How Venture Capitalists Use Artificial Intelligence To Better Source Deals And Assess Startups

This article is an overview of the latest developments in AI for venture capital and the emerging ecosystem of solution providers (as of Q2 2018).


Inspired by the recent Medium post by Francesco Corea on “Artificial intelligent and Venture capital”, we did not want to miss on the opportunity to throw our two cents and  discuss the status quo of artificial intelligence in the VC industry. It was only few years ago that a fistful of VCs started to experiment with automation and machine learning as part of their internal operations. Today, you see investing firms publishing offers for data science jobs and openly talking about the different ways they use machine learning. But because the VC industry  is dealing with way less quality data than other types of investors, data-driven approaches are hard to implement. Even if the industry as a whole is still lagging behind, recent announcements from big funds pursuing AI-related initiatives are sparking the interest of more traditional startup investors. In addition to internal projects from VC firms, providers of AI solutions for VCs also start to emerge and offer a quicker, more robust path for investors to adopt machine learning.

But how do VCs benefit from artificial intelligence? Let’s look at it from 3 different angles: by player, by area of application and by data type.

1. Perspective by player


Please, note that the number of firms currently employing these applications may be significantly higher. This article is only based only on publicly available information. This is an issue specially relevant for the largest players which are more hermetic when it comes unveiling their dealsourcing and assessment approaches. For instance, GV and Sequoia, have sporadically disclosed to employ data scientists, but never explained in what their efforts translate.

Even considering these constraints, the total invested volume by the reported funds amounted to $9.0Bn by 2017 and to $5.3Bn by 2018 YTD. Their volume of investments added up to around 1,200 by 2017, with an average check size of $7.4M that shrinks to $2M after excluding the 5 largest players.

2. Perspective by area of application.



Researchers and VCs have long been aware of the many biases influencing their decisions. One of the most prominent being the perpetuation bias by which the applicants that most closely resemble the treats of the idealized entrepreneur are more likely to get funded. The extensive work of Laura Huang proves several examples of these instances. (e.g. “Investors Prefer Entrepreneurial Ventures Pitched by Attractive Men.”). Can data-driven approaches mitigate the difficulty for “humans” to create meaning out of large sets of unstructured information?

Due to the relative scarcity of start-up data and its heterogeneity, most current solutions focus on predicting succesful events. For instance, Hone Capital defines success as the ability for a start-up to raises a Series A round and attempt to predict the likelihood of such scenarios. Following this methodology, they claim to be able to identify companies raising Series A with an accuracy of 40%. This represents x2.5 the industry average, which soars to x3.5 when the results of the model are also filtered by the investment team.

The trade-off always remains data quality vs. data quantity. For example, widely available data in public databases, such as Crunchbase, Pitchbook, Owler, or Dealroom, are in the quantity game. Information is abundant but rarely go into details when dealing with small companies. Data is scarce, sometimes vague and often outdated. It makes for great industry level analysis but not so much at the company-level. Who can blame them? Data collection at this level has to be done manually in most cases. Some players like CB Insights realized it could automate some parts in its data collection process (they claim 70%).

There are no shortcuts to achieve superior data quality. By 2012, Correlation Ventures had already partnered with 20 VCs to access their internal statistics and reached to hundreds of companies manually. They gathered a dataset of 80,000 equity financings in which at least a VC firm participated since 1987. These sources are now benchmarked against the internal data available for each company applying for funding. All applicants are required to submit basic planning, financial history, and legal documents (e.g. term sheets, cap tables). The data is then used in the firm’s analytic models. Their sustained efforts have translated in one of the most automated processes in the industry: Once a start-up scores high based on their criteria, only a single 30-minutes interview in person is needed to make a decision. It reduces the time required for decsion making to an average of 2 weeks.

Differently, WR Hambrecht, a US based investor, focuses on a different kind of question: “How can we better predict when innovations will survive or fail?”. As a result, they claims that factors related to a startup’s operations have a predictive power of 20% and that only 12% is related to team. After 8 years of operations, their model has been accurate on 67% of their predictions, and the funds are estimated to achieve returns over 500% based on subsequent offers over their portfolio companies.


Dedicated AI applications aim at automating and expanding the sourcing processes of VCs to diversify its scope, discover promising startups, and discard dubious ones.

The evaluation solutions are more prominent amongst VC firms. External data service providers, specialized in VC, lead in the sourcing field. The investment required to set up an infrastructure to crawl, homogenize, and maintain various data sources works better when servicing a pool of customers rather than when assumed by a single VC, for itself.

But the most data-intensive VCs did not wait to build their own software solutions. For instance, InReach Ventures, which also has an AI evaluation model, invested $7 Mn to develop its proprietary software, assuming maintenance costs over $1 Mn per year. As of December 2017, its suite of AI applications allowed it to evaluate 95,000 European start-ups, and later screened a sample of 2% that were a good initial fit.

Theoretically, the technical process is straightforward. A combination of public and private data sources is first selected, then crawled, consolidated, and finally filtered by investment criteria. The variety of databases in use may turn out to be differential in some cases. For instance, the seed fund SignalFire disclosed that they “collect data from patents to academic publications to open source contributions to financial filings”. SignalFire’s GP also declared to invest in private raw consumer transactions data. Apart from applying these to discover new hidden gems, they opened their data platform to 50 third parties in exchange for filling roles as on-demand advisors to their portfolio companies.

In general terms, these AI processes normally entail data crawling modules (i.e. to map, monitor and extract unstructured sources of data), identification modules (i.e. to homogenize and consolidate company references and understand relations within the start-up network) and clustering modules (i.e. to group and categorize similar players, industries or news). Once these processes are implemented, VCs experienced a noticeable increase in the quantity and diversity of the leads sourced. Fly Ventures claims to discover 1,000 new start-ups per week. Right Side Capital has been able to invest in 850 companies since establishing a new data-driven approach in 2012, allowing the fund to reduce its check size below $300k. Social Capital presents even a lower average allotment, amounting to only $70k per investment, and its recent investments are distributed accross 24 countries with 80% of startups being led be non-white founders.


First, feedback solutions have been developed to provide ad-hoc recommendations for benchmarking start-ups vs. competitors. Solutions deploying this kind of applications are usually present either in one of the two previous categories, as they leverage data sources already gathered to tackle sourcing and evaluation solutions. Roberto Bonanzinga of InReach Ventures, explained the synergy: “By better clarifying which data best translates to successful startups, VCs can educate current and future entrepreneurs”.

Startup Compass, for instance, defends that start-ups should grow proportionally amongst each of its dimensions (team, product …). They developed a tool to warn and guide those start-ups prematurely scaling. Hone Capital aims at guiding entrepreneurs with recommendations based on concrete success metrics, that may have been tested while sourcing and evaluating their leads.

Within training solutions, we have broadly grouped those solutions that not only give feedback to entrepreneurs, but go one step further and deliver the means to improve portfolio companies’ performance. Some of most disruptive solutions can be found in this category.

First, there exist funds supplying data to train and validate the business models proposed by their promising AI investees which has been suffering from data hunger since its inception. For Gradient Ventures, the most recent early-stage Google fund, data was already there. Usually, it is not that easy. The team at Georgian Partners has found out a way of achieving similar results without that playfield advantage. They achieved it using differential privacy techniques where portfolio companies can pool, share, and anonymize proprietary data and contribute from shared insights.

In addition, external providers are also showing very creative solutions. is offering a conversational automated bot that helps founders become better at pitching by replying questions from a bot. The bot can play the role of an incubator, a seed fund or a high-profile VC.

Lastly, matching solutions also exist for start-ups, offering them the possibility to find  investors that are investing in similar startups at the same stage. For example, Dorm Room Fund offer startups and investors alike a list of prospects based on company description, industry, and location.

3. Perspective by data source.


Cross-functional data

Every now and then we spot global data providers expanding their financial data services to VCs. Given that it may be in the interest of venture capitalists, we have considered a wider range of global data providers. Most data solutions cater to different kinds of investors including private equity funds, hedge funds, lenders and large corporates.

  • Digital footprint: Twitter, Facebook, App Store, Web traffic, web forums… Probably the most extensive source of information, especially for B2C companies, with the challenge of extracting and transforming it in useful and understandable insights for investors. Some interesting examples here are iSentium and Dataminr. The former scraps Twitter posts, identify keywords expressing positive or negative sentiments and lastly ranks each companies’ sentiment on a quantitative score. Following a similar philosophy, Dataminr lively monitors social network activity to immediately alert of sentiment-changing events.
  • Financial information: AI has enabled techniques to look at the traditional financial sources on a more innovative and timely manner. On the processing side, Kensho has developed a platform that crawls publicly available company data to help answer financial user queries instantly. On the analytical side, Prattle is discovering insights hidden deep in traditional central bank reports and company earnings calls. They do sentiment analysis based on grammatical structures, nuanced wordings and tones in use.
  • Consumer data: A few companies, such as Earnest Research, specialize on acquiring consumer data, consolidating it and extracting insights on consumer behavior. The CircleUp team, which tries to predict likelihoods of breakout success for over 1.2M US retail companies claims that this sector is uniquely positioned to benefit from data treatment techniques. Its CEO declared that: “The business models of retail companies are very similar. Whether a company is selling dog food, shampoo or water. Second, there’s endless data on consumer product and retail companies“.
  • Satellite images: AI image processing and object recognition techniques are claimed to enable the treatment of satellite imagery to estimate granular economic and demographic metrics, substituting to some extent more traditional economic indicators. For instance, SpaceKnow launched in 2016 an index to monitor industrial activity in China. It claims to process 2.2 Bn satellite observation points and individually monitoring around 6,000 industrial facilities to do so.
  • Team background and dynamics: Finally, there exist a set of venture companies that based on team data seek either to support team dynamics or to evaluate which companies are most likely to thrive. AiNgel, is specialized in scoring companies using data such as educational background, employment history, entrepreneurial experience and personality traits.
  • Transactions data: Credit card transactions are an highly fragmented and unstructured data type, but also the most granular to understand consumer behavior, trends and expenditures. This kind of data has been extensively pooled and analyzed by Second Measure. They claim to access an anonymized selection of 2–3% of all credit card transactions in US down to the store level.


A few years back, the deployment of AI applications constituted a bold and novel bet for VCs with the promise of better dealflow. The point we are touching here, following up on Francesco’s article, is that an increasing number of VC firms are looking at developing their own in-house software to digest and analyze an increasing amount of startup data. However, it’s important to remember not to lose focus dealing with non-core activities, or as Cassie Kozyrkov puts it “Are you in the business of making bread? Or making ovens?”

At PreSeries, we have designed a framework upon which you can automate start-up dealsourcing and assessment efforts. We built our platform to take full advantage of both public and proprietary investor data, while keeping all private information secure. Feel free to get in touch, we’d love to show you how it works.

Arturo MorenoCEO – Twitter

Fabien DurandProduct & Marketing – Twitter

Alfonso PalomeroStrategy Intern – LinkedIn



Entrepreneur. February 6th, 2018. Here’s how AI is changing VC funding –

McKinsey&Company. June 27th, 2017. A machine learning approach to Venture Capital –

Forbes. October 2nd, 2015. Reimagining VC Investing: How Correlation Ventures is Attracting and Keeping the Best New Startups –

XConomy. March 13th, 2018. Too many venture capital cooks in the kitchen –

Medium. March 16th, 2017. Introducing the U.S. Venture Exit Year Index by Correlation Ventures –

Wall Street Journal. January 13th, 2012. Correlation ventures raises 165M$ for data focused investment approach.

Boston Biotech Watch. January 17th, 2012. Quant VC Correlation Ventures new “Dream Date”.

PEHub. January 17th, 2012.. Correlation Ventures Closes $165M Fund That Will Use Predictive Analytics –

Valor. July 19th, 2012. Are micro VCs boosting along short-term entrepreneurs? –

Fortune. August 5th, 2015. Could algorithms help create a better venture capitalist? –

Fast Company. November 19th, 2013. This prediction algorithm can tell if your start-up will fail –

Financial Times. December 11th, 2017. Artificial intelligence is guiding venture capital to start-ups –

Techcrunch. October 22nd, 2015. Watch out, VCs: Chris Farmer says he’s about to massively disrupt the industry –

Medium. October 30th, 2015. Venture capital disintermediation is coming.

Pitchbook. March 15th, 2018. Data driven investing: Why “gut-feeling” may no longer be enough? –

The news stack. May 14th, 2018. Could data-based, human-free investing eliminate bias? –

Bloomberg. May 1st, 2018. Impress the Algorithm. Get $250,000 –

CNBC. July 10th, 2017. Google will invest in AI startups and send its engineers to help them out for up to a year –

The Globe And Mail. April 6th, 2018. Georgian Partners rewriting the rules for venture capitalists as it closes in on record Canadian fund –

Techcrunch. January, 2018. Dorm Room Fund has built a CRM for founders raising a seed round –

Digital Globe. March 30th, 2018. Spaceknow: Using GBDX to bring transparency to the global economy –

J.P. Morgan. May, 2017. Big Data and AI strategies: Machine learning and alternative data approach to investing.

PreSeries Predicts! Our A.I.’s Ranking Of The Top 10 Startups in FinTech

Originally published on Medium

PreSeries predictive algorithms crawl the web hungry for startup information. So far, almost 400k companies have been ruthlessly processed, scored, and ranked. Today, we offer you a sneak peek at the PreSeries Dashboard and our latest ranking of the Top 10 startups in FinTech from around the globe.

Top 10 Startups in FinTech

PreSeries’ Company Ranking — FinTech (March 9, 2018)

Do you agree/disagree with the ranking? Let us know your thoughts on Twitter with the hashtag #PreSeriesPredicts

1 — Nubank

Nubank is the leading fintech in Latin America. Using bleeding-edge technology, design and data, Nubank is committed to fighting complexity and empowering Brazilians to take control of their finances. Over 8 million people have applied for its mobile-controlled credit card since its launch on September 2014. Located in the Pinheiros region of São Paulo, Nubank has raised USD 180 million in investment rounds led by Sequoia Capital, Founders Fund, Tiger Global, Kaszek Ventures, Goldman Sachs, QED Investors and DST Global.

PreSeries’ overall scoring of Nubank over time

2 — StreetShares

StreetShares offers unique financial solutions for America’s heroes and their communities. StreetShares’ technology captures the social loyalty that exists within the military community and harnesses that trust to lower risk in financial transactions. StreetShares provides a suite of specialty finance products focused on the military and veterans market, including small business funding, lines of credit, and alternatives to VA small business loans for vet-owned businesses. StreetShares is also a factoring company offering invoice factoring and account receivables financing for the government contract (GovCon) community, as well as the StreetShares Patriot Express® program. StreetShares offers alternative investments, including a veterans social-impact investing product called Veteran Business Bonds. StreetShares is veteran-run and located outside of Washington, D.C.

PreSeries’ overall scoring of StreetShares over time

3 — Bond Street

Bond Street is a startup focused on transforming small business lending through technology, data and design. Small business owners are the foundation for growth in our economy, and yet today’s banking system has left them behind. They’re building a better future where access to financing is simple, transparent and fair. They’re backed by a renowned group technology and financial services investors and are building a world-class team in New York City.

PreSeries’ overall scoring of Bond Street over time

4 — Shufti Pro

ShuftiPro is a digital identity verification application that offers an intelligent mechanism for the verification of ID card, Passport, Driving License, and Credit/Debit card. Their mission is to enable businesses eliminate customer’s identity frauds and increase their profitability. ShuftiPro is an online verification application designed to minimize online identity frauds while providing businesses a viable solution to trim down the risks involved while maintaining KYC. It has specifically helped online merchants in making online transactions secure. ShuftiPro is currently supporting 150+ languages in more than 150 countries.

PreSeries’ overall scoring of Shufti Pro over time

5 — Bancor

Bancor Protocol™ is a standard for the creation of Smart Tokens™, cryptocurrencies with built-in convertibility directly through their smart contracts. Bancor utilizes an innovative token “Connector” method to enable formulaic price calculation and continuous liquidity for all compliant tokens, without needing to match two parties in an exchange. Smart Tokens™ interconnect to form token liquidity networks, allowing user-generated cryptocurrencies to thrive. For more information, please visit the website and read the Bancor Protocol™ Whitepaper.

PreSeries’ overall scoring of Bancor over time

6 — Cadre

Cadre provides superior access and insight to the universe of alternative investments. Founded in 2014 by Ryan Williams, Cadre is a marketplace where investors benefit from greater transparency, actionable information, lower fees, and more flexibility. The company’s innovative technology drives efficiency and powers insight for its participants. Cadre has raised approximately $135M in funding and transacted on several hundred million dollars worth of investments to date.

PreSeries’ overall scoring of Cadre over time

7 — Proplend

Proplend’s FCA approved peer to peer lending platform connects investors direct to creditworthy borrowers — enabling investors to earn attractive returns and borrowers to gain access to funding that may not otherwise be available. Investors choose which loans and borrowers to lend to, investing to the loan to value-based risk tranche(s) they’re comfortable with. All loans are secured by income producing UK commercial property.

PreSeries’ overall scoring of Proplend over time

8 — Spotcap

Spotcap empowers small business owners with tailored finance, allowing them to focus on what really matters — their business. The company assesses the real-time performance of businesses to grant short-term credit lines. Headquartered in Berlin Germany, Spotcap launched in Spain in September 2014 before expanding to the Netherlands and Australia in 2015, the UK in 2016 and New Zealand in 2017. The company is led by Founder and CEO Jens Woloszczak. The growing team currently consists of more than 120 employees globally. Spotcap is backed by a number of world-class investors including Rocket Internet, Finstar Financial Group, Access Industries, Holtzbrinck Ventures and Heartland Bank.

PreSeries’ overall scoring of Spotcap over time

9 — Octane Lending

Octane Lending is a point of sales finance and insurance marketplace that helps salesmen help their customer’s obtain financing. They are currently focused on the recreational market (motorcycles, ATVs, UTVs, Personal watercrafts, boats, RVs and snowmobiles). Their web based platform helps dealers save time by eliminating the need to rekey customer information and helps move more units by opening dealerships to more prime/subprime lending sources. They leverage their large merchant network to act as an efficient compliment to lenders’ existing loan origination systems.

PreSeries’ overall scoring of Octane Lending over time

10 — Kasisto

Kasisto leverages decades of research and development in artificial intelligence. KAI Banking enables financial institutions to add virtual assistants and smart bots to their mobile apps and leading messaging platforms. With an emphasis on great user experience, KAI-powered virtual assistants and smart bots are easy to implement, customize and maintain.

PreSeries’ overall scoring of Kasisto over time


Love PreSeries AI-driven rankings? Stay tuned, follow us at @PreSeries & #PreSeriesPredicts

Want to give PreSeries a go? Get in touch here!

Startup raises £100,000 from an AI-powered VC in seconds, live on-stage!

Originally published on Medium
7th AI Startup Battle at PAPIs ’17 (Boston, Oct. 2017)

Boston, October 2017… I thought I heard a mosquito fly. The room was packed, but everyone yet remained silent. All were focusing their attention on a little device on-stage. A small black vertical cylinder was stealing the show, before it even started. “Is it … an Amazon Alexa?” said someone, and curiosity was quick to fill the whole room. Everyone had a theory about why a voice-assistant was being prepped like an athlete ready to enter the field. Excited but yet a bit more worried were the people in the front row, startup founders. They knew what was about to happen. An AI was about to judge their startups live on-stage, and in the end, choose a winner. The tension was palpable, “you can read a crowd of ‘human’ investors, but how do you approach an AI”?

Suddenly, a deep voice arose from the stage: “Alexa, ask PreSeries to start the Battle”. A mere second later after the host’s command, the little black cylinder was awake and hungry for startups to judge. “I am ready to score the first startup” it said calmly.

Startup founders then took turns, 1–2 minutes each on stage, to answer questions from the PreSeries AI. The questions revolved around team composition, founders’ background, proprietary technology or industry characteristics.

PreSeries in action!

“Ok, that’s everything I needed to know” said PreSeries. It only took 8 minutes to collect and evaluate the necessary information to determine, among 5 contenders, which one is most likely to succeed. “Ask PreSeries who is the winner?” commanded the host. “The winner is GreenSight Agronomics” replied the AI. One of the attendants, a local VC, said “the funny thing is that, the winner was my favourite!”

The next AI Startup Battle will be at PAPIs Europe (April 5 — London)

Impressed by an AI thinking like a VC? Now remember about the last time that you missed on a deal because your process was too slow… Maybe you took a lot of time to learn about that company… Or maybe your decision making process was too long because you needed to gather more data … The problem here is our own limitations as investors. Spotting the signals that are good predictors of success for startups is not easy. Because we’re dealing with such uncertainty, the amount of data needed to decently derive startups insights and uncover actionable trends is considerable. Fortunately for us, machine learning (ML) provides the answer.

We are witnessing the adoption of ML across the entire venture capital industry at an ever-accelerating pace. Some VC firms started a while ago to use machine learning, but most of them are just taking their first steps. From data collection, processing, to predictive insights … the preachers of disruptions are the ones being slowing disrupted (Google, Hone Capital,, InReach Ventures, Sequoia, Kleiner Perkins, etc.)

If you invest in startups for a living and feel that you are not doing any of this …

We created PreSeries with the vision of a faster, more efficient and transparent process to allocate resources in the startup community. We do the heavy lifting when it comes to data collection and predictive modeling.

The more relevant data you feed machine learning models, the better the quality of the analysis. By using machine learning in lieu of manual guess-estimates (read “spreadsheets”) to evaluate startups, you not only address the breadth of information you can handle, but you’re also able to automate and drastically reduce the cost of the whole process.

We built PreSeries for that purpose. We want startup investors to concentrate time and money where it matters, not on technical tasks like collecting, processing, or evaluating data. Startup scouting and assessment should be like breadcrumbs in your budget. Let us show you how!

We argue that by liberating time from scouting and screening, you can spend time on helping your portfolio companies, which is what we believe venture capital will be all about in the future. (Check out Hunter Walk’s VC time distribution to get a better idea of current time distribution.)

Let our AI invest £100,000 in your startup!

We recently announced that London-based AI Seed venture capital fund will be investing £100,000 in the winner of the next AI Startup Battle that will take place on April 5, 2018 in London at Europe. And as you already know: No humans in the jury. More info and prizes details here!


PreSeries joins FinTech Sandbox

Originally published in Medium

Building a FinTech startup is like riding a carriage on a dirt road. Sure it’s exciting to follow the path less traveled, but say hello to the bumpiest ride of your life. In this analogy, let’s imagine that PreSeries, our machine-learning platform for startup investors, is a FinTech carriage that needs to find its way through the “data potholes”. With practice, navigating through the uncharted territory of startup data becomes a second nature, but the dream of a road paved with better data remains strong.

The 4 steps of working with startup data!

But why is working with startup data such a challenge? At PreSeries, we are building an automated platform to scout and assess startups from around the globe in few clicks. It goes without saying that startup data is our lifeblood but is … well … scarce, often outdated, expensive to source, and you encounter missing data as often as the word “disrupt” at a tech conference. That’s the nature of working with early-stage private companies, they’re not really open books. But hey, hate the game not the players, right?

This is why we are very happy to announce that PreSeries is joining the FinTech Sandbox program. FinTech Sandbox is a Boston-based nonprofit that drives global FinTech innovation and collaboration. Their 6-month program provides access to data feeds and APIs from industry leading data partners, top quality cloud hosting from infrastructure partners, and much more. FinTech Sandbox is a thriving community of 2,200+ members, 70+ startups, and 40+ partners. We are thrilled to join this growing digital family!

This is an important step for us!

  1. Being part of such an amazing community of FinTech passionate experts makes us really proud. If you are amazed by the team running FinTech Sandbox (jean donnelly, David Jegen, Sarah Biller or Mona M. Vernon to name just some), or the data partners (ThomsonReuters, S&P Global, Dun&Bradstreet or Edgar to name a few), you would also like to check the startup alumni section: Quantopian, CircleUp or Nutonian among others.
  2. Access to new premium data streams will help us increase the quality of our machine learning models. We want to develop the right models and tools so that our users are later on able to access and customize depending on their preferences.
  3. Lastly, we are excited to work with the FinTech Sandbox data partners and explore ways to develop long-standing relationships with them. We are advocates for more data to find and assess startups and are excited to open a whole new market in terms of data consumption with the venture capital community.
The PreSeries Dashboard

Our mission is to build the long-awaited crawling & machine-learning infrastructure needed for better startup scouting and analysis, so startup investors don’t have to! For venture capitalists, our SaaS platform is eliminating the time and cost of building their own machine-learning solution by democratizing access to predictive technologies. We are saving investors an estimated 2 to 5 years of development and between $6 to $10 million a year in development and maintenance cost (infrastructure, data providers, engineers and analysts salaries, etc.).

On a last note, I want to stress the fact that PreSeries is growing and looking for passionate people to join the team. If you want to help us make venture capital a more data-driven practice, fill out our application form! We’re looking for data engineers, data scientists, designers, front-end developers, as well as sales & marketing people. Looking forward to your application!

GreenSight Agronomics wins the 7th A.I. Startup Battle at PAPIs ’17

The 7th A.I. Startup Battle (hosted by PAPIs ’17 at Microsoft N.E.R.D.) came to an end yesterday in Boston in fully automated fashion. The jury of this unique competition, PreSeries’ algorithms, has predicted that GreenSight Agronomics is the startup most likely to succeed in comparison to the three other contenders. After an intense Q&A session between the startups and PreSeries (through a voice-enabled device present on stage), the A.I. ranked the participants and crowned GreenSight Agronomics. They provide an automated, agronomic intelligence platform for superintendents, land managers, and farmers. They combine automated drones, patented sensors, and proprietary analytics to deliver customers maps and alerts enabling them to better manage water, pesticide, and fertilizer usage. They are already the leader in turfgrass remote sensing and analysis. They are helping Top-100 golf courses across the U.S. and Canada reduce water consumption, better task labor associated with irrigation and moisture measurement, and achieve better outcomes with fewer fungicides, pesticides, and fertilizer.

Winners of the 7th A.I. Startup Battle: GreenSight Agronomics represented by James Peverill, CEO & Lilian Ting, Director of Business Development

In the battle, all five startups have had the chance to introduce their company during a 5­-minute pitch prior to the human-A.I. interaction. Later on, PreSeries’ AI took the time to ask some questions to all the contenders about key aspects of their business. The exchange was made possible through a voice-assistant device present on stage (our very own ‘VC-in-a-box’).

Klarity, a software that automatically reviews sales contracts (NDAs, Beta, MSA/SLA agmts), so lawyers don’t need to be involved, came in 2nd position. They have completed the first module that uses Natural Language Processing to analyze NDAs and provides the user with (1) an overall risk profile, (2) a list of clauses present and non-present with individual risk profile attached to each clause (green, yellow, red), and (3) detailed analysis of the implications of a clause to the business + actionable insights that help renegotiate the agreement. Their goal is to expand to other contract types and industry verticals and eventually automate all contractual work.

Pearlo, a cloud-based application that helps busy professionals to quickly and inexpensively improve their processes arrived in 3rd position. It is powered by artificial intelligence and, once the user enters the narrative describing how the current process works, the tool delivers a graphical representation of the process, an efficiency score and a set of recommendations for improvement., a startup that empowers the many media artists around the world to collaborate remotely in a virtual studio arrived in 4th position. They help collaboration between humans and AI artists, to develop and commercialize original media content (music, film & VR). Their platform provides advanced editors, rights management, and global content distribution. They are a team from MIT and Berklee College of Music, with advisors including Jack Dorsey (CEO of Twitter).

After the event, BigML CEO & Co-founder and President of PreSeries, Francisco J. Martin, shared his thoughts: “Our startup battles have inspired a wide audience of investors and startup founders across the globe who are looking for concrete, quantifiable feedback on their performance on a continuous basis. Boston has naturally become a regular stop given the depth of the startup ecosystem here. We are impressed with arguably the most accomplished set of competitors to date in this 7th edition of the Startup Battle and wish them the best in attracting new customers, gaining further traction and securing new rounds of financing.”

GreenSight Agronomics, the winner of the battle has now the chance to participate in Telefónica Open Future _ Program. In this respect, it is eligible to access, up to six months, to Telefónica Open Future’s pre-acceleration services, subject to space availability (desk space and connectivity). After the six-months of pre-acceleration, PAPIs ’17 winner will be evaluated and, in case of a positive evaluation by Telefonica Open Future, PAPIs ’17 winner may have access to Wayra’s Acceleration Program. Wayra Acceleration Program offers financing for up to 50.000$ in the form of a convertible note and acceleration services, for a maximum period of 12 months, valued at a maximum of 70.000$, subject to the fulfilment of certain milestones agreed with Wayra (in the form of physical co-working space for the team, connectivity services, access to its network and know-how, consultancy services, entrepreneurship training, access to the Wayra network of potential investors, other entrepreneurs and practitioners from the venture capital industry).

For Ana Segurado, Telefónica Open Future_ Global Director, events like the one that we are hosting demonstrate the importance of making use of more technology to help investors make data-driven decision and avoid poor investment outcomes: “Machine Learning increasingly facilitates work with an amount of data and stages that, otherwise, make pricing decisions similar to playing in a casino with marked decks. It gives investors very valuable information that must be very seriously considered to make decisions in a short period of time”.


Our next AI Startup Battle will be in London in April 2018, stay tuned on Twitter with #AIStartupBattle and @PreSeries.

Meet the Contenders of the 7th A.I. Startup Battle in Boston!

PAPIs ’17, the 4th International Conference on Predictive Applications and APIs, is a series of annual events where experts come discuss new developments, opportunities and challenges in real-world Machine Learning. This year, PAPIs ’17 returns to Boston, on October 24-25, 2017, and will host the 7th Edition of our AI Startup Battles (Oct. 24).

The audience, mainly decision makers and developers interested in the latest technology to build real-world intelligent applications, will witness the power of PreSeries, a predictive application that provides fact-based insights as well as many other investments and traction related metrics to help investors foresee which companies warrant a potential investment. PreSeries predictive models are trained with a diverse set of public and private data on more than 370,000 companies worldwide. On the 24th, after the startup pitches in the morning (schedule), our PreSeries A.I. will engage in a Q&A session with the 4 contenders through a voice-enabled device present on stage. It will then score and rank the startups. No human interference will occur during the scoring process, only PreSeries’ algorithms will be in charge of selecting the winner.

Meet the contenders!

At this point, you may be wondering who will be competing in the battle, so let’s get to know the contenders.


Klarity builds software that automatically reviews sales contracts (NDAs, Beta, MSA/SLA agmts), so lawyers don’t need to be involved. We have completed the first module that uses Natural Language Processing to analyze NDAs and provides the user with (1) an overall risk profile, (2) a list of clauses present and non-present with individual risk profile attached to each clause (green, yellow, red), and (3) detailed analysis of the implications of a clause to the business + actionable insights that help renegotiate the agreement. Our goal is to expand to other contract types and industry verticals, and eventually automate all contractual work.

GreenSight Agronomics

GreenSight provides an automated, agronomic intelligence platform for superintendents, land managers, and farmers. They combine automated drones, patented sensors, and proprietary analytics to deliver customers maps and alerts enabling them to better manage water, pesticide, and fertilizer usage. They are already the leader in turfgrass remote sensing and analysis. They are helping Top-100 golf courses across the U.S. and Canada reduce water consumption, better task labor associated with irrigation and moisture measurement, and achieve better outcomes with fewer fungicides, pesticides, and fertilizer.


NewPearl, Inc. is the creator of Pearlo. Pearlo is a cloud-based application that helps busy professionals to quickly and inexpensively improve their processes. It is powered by artificial intelligence and, once the user enters the narrative describing how the current process works, the tool delivers a graphical representation of the process, an efficiency score and a set of recommendations for improvement. empowers the many media artists around the world to collaborate remotely in a virtual studio, with other human and AI artists, to develop and commercialize original media content (music, film & VR). Our platform provides advanced editors, rights management, and global content distribution. They are a team from MIT and Berklee College of Music, with advisors including Jack Dorsey (CEO of Twitter).

Stay tuned!

Be sure to stay tuned as the winner will be announced right after the event on social media (on Twitter with #AIStartupBattle) as well as on our blog. For more details, please follow us on: LinkedIn, Google+, Facebook, or Twitter. The countdown starts now!

Spotlight Feature: Dashboard API

Making predictions about startups is good, but letting everyone integrate predictions about startups in their apps is even better. And that’s because the real value of predictions is realized when we go beyond “predictive analytics” and start becoming “prescriptive”. In other words, it’s all about making predictive insights actionable. This is the reason why we are releasing our Dashboard API, to let you do just that. Now, all of our predictive scores about startups, investors, and areas, can be embedded into any of your apps and integrated into any of your decision-making processes. For example, you can now combine constant tracking of the performance and traction of your portfolio companies as well as those of your competitors in your own internal app, which also includes top secret financials for your portfolio companies. Currently, our Dashboard API is only accessible to subscribers of the Analyst Plan and our partners (Interested in a partnership? Get in touch with us!).

All of PreSeries, one API (our API domain) gives you access to the following predictions: company search and company data, as well as the following resources: acquisition, deactivation, company, investor, IPO, patent, person, product, and round. conforms to the design principles of Representational State Transfer (REST). is entirely HTTPS-based. You can create, read, update, and delete resources using the respective standard HTTP methods: POST, GET, PUT and DELETE. All communication with is JSON formatted.


Company related properties available via the PreSeries API.

The Company Search allows you to find companies in PreSeries based on preferences. The Company Data endpoint allows you to find information about all the company predictions made by PreSeries. The endpoint is available in two different modes: (1) one snapshot: data related to a specific snapshot. You need to choose between only_last_snapshot or snapshot_date as a query property. (2) time series: all company data points listed in chronological order. This is the default option if neither snapshot_date nor only_last_snapshot is informed. You can specify a time period using the interval property.

A sample of the 38 Company creation related properties available through the PreSeries API.


A sample of the 39 Company related properties available through the PreSeries API. also allows you to list, create, retrieve, update, delete your data about company activations, deactivations, general information, investors, IPOs, patents, people, products, rounds of investments.

A sample of the 41 Patent related properties available through the PreSeries API.

A complete list of all the predictions and resources available through the Dashboard API can be found here: We’d like to know more about your application ideas so feel free to get in touch with us to request API access!

PreSeries opens its doors!

We are thrilled to report that PreSeries is opening its doors! As we celebrate the official launch, we’d like to invite all of you to try it out.

Enter PreSeries

If you work in venture capital, corporate strategy, innovation, M&A or investment banking, chances are you are spending too much of your time scouting for the most reliable and up-to-date data sources only to spend even more time to clean, prepare the data, and perform analyses to derive meaningful insights. Given this complex data landscape, PreSeries is positioned to be the Swiss Army knife that gives you an unfair advantage over other investors. With our unique approach taking advantage of Machine Learning to assess a startups’ likelihood of success, we allow you to cut through the noise and focus on what matters. After all, data-driven approach to early-stage investment is the only way to remove our human biases once and for all.

PreSeries is also the perfect solution for cash-hungry entrepreneurs. Fundraising activities often feel like hunting for whales on makeshift raft for many startup founder’s in an ocean of investors with fickle interest. Using PreSeries to target the right VCs is the equivalent of upgrading to a naval grade vessel. Now, you’re finally able to gain first-hand knowledge of VCs most likely to invest in your company and discard the ones that will likely take your precious time by making you run around the block twice.

If you are an analyst or a data-scientist, there is a high probability you’re not a fan of data fairytales. You have to see it to believe it. Well, you’re in luck! With the PreSeries Analyst Platform (powered by BigML), we give you access to PreSeries’ Machine Learning engine room. You’ll be able to look under the hood to not only access but also customize our consolidated datasets, Machine Learning models, predictions and much more.

More blog posts detailing specific PreSeries’ features will be published on a regular basis, so stay tuned!

P.S.: If you’re interested in a demo, simply get in touch with us at and we’ll schedule one in no time.