DFIN + eBrevia: A case study of automation in data extraction and contract analytics

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The pace of U.S. M&A activity in artificial intelligence & machine learning (AI & ML) ramped up to record levels last year, soaring at a near linear rate since 2016 to 145 completed deals while value eclipsed the prior high of $8.5 billion to close on $21.3 billion. As recently as 2015, just 27 transactions closed on $2 billion in aggregate. The steep uptick in both value and volume follows several years of increased venture investment into the space.
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AI ML AI MACHINE Learn company eBrevia deal DFIN contract data
https://techcelerate.ventures
About Techcelerate Ventures

Tech Investment and Growth Advisory for Series A in the UK, operating in £150k to £5m investment market, working with #SaaS #FinTech #HealthTech #MarketPlaces and #PropTech companies.
AI ML AI MACHINE Learn company eBrevia deal DFIN contract data
AI & MACHINE LEARNING LANDSCAPE 
DFIN + eBrevia: A case study of automation in 
data extraction and contract analytics
Data provided by
DFINsolutions.com
2   AI & MACHINE LEARNING L ANDSC APE
Executive Summary
Contents
Executive Summary
2
Industry 
Developments
3
Q&A
4-5
AI & ML 
Landscape
6-7
Dealmaking 
Landscape
8-10
About Us
11
"DFIN had a lot of 
foresight and now 
has taken a head 
start in this area."
N ED G A N N ON 
PR E S ID E NT,  E B R E V I A
The pace of U.S. M&A activity in artificial intelligence & machine learning (AI & 
ML) ramped up to record levels last year, soaring at a near linear rate since 2016 
to 145 completed deals while value eclipsed the prior high of $8.5 billion to close 
on $21.3 billion. As recently as 2015, just 27 transactions closed on $2 billion in 
aggregate. The steep uptick in both value and volume follows several years of 
increased venture investment into the space.
Propelled by the need to stay on top of technological innovation, buying effective 
startups over building bespoke internal systems has quickly emerged as a 
preferred approach for sponsors and strategics alike looking to add AI- & ML- 
enabled capabilities to their platforms. Since the start of 2008, half of all acquired 
AI & ML companies in the U.S. had VC backing. With few exceptions, the others 
had raised no institutional capital prior to purchase.
Dealmakers expect improved automation to drive down the time taken to conduct 
due diligence while improving accuracy and controlling costs. As M&A strategies 
shift in scope to capture emerging tech verticals, contract analytics could quickly 
become indispensable to the review process for buyers that need to add new 
capacities outside of their core competencies. That dynamic has the global 
market for AI & ML poised to grow by 150% this year alone. Analysts estimate the 
market will reach $191 billion in value by 2025 driven primarily by solutions for 
the enterprise.
AI & MACHINE LEARNING L ANDSC APE   3 
Industry Developments
37% CAGR
The global AI & ML market 
is expected to expand at a 
compound annual rate of 36.6% 
from $21.5 billion to $191 billion 
between 2018 and 2025. The use 
of AI & ML for the enterprise 
will fuel this growth, with 
manufacturing poised to lead 
the way as the Industrial IoT 
comes online replete with the 
cybersecurity challenges it poses.
Cybersecurity
A growing area of application for 
AI & ML is cybersecurity defense. 
Firms can continually monitor 
a wider range of potentially 
malicious behaviors and close 
the gap between detection and 
response with intelligent threat 
detection enabled by automation 
and deep learning. 
Big Tech
The most active strategic 
acquirers in the AI & ML space 
represent the biggest of Big Tech. 
Companies like Alphabet, Amazon 
and Apple regularly purchase 
promising startups, frequently 
before they have raised any 
institutional capital.
Tasks, not jobs
While some peg job replacement 
figures at 40% from automation, 
researchers from MIT's Sloan 
School of Management anticipate 
that AI & ML will more likely 
eliminate a subset of tasks from 
among the 20 to 32 that comprise 
the 964 types of job the U.S. Labor 
Department tracks.
$2B
The field of legal analytics is 
projected to grow at a compound 
annual rate of 32.7% from $451 
million to $1.9 billion between 
2017 and 2022. Although North 
America appears poised to 
lead that expansion, Europe is 
anticipated to grow at a slightly 
steeper clip. 
$437B
Legal services in the U.S. 
represents a $437 billion 
market. With junior associates 
charging anywhere from 
$300 to $500 per hour to 
run contract reviews during 
due diligence, the legal fees 
in M&A pile up quickly and 
annually top $1.5 billion.
4   AI & MACHINE LEARNING L ANDSC APE
Q&A
Ned Gannon
President, eBrevia
As a co-founder and President of eBrevia, Ned Gannon drives the strategic direction of the company and is 
responsible for overall management. eBrevia uses machine learning technology to improve the efficiency and ac-
curacy of contract review. Ned brings a broad range of legal and business experience to the company. While prac-
ticing corporate law with Paul, Hastings, Janofsky and Walker LLP and LeBoeuf, Lamb, Greene & MacRae LLP (n/k/a 
Dewey & LeBoeuf LLP), Ned represented private equity funds, strategic investors, venture capital funds and startup 
companies in mergers, acquisitions, financings and general commercial matters. Prior to practicing corporate law, 
Ned worked in a sales and business development role at Survey Sampling, Inc., a privately-held data collection 
company. Ned was selected by Capterra as one of five Legal Tech entrepreneurs to watch and speaks frequently 
on machine learning technology's impact on the legal industry. Ned holds a Juris Doctor from Harvard Law School, 
a Master in Public Administration from Harvard's Kennedy School of Government and a Bachelor of Science from 
Boston College.
The combination of eBrevia and DFIN 
emerged from an existing relationship 
between the two companies. How did 
that initial partnership take shape?
Gannon: It really came from both of us 
selling to global law firm customers 
simultaneously and encountering each 
other in that space specifically. DFIN 
was selling a new virtual data room 
and eBrevia was selling our contract 
analyzer software that focuses on 
accelerating the due diligence process. 
Venue is used very heavily in M&A, so 
there were a lot of natural synergies 
there given a common element in 
our customer base, which has since 
evolved and diversified quite a bit. In 
addition to global law firms, we also 
work frequently with three of the big 
four consulting firms, corporate legal 
departments, financial institutions, 
legal process outsourcers, and 
commercial real estate firms. DFIN also 
has its own longstanding relationships 
with many of these client types.
How did DFIN's role as an early 
investor factor in as the relationship 
developed over time and as the 
partnership grew from those initial, 
natural synergies?
Gannon: After we signed a partnership 
sometime in the summer of 2015, we 
got a chance to work closely with the 
DFIN salesforce. As the relationship 
continued to evolve, we reached a 
point where we were going to be 
raising another round of financing 
and DFIN expressed interest in 
participating. In the end, they led 
the round in September 2016, which 
further cemented the connections 
between the companies. From there, 
we did some additional training of 
their salesforce and started to focus 
more on the integration of some of 
the existing productssomething that 
we're continuing to focus on today.
How has DFIN folded eBrevia into 
Venue? Is it more of a discreet 
component of an overall process? 
How do you view the offering from a 
product suite stand point?
Gannon: Our product will continue to 
be sold on standalone basis, as it's 
also an enterprise tool that doesn't 
necessarily have to be used within 
Venue. For a lot of consulting firms, law 
firms, and corporate legal departments, 
eBrevia makes sense on an enterprise 
basis while on a transactional basis the 
contract analytics tool complements 
Venue's value proposition. In addition 
to pulling data out of contracts with 
what we call pretrained provisions, 
nontechnical users can also train the 
system themselves to extract custom 
information to meet their specific 
needs. For instance, a nontechnical 
user at an energy company tasked with 
extracting data from power purchase 
agreements can train our analytics tool 
to do it for them. As a result, eBrevia 
is a really nice fit for the enterprise. 
People can tailor it to meet their 
specific needs. But another part of 
that strategy is to help clients access 
contracts and company data wherever 
it might reside, whether that's in a 
contract management system or, on a 
transactional basis, perhaps the buyer 
or seller have selected a data room 
other than Venue, we still want to be 
able to access that data. As a result, we 
have worked with DFIN on integrations 
with contract management systems 
and other virtual data rooms as well.
When you've come back to existing 
customers, how have you narrated 
the combination with DFIN to them? 
What questions have they asked? Is 
there a lot of nuance to the offering or 
their understanding or is there a lot of 
teaching and explaining that goes on?
Gannon: Most understand that the 
software can help to analyze contracts 
more accurately and efficiently during 
diligence, and there's no software 
out there that can also store the 
documents within the context of 
a deal. Combining those functions 
together in Venue creates a lot of 
value for customers. Meanwhile, we 
haven't found that we need to do a 
tremendous amount of explaining of 
how the products work in tandem. 
People are already comfortable with 
contract analytics. They see it, they've 
AI & MACHINE LEARNING L ANDSC APE   5 
heard about it, many have used it. The 
same holds true of virtual data rooms 
like Venue. We've reached a point 
in the development of this market 
where, in the grand scheme of things, 
although it's still very early days yet 
in terms of contract analytics and the 
value that they can create, as products 
like ours evolve, that will only increase.
Our customers have done a lot of 
bespoke self-training, helping them 
to create a lot of upsides from our 
combination with DFIN for themselves. 
We have very close relationships with 
many of them and, in fact, some of 
our best features have come directly 
from client suggestions. In the past, 
people might have requested we build 
a particular function to help with 
their workflow and it may have taken 
us awhile to allocate the engineering 
resources. Now, we're able to move 
a lot faster as part of a much larger 
entity with a lot of levers to pull and 
that allows us to focus more on the fun 
stuff for clients.
In addition to greater scale, what 
are the most compelling drivers of 
dealmaking in this space?
Gannon: When we first started 
eBrevia, we partnered with Columbia 
University's data sciences institute 
back in 2012 and spun the company 
out of the university. Back then, AI & 
ML weren't very hot topics. In fact, we 
had some early angel investors and 
VCs that we pitched that tended to 
shy away from us really because they 
weren't convinced that AI could be 
sophisticated enough to accurately 
analyze contracts. Obviously, times 
have very much changed. A lot of that 
is driven by the enthusiasm of large 
corporates for adopting this type of 
technology, as they've really witnessed 
firsthand the benefits for their own 
work processes. As a result, that's 
sparked a lot of interest from acquirers, 
whether it's strategic acquirers looking 
to complement their own products 
or private equity funds. My general 
sense is, though, that we're still in the 
early days of AI-related acquisitions. 
DFIN had a lot of foresight and now 
has taken a head start in this area so 
it can focus on deploying this type 
of technology throughout its product 
suite, and that'll be a gradual process 
over time. Meanwhile, other folks who 
haven't been quite as quick to move 
are still looking at the market or are 
trying to build something themselves. 
That could place them a bit further 
behind because building in this space 
is also uniquely challenging.
How are you finding industry experts 
classifying the various categories of 
AI and machine learning relative to 
analytics software overall? 
Gannon: At this point in time, the 
most successful AI & ML offerings are 
the ones that complement human 
expertise. That's very much how 
eBrevia has been designed, namely, 
to assist the attorney in their job 
more accurately and efficiently. That's 
also what folks should look at in an 
acquisition. When you start talking 
about companies that are saying, "Hey, 
invest in us because we're going to 
totally eliminate the need for, X, Y, Z," I 
think that particularly with professional 
positions, given the kinds of knowledge 
bases required for them, that's 
sometimes a bit overblown. There are 
certain tasks that can obviously be 
automated. And that's really where I 
think the most successful AI companies 
play and leave the higher-level creative 
tasks to the humans.
So, you feel like there's still a solid 
divide between what the human and 
what the machine can provide?
Gannon: I think you'll continue to see 
the machines moving up the value 
chain. But for the foreseeable future 
there's always going to be a very 
significant need for the human to be 
engaged in the higher-level tasks and 
higher-level analysis.
6   AI & MACHINE LEARNING L ANDSC APE
AI & Machine Learning Landscape
The current expansion 
of M&A activity in U.S. 
AI & ML has targeted 
startups that scaled 
quickly following 
several years of 
increased venture 
investment into the 
space.
$5.7$3.5$7.4$8.5$7.4$21.316
13
13
13
29
27
49
96
146
2010
2011
2012
2013
2014
2015
2016
2017
2018
Deal value ($B)
Deal count
Acquirer Name
# of Acquisitions
Alphabet
8
Apple
7
Intel
6
Microsoft
5
Nuance
4
Amazon
4
Yahoo
4
Oracle
3
PTC
3
Salesforce
3
Facebook
3
Accenture
3
The dealmaking activity of tech giants like 
Alphabet, Amazon and Apple illustrates 
the central role played by M&A to maintain 
a competitive edge in AI & ML.
Source: PitchBook
Top strategic acquirers of VC-backed AI 
& ML companies, 2010-2018
U.S. M&A activty in AI & ML
19
combined deals for Alphabet, 
Amazon, Apple
Investor Name
# of Investments
Vista Equity Partners
5
Insight Venture 
Partners
3
The Blackstone Group
2
Lead Edge Capital
2
GCP Capital Partners
2
Warburg Pincus
2
Most active PE investors in AI & ML companies, 2010-2018
AI & MACHINE LEARNING L ANDSC APE   7 
U.S. M&A activty (#) in AI & ML by target company backing status
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011 2012 2013 2014 2015 2016 2017 2018
Acquisitions
Buyouts
0
20
40
60
80
100
120
140
160
2010
2011
2012
2013
2014
2015
2016
2017
2018
No backing
Publicly held
PE-backed
VC-backed
Source: PitchBook
Private equity interest 
has crept up considerably 
over the past decade, 
with sponsored deals 
representing 17.5% of all 
transactions closed since 
the start of 2008.
U.S. M&A activity (#) in AI & ML by deal type
8   AI & MACHINE LEARNING L ANDSC APE
Dealmaking Landscape
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011
2012 2013 2014 2015 2016 2017
2018
5B+
1B-5B
500M-1B
250M-500M
100M-250M
Under 100M
Source: PitchBook
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2011
2012 2013 2014 2015 2016 2017 2018
$5B+
$1B-$5B
$500M-$1B
$250M-$500M
$100M-$250M
Under $100M
With headline grabbing achievements 
to its credit, AI & ML have captured the 
public's imagination. But dealmakers 
have largely looked beyond the hype 
generated by Watson's "Jeopardy!" 
performance, focusing instead on 
enterprise applications to fuel forms 
of automation that can cut costs 
and free employees to concentrate 
on more advanced projects. "There 
are certain tasks that can obviously 
be automated. And that's really 
where I think the most successful AI 
companies play and leave the higher-
level creative tasks to the humans," 
says Ned Gannon, Co-Founder & 
President of eBrevia.
Keeping pace with these 
developments drove M&A activity in AI 
& ML to record levels in the U.S. last 
year, with 145 deals closing on $21.3 
billion in value. Just 27 transactions 
closed on $2 billion in aggregate only 
three years earlier. But the types 
of automation ushered in by AI & 
ML can range from IT operations 
to self-driving vehicles, with single 
transactions capable of keeping 
aggregate values up even if the 
volume of disclosed deal sizes levels 
off or drops. And it's not just strategics 
driving M&A in AI & ML.
Last year, KKR completed its largest 
acquisition since the global financial 
crisis with the purchase of BMC 
Software for $8.5 billion. The deal 
accounted for $6.9 billion of the $7.4 
billion in disclosed value that year, 
which only registered 13 completed 
transactions in AI & ML. It also 
highlights the potential for value 
creation that PE's buy-and-build 
strategy can realize in this space.
U.S. M&A activity (#) in AI & ML by deal size
U.S. M&A activity ($M) in AI & ML by deal size
Source: PitchBook
AI & MACHINE LEARNING L ANDSC APE   9 
Founded: 2011 
Founders: Ned Gannon, President, Adam 
Nguyen, SVP, and Jake Mundt, CTO 
Venture capital raised: $4 million 
Acquisition date: December 27, 2018 
Deal size: $23 million 
History: DFIN's purchase of eBrevia at 
the end of 2018 not only capped the 
busiest year on record for U.S. M&A 
activity across the AI & ML space, it 
also cemented a partnership that 
first emerged from the pair selling 
their complementary products to 
global law firms. Two of eBrevia's 
founders, Ned Gannon and Adam 
Nguyen, graduated from Harvard Law 
School and practiced commercial 
law prior to launching the analytics 
platform. eBrevia's third founder, 
Jake Mundt, received his master's 
degree in Computer Science from 
Columbia University and previously 
worked for a company extracting 
data in the medical industry. In 2014, 
they secured a $1.5 million round 
that brought eBrevia's total seed 
funding to $2.1 million raised from 
investors including Connecticut 
Innovations and Rothenberg Ventures. 
DFIN + eBrevia Case Study
"For the foreseeable future, there's always going to be a very 
significant need for the human to be engaged in the higher-level 
tasks and higher-level analysis." 
N ED G A N N ON , PR E S I DEN T,  EB R E V I A
After entering a formal partnership 
in 2015, DFIN's salesforce started to 
train on eBrevia's analytics platform. 
In 2016, DFIN led eBrevia's Series A 
round, which brought in another $2 
million in funding. It also signaled a 
deepening of the pair's partnership, 
with eBrevia's AI & ML tools becoming 
more fully integrated with Venue. 
The companies have been working 
very closely together since 2015 and 
that really goes to multiple levels of 
the organization. The global capital 
markets salesforce at DFIN has been 
trained on the product. The Venue 
salesforce has been demoing eBrevia 
for at least 18 months now. In a lot of 
ways, we have always felt like part of 
the broader organization. 
Ned Gannon, President of eBrevia
Product: Gannon, Nguyen and Mundt 
created eBrevia based on AI & ML 
licensed from Columbia University, 
spinning the company out of the 
university in 2012. The analytics 
platform leverages NLP technology 
to extract data from contracts. The 
company's software identifies and 
extracts legal provisions and other 
data from legal documents regardless 
of the vocabulary used or where the 
information might be buried within the 
document. In addition to pre-trained 
concepts, users can also teach the 
system to read and extract any number 
of custom categories of information 
from contracts to meet their specific 
needs. Integration with Venue allows 
eBrevia's Contract Analyzer to operate 
within DFIN's virtual data room though 
the software also continues to be sold 
on a standalone basis. Parties on the 
buy side of a deal, for instance, can use 
eBrevia in Venue to identify and extract 
all the change of control provisions 
or assignment provisions in the target 
company's contracts to prepare the 
due diligence memo. In addition to 
being used on the buy and sell side 
of transactions, eBrevia's software is 
also leveraged for compliance, audit/
consulting work, lease abstraction and 
to enable corporations to identify and 
track risks and obligations within their 
company contracts.
10   AI & MACHINE LEARNING L ANDSC APE
Since 2Q 2016, no quarter for U.S. M&A 
in AI & ML activity has fallen below 
$1 billion in value. But the recent 
YoY uptick in overall deal activity 
really got its start in 3Q of that year, 
which accounted for 10 completed 
transactions representing $1.4 billion 
in value.
With strategics under pressure to stay 
abreast of innovation, the alternatives 
to M&A in order to add AI- & ML- 
enabled capabilities can represent 
a losing proposition, as building a 
bespoke system could comprise a 
multiyear commitment to an uncertain 
process for companies that risks 
putting them behind acquisitive 
competitors. The top dealmakers in 
this space highlight the essential part 
played by M&A to keep pace with 
innovation and maintain a leading 
position in the market.
The relative lack of talent in AI & 
ML engineering can make this a 
8
6
4
11
8
7
7
5
9
15
10
15
16
24
26
30
30
42
32
42
0
5
10
15
20
25
30
35
40
45
$0
$2
$4
$6
$8
$10
$12
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
2014
2015
2016
2017
2018
Deal value ($B)
Deal count
Source: PitchBook
U.S. median and average deal size ($M) in AI & ML
challenge, however. "The acqui-hire 
piece is a big part of it. Who is the 
team of the company you're looking 
to acquire? Would they fit within your 
company's culture? Could their skillset 
be leveraged in other areas within 
the organization as well as to further 
product integration?" says Gannon.
As a consequence of acquisition 
targets frequently comprising small 
teams of highly specialized engineers, 
though, deal sizes have generally 
proliferated at the lower end of the 
range. For years with more robust 
deal value data disclosed, median 
transaction sizes ranged between $43 
million last year and $50 million in 
2017 on 31 and 22 deals, respectively.
The precipitous rise of U.S. M&A 
activity in AI & ML is still striking; 
however, given the relative immaturity 
of the AI & ML ecosystem alongside 
continued investor interest and 
strategic need, deal data over the past 
decade could well pale in comparison 
to future figures. The volatility of 
transaction sizes to date also suggests 
that valuation models and the process 
of pricing AI & ML assets may remain 
a challenge for some time to come. 
"I would say you'd want to look at 
companies that have an AI-based 
product that complements an existing 
product or service you have," Gannon 
says.
$50M
median deal size in 2017
AI & MACHINE LEARNING L ANDSC APE   11 
About Us
Donnelley Financial Solutions (DFIN) 
DFIN is a leading global risk and compliance solutions company. We provide domain expertise, enterprise software and data 
analytics for every stage of our clients' business and investment lifecycles. Markets fluctuate, regulations evolve, technology 
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Learn about DFIN's end-to-end risk and compliance solutions 
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