started that

48:18-48:21

development work we were obviously

48:19-48:26

engaged with one of our other strategic

48:21-48:28

clients being HSBC and coming back to

48:26-48:29

the demo which Jamie always scares the

48:28-48:31

jeepers out of me when he does a live

48:29-48:33

demo I think everyone in this room knows

48:31-48:36

that but tremendous as always

48:33-48:38

performance by J, but if we look at

48:36-48:41

that partnership at Fabric but we take

48:38-48:43

that partnership with HSBC and the work

48:41-48:47

we did with HSBC around creating that

48:43-48:49

curated and trusted graph view

48:47-48:51

working with Microsoft Open AI I think

48:49-48:52

we weren't just challenging the norm

48:51-48:55

there, we were also working directly with

48:52-48:57

R&D on how do you put this to play. Absolutely.

48:55-48:59

Absolutely. And actually, from the first

48:57-49:01

time HSBC saw the opportunity with

48:59-49:03

Fabric, you know, they had a lot of

49:01-49:04

feedback and I think actually the

49:03-49:07

partnership with Quantexa to really

49:04-49:08

unlock how do you turn that into an

49:07-49:11

offering that really meets all the kind

49:08-49:14

of compliance requirements Etc of a

49:11-49:15

large really complicated bank like HSBC.

49:14-49:18

There's nothing like starting with the

49:15-49:19

more complicated one so I think the

49:18-49:22

three-way partnership there has been

49:19-49:24

amazing. We like difficult. We like

49:22-49:27

Difficult. And I'm just going to

49:24-49:29

do the last piece again a key part to

49:27-49:33

the partnership last year we launched

49:29-49:37

and announced today uh QCloud AML

49:33-49:40

and it's one product line AML and it's

49:37-49:43

the start. It's the start on how do you

49:40-49:46

get graph, how do you get curated trusted

49:43-49:49

data to every segment? And, you know,

49:46-49:50

Microsoft are, I'm going to say, ‘The

49:49-49:52

Godfather’ when it comes down to the

49:50-49:55

partner ecosystem and it's been a

49:52-49:56

fantastic journey to see how we've grown

49:55-49:58

with Microsoft and I think how

49:56-49:59

Microsoft's learned from Quantexa as well.

49:58-50:01

Anything you want to share on the

49:59-50:03

partnership with Cloud AML. Yeah, I mean, I

50:01-50:06

think, you know, like it's funny when

50:03-50:09

you use the word partnership it's always

50:06-50:11

very easy to say and hard to do and,

50:09-50:13

you know, if you get partnerships right

50:11-50:14

you it's not just one way partnerships

50:13-50:17

there's like ecosystems of Partnerships

50:14-50:19

that that unlocks. I think you've just

50:17-50:21

done an exceptionally good job in the

50:19-50:24

way that you have partnered with us, the

50:21-50:25

way you've taught us, but also in the

50:24-50:28

way that you have unlocked the

50:25-50:30

ecosystem to really focus on value and

50:28-50:32

delivery for our mutual clients. So, thank

50:30-50:35

you very much for that. And one big

50:32-50:39

mutual client, you know, we had a very

50:35-50:42

competitive bid with RSA and I

50:39-50:46

think it was 15 people, 15 vendors came

50:42-50:49

down to 10, down to three, down to two, and

50:46-50:52

obviously, our partnership in that

50:49-50:55

race was was unbelievable but there

50:52-50:57

was one individual from RSA who I think

50:55-50:59

has been a catalyst for some of this

50:57-51:00

work and at some of this partnership so

50:59-51:03

I just want to say a big thank you to

51:00-51:05

Adele she's here today from RSA on

51:03-51:07

some of the great partnership we've

51:05-51:09

done together in the past but also with

51:07-51:10

Microsoft. Yeah, and I think, actually, just

51:09-51:12

that I mean the collective work you know

51:10-51:14

in these things I talk about partnership

51:12-51:16

you have to unlock it and I think, you

51:14-51:18

know, as was demoed earlier on, you know,

51:16-51:20

combating fraud and financial crime is

51:18-51:22

like one of the biggest challenges at

51:20-51:24

the industry faces and I think some

51:22-51:26

of the visionary leadership that Adele

51:24-51:27

and the team have driven is really about

51:26-51:30

how we can unlock that with some of the

51:27-51:32

innovation that we collectively bring

51:30-51:34

It was unlocked uh through

51:32-51:36

Marketplace so that's also innovation

51:34-51:38

that enables some of those solutions

51:36-51:42

to bring to life and really for me this

51:38-51:44

is about how we think about speed,

51:42-51:46

scale, and innovation in order to tackle

51:44-51:48

that. I love the demo earlier on with the

51:46-51:50

network and actually when you think

51:48-51:52

about the impact that that has down on

51:50-51:55

the front line with some of the

51:52-51:57

additional, you know, sex crime and

51:55-51:59

other bits and pieces that were resolved

51:57-52:02

as part of that it just shows you the

51:59-52:03

power of how you unlock data and AI to

52:02-52:05

tackle one of these things so I would

52:03-52:06

just say you know thank you again for

52:05-52:09

the partnership, thank you to the

52:06-52:10

partnership with RSA and I think

52:09-52:13

it's really exciting and I would just

52:10-52:15

say, Vish, maybe using your words you talk

52:13-52:17

about there's a lot done but lots to do

52:15-52:19

or whatever the words were you used

52:17-52:20

earlier on. I would say listen, you

52:19-52:23

stood on the stage a year ago and talked

52:20-52:25

about the potential for the partnership.

52:23-52:28

We have as much ambition collectively

52:25-52:30

for our customers with Quantexa

52:28-52:33

and we're here to go so thank you so

52:30-52:36

much. No, it's been an absolute

52:33-52:38

privilege working with yourself

52:36-52:41

and the wider team at Microsoft. I

52:38-52:44

remember when we originally met at

52:41-52:46

number 10, I also had a lovely lunch with

52:44-52:49

Satya which was fantastic and truly

52:46-52:51

humbling to meet him and again the

52:49-52:54

vision when it came down to connected

52:51-52:56

data to empower AI was obviously a very

52:54-52:57

critical part to the conversation. And

52:56-52:58

he was super inspired after the

52:57-53:02

conversation as well and maybe I'll do

52:58-53:04

one final plug for Quantexa as well as

53:02-53:05

my job at Microsoft I'm also the

53:04-53:08

chair for the industrial strategy

53:05-53:10

Council for the UK government and so

53:08-53:12

it's examples like Qunatexa that is really

53:10-53:15

about how we're driving growth and

53:12-53:16

opportunity for the UK economy and I

53:15-53:18

couldn't be prouder of the work that

53:16-53:22

they've done. So, thank you so much. That's

53:18-53:22

very kind, thank you.

53:26-53:31

So, again, thank you for those kind words,

53:29-53:34

Clare, and can we have a big round of

53:31-53:34

applause for Clare. Thank

53:37-53:46

you. So, I mentioned in my opening ‘the

53:42-53:50

ecosystem'. The ecosystem that we have

53:46-53:52

collectively invested in. The ecosystem

53:50-53:55

that we work closely to deliver the best

53:52-53:59

services capabilities the full end to

53:55-54:03

end and tonight again wouldn't be what

53:59-54:05

it is without some of our very strategic

54:03-54:08

partnership, partners. We actually had

54:05-54:11

our PAB, our partner Advisory Board,

54:08-54:13

earlier today. It was, again, truly

54:11-54:15

humbling spending time with many of our

54:13-54:17

strategic partners talking about the

54:15-54:19

innovation we have done and doing

54:17-54:22

together with many of our partners but

54:19-54:24

also, what's tomorrow? What are we going

54:22-54:26

to do tomorrow? So, again, I just want to

54:24-54:28

do a big round of applause to our

54:26-54:29

strategic partners who are all up on the

54:28-54:31

screen. I'm not going to mention the

54:29-54:34

names but a huge round of applause to

54:31-54:34

our

54:38-54:42

partners.

54:39-54:44

Now, last year we kicked off, or last year

54:42-54:46

was the second year of our partner

54:44-54:50

awards.

54:46-54:52

Now, weirdly, there's a lot of competition

54:50-54:54

when it comes down to partner awards. I,

54:52-54:56

actually, had a lot of them asking me,

54:54-54:58

‘who's won the partner award?’ Yet as

54:56-55:02

absolutely I would say, ‘I have no

54:58-55:05

privilege at that time to comment.’

55:02-55:07

But, this year, because of the great work

55:05-55:11

we have done with our partners, we’ve

55:07-55:12

actually got two partner awards. Now that

55:11-55:14

that's not a cop out saying we're not

55:12-55:17

going to make a decision. Right. It's

55:14-55:19

actually from the success we have seen

55:17-55:21

on the two sides of the coin. We have

55:19-55:24

seen areas around

55:21-55:28

innovation and we' have seen areas

55:24-55:32

around growth. And look, growth is not a

55:28-55:35

bad thing. Growth is what stimulates

55:32-55:37

the innovation; innovation stimulates

55:35-55:40

back the growth. So, there's two key

55:37-55:45

components here. We've got the innovation

55:40-55:48

award and we have the growth award. So, I

55:45-55:53

know the partners are eagerly wanting to

55:48-55:56

know who the winners are. So, the first

55:53-55:59

one, ‘Innovation Partner of the Year’.

55:56-56:01

So, it's actually one of our very very

55:59-56:04

early day partners who, actually, I was

56:01-56:06

talking to the MD today on one of the

56:04-56:09

first engagements we had with the

56:06-56:12

partner back in 2016 when we started Q.

56:09-56:13

And this achievement goes to the partner

56:12-56:15

because of some of the great work we've

56:13-56:18

done around customer intelligence which

56:15-56:21

is more around the innovation for the

56:18-56:24

RMs - the digitalization of the front

56:21-56:26

office. There's been a lot of Forward

56:24-56:29

thinking when it comes down to that

56:26-56:32

innovation. How do you get the data

56:29-56:35

talking to the RM? How do you get better

56:32-56:38

qualified leads in natural language back

56:35-56:42

to the RM? And we have seen substantial

56:38-56:46

results. Hundreds of millions of dollars

56:42-56:48

of net new revenue booked based upon the

56:46-56:50

combined lead generation set of

56:48-56:53

algorithms we've done across the

56:50-56:56

entities and graphs. So, it's with huge

56:53-56:58

delight, it's with huge pleasure

56:56-57:02

that I would like to give Innovation

56:58-57:05

Partner of the Year Award 2025 to

57:02-57:07

Accenture! A big round of applause to our

57:05-57:07

friends at

57:10-57:16

Accenture. I was looking for

57:13-57:17

you. Thank you. We do have an award we,

57:16-57:18

actually, do have an award but it's

57:17-57:22

behind the scenes but I'll give you.

57:18-57:25

Thank you. Trust me trust me it's over

57:22-57:25

there.

57:26-57:30

There. Thank you for telling

57:32-57:39

me. At least I know where the other award

57:35-57:41

is so thank you. So, the second award,

57:39-57:44

which again I mentioned about growth - so

57:41-57:48

that was innovation - now about

57:44-57:50

growth. Growth. It's a hot topic. The Mayor

57:48-57:53

of London mentioned earlier today

57:50-57:57

about growth: it's critical, it drives

57:53-58:00

innovation. And as we all know,

57:57-58:03

the ability to take data and attach it

58:00-58:06

to the AI algorithms is going to

58:03-58:08

transform the way people make decision.s

58:06-58:11

So, in this particular,

58:08-58:13

one the partner who has won the growth

58:11-58:15

award has not just been simply

58:13-58:18

innovating when it comes down to with

58:15-58:21

their clients and growing the

58:18-58:25

Quantexa stack but they're also building

58:21-58:27

capability sitting within the Quantexa

58:25-58:29

stack. If that's around financial

58:27-58:31

services, if that's around

58:29-58:34

telecommunication, or if that's around

58:31-58:38

the government and public sectors. So,

58:34-58:41

once again, this gives me huge

58:38-58:44

pleasure to announce that the Growth

58:41-58:47

Partner of the Year is

58:44-58:54

KPMG. And is Karim here?

58:47-58:58

Applause

58:54-59:01

Yes. Thank you. Congrats. Thank

58:58-59:03

You.

59:01-59:08

So, night's not

59:03-59:11

over. We are coming to about

59:08-59:14

halfway. What you've seen tonight is the

59:11-59:16

power of data with

59:14-59:18

AI. What you've seen tonight is

59:16-59:20

excellence when it comes down to

59:18-59:23

execution and

59:20-59:26

transformation. Resilience has been a key

59:23-59:29

part to the to the DNA of Q. Resilience.

59:26-59:32

We had the acronym of our culture: DATA.

59:29-59:35

Determination. Accountability. Teamwork.

59:32-59:40

And Ambition. It's a key part to who we

59:35-59:42

are at Q. And so, without further ado,

59:40-59:46

we're now going to have a set of

59:42-59:48

sessions where, once again, we're going to

59:46-59:51

talk about what we have done, we've

59:48-59:54

talked about it, but what are we going to

59:51-59:58

do? A lot of innovation has

59:54-00:02

happened but we are not

59:58-00:06

done. As I said earlier, we have just

00:02-00:08

started. The revolution is happening and

00:06-00:09

it's happening in this

00:08-00:12

ecosystem.

00:09-00:15

So, I'm delighted to

00:12-00:20

announce

00:15-00:26

QLabs. QLabs will change the way innovation

00:20-00:31

happens. It's a safe area to allow us,

00:26-00:35

our clients, and our partners to

00:31-00:35

innovate. Play the

00:36-00:41

video. We want to create an environment

00:39-00:43

physical and virtual, for our customers

00:41-00:45

and our partners to get fast track open

00:43-00:48

access to the innovations that we're

00:45-00:50

working on at Quantexa. We realize that

00:48-00:52

it's a great opportunity for us to give

00:50-00:55

an identity to innovation, which is how

00:52-00:57

Qlabs was born. Innovation Labs gives us a

00:55-00:58

real opportunity to embrace change. I'm

00:57-00:59

really excited about it not just

00:58-01:01

giving us a framework for

00:59-01:03

experimentation but also a framework for

01:01-01:05

us being able to adopt it into the

01:03-01:07

product. Going from rapid prototyping to

01:05-01:10

validation and launching a successful

01:07-01:12

product in the market. AI is a once- in a

01:10-01:14

generation technology that's creating

01:12-01:16

infinite potential and opportunities for

01:14-01:18

innovation. When there's so many new

01:16-01:20

technologies and new interests it's a

01:18-01:22

great way to have a look at them and be

01:20-01:24

like is this something Quantexa should be

01:22-01:26

doing? Is this something that would give

01:24-01:27

value to our customers and partners?

01:26-01:29

Some of the new projects that we're

01:27-01:32

working on are things like simulation

01:29-01:34

lab where we allow our customers to

01:32-01:37

generate and explore potentially

01:34-01:39

millions of scenarios through sort of

01:37-01:41

cause and effect relationships and using

01:39-01:42

things like Monte Carlo tree search to

01:41-01:44

automatically explore a large number of

01:42-01:47

scenarios which ultimately allows our

01:44-01:49

customers to identify emerging risks and

01:47-01:51

opportunities and plan for them

01:49-01:53

better. One of the, kind of, problem areas

01:51-01:54

that we've been talking to our

01:53-01:57

customers about is supply chain

01:54-02:00

monitoring. As you know, supply chains

01:57-02:02

are obviously incredibly complex

02:00-02:04

processes and you've got multiple tiers

02:02-02:06

of suppliers and something that's

02:04-02:08

never been able to be completely

02:06-02:11

visualized which is monitoring your

02:08-02:13

supply chain using news intelligence. So,

02:11-02:15

you can monitor multiple layers of your

02:13-02:17

supply chain and see how that would have

02:15-02:19

a knock-on effect with the rest of your

02:17-02:21

supply chain. A key part of innovation is

02:19-02:23

curiosity, right? Asking questions,

02:21-02:25

looking at things in a different light,

02:23-02:27

and finding ways to make things better.

02:25-02:29

So, it's really also a place for us to be

02:27-02:31

curious together with our customers as

02:29-02:33

well as internally. We're really pushing

02:31-02:35

the boundaries of what's possible with

02:33-02:38

data analytics and AI. We're working hand

02:35-02:40

in hand with our R&D and GTM teams to

02:38-02:42

turn breakthrough ideas into real world

02:40-02:43

solutions. We're pushing the boundaries

02:42-02:46

of what's possible with decision

02:43-02:49

Intelligence.

02:46-02:49

Music