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).

Introduction

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

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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.

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Evaluation

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.

Sourcing

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.

Value-added

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. Pitchbot.vc 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.

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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.

Conclusion

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

 

Sources

Entrepreneur. February 6th, 2018. Here’s how AI is changing VC funding – https://www.entrepreneur.com/article/309198

McKinsey&Company. June 27th, 2017. A machine learning approach to Venture Capital – https://www.mckinsey.com/industries/high-tech/our-insights/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 – https://www.forbes.com/sites/mnewlands/2015/10/02/reimagining-vc-investing-how-correlation-ventures-is-attracting-and-keeping-the-best-new-startups/#2c4bca393929

XConomy. March 13th, 2018. Too many venture capital cooks in the kitchen – https://www.xconomy.com/san-diego/2018/03/13/too-many-venture-capital-cooks-in-the-kitchen/

Medium. March 16th, 2017. Introducing the U.S. Venture Exit Year Index by Correlation Ventures – https://medium.com/correlation-ventures/u-s-venture-exit-year-index-by-correlation-ventures-1bf98d1077a9

Wall Street Journal. January 13th, 2012. Correlation ventures raises 165M$ for data focused investment approach. https://blogs.wsj.com/venturecapital/2012/01/13/correlation-ventures-raises-165m-for-data-focused-investment-approach/

Boston Biotech Watch. January 17th, 2012. Quant VC Correlation Ventures new “Dream Date”. https://bostonbiotechwatch.com/2012/01/17/quant-vc-correlation-ventures-vcs-new-dream-date/

PEHub. January 17th, 2012.. Correlation Ventures Closes $165M Fund That Will Use Predictive Analytics – https://www.pehub.com/2012/01/correlation-ventures-closes-165m-fund-that-will-use-predictive-analytics/#

Valor. July 19th, 2012. Are micro VCs boosting along short-term entrepreneurs? – http://vator.tv/news/2012-07-19-are-micro-vcs-boosting-along-short-term-entrepreneurs

Fortune. August 5th, 2015. Could algorithms help create a better venture capitalist? – http://fortune.com/2015/08/05/venture-capital-hits-average/

Fast Company. November 19th, 2013. This prediction algorithm can tell if your start-up will fail – https://www.fastcompany.com/3021903/this-prediction-algorithm-can-tell-if-your-startup-will-fail

Financial Times. December 11th, 2017. Artificial intelligence is guiding venture capital to start-ups – https://www.ft.com/content/dd7fa798-bfcd-11e7-823b-ed31693349d3

Techcrunch. October 22nd, 2015. Watch out, VCs: Chris Farmer says he’s about to massively disrupt the industry – https://techcrunch.com/2015/10/22/watch-out-vcs-chris-farmer-says-hes-about-to-massively-disrupt-the-industry/

Medium. October 30th, 2015. Venture capital disintermediation is coming. https://startupsventurecapital.com/introducing-global-beta-ventures-ad49dd7bebd0

Pitchbook. March 15th, 2018. Data driven investing: Why “gut-feeling” may no longer be enough? – https://pitchbook.com/news/articles/data-driven-investing-why-gut-feel-may-no-longer-be-good-enough

The news stack. May 14th, 2018. Could data-based, human-free investing eliminate bias? – https://thenewstack.io/could-data-based-human-free-investing-eliminate-bias/

Bloomberg. May 1st, 2018. Impress the Algorithm. Get $250,000 – https://www.bloomberg.com/news/features/2018-05-01/white-male-vcs-tend-to-fund-white-male-entrepreneurs-could-robots-do-better

CNBC. July 10th, 2017. Google will invest in AI startups and send its engineers to help them out for up to a year – https://www.cnbc.com/2017/07/10/google-launches-gradient-ventures-to-invest-in-a-i-start-ups.html

The Globe And Mail. April 6th, 2018. Georgian Partners rewriting the rules for venture capitalists as it closes in on record Canadian fund – https://www.theglobeandmail.com/business/article-georgian-partners-rewriting-the-rules-for-venture-capitalists-as-it/

Techcrunch. January, 2018. Dorm Room Fund has built a CRM for founders raising a seed round – https://techcrunch.com/2018/01/25/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 – http://blog.digitalglobe.com/technologies/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!

A VC-in-a-Box crowned Dataholics.io at the 6th AI Startup Battle in São Paulo

The 6th Artificial Intelligence Startup Battle came to an end yesterday in São Paulo in fully automated fashion. The jury of this unique battle, PreSeries’ algorithms, has predicted with a score of 96.50 out of 100 that Dataholics is the startup most likely to succeed from among other contenders. Dataholics captures and structures millions of data points about people on social networks such as Facebook, Linkedin, Google, Twitter, Google search results, blogs, web portals and online services. Their algorithm creates a unified profile for each person based on behavioral, professional and demographic indicators from their email, cell phone, name or ID.

From left to right: Renato Valente – Country Manager – Telefonica Open Future_ & Wayra Brasil; João Gabriel Souza – Co-Founder & CEO – Mr. Descartes; Eduardo D. Martucci – Founder and CEO – Voice Commerce; Daniel Mendes – Founder and CEO – Dataholics; Dhiogo Corrêa – Data Architect – Itera; Rafael Libardi – Public Relations Executive – Data H.

In the battle, all five startups have had the chance to introduce their company during a 5­-minute pitch. 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 (thus the name ‘VC-in-a-box’).

Itera, came in 2nd with a score of 86.81. Itera is a technology company founded in 2008 and established in São Carlos/SP, always aiming to build innovative solutions for its clients. They are now investing in a machine learning platform for text mining named ALICE. The platform is currently focusing on finance, and marketing case studies.

Mr. Descartes got the 3rd position with a score of 62.13. This company provides a chatbot to help cities improve their waste management and sustainability. They work in collaboration with local governments, businesses, and people from the community in order to generate data, educate the public and build lasting partnerships.

Voice Commerce was the 4th startup in the ranking with a score of 62.12. Voice Commerce is a voicebot that provides anyone with a simple, objective and secure online purchase experience through voice commands. It creates the perfect solution for people with visual impairment when buying goods and services online.

Finally, Data H achieved the 5th position with a score of 62.10. DATA H is a startup focused on creating intelligent products and artificial intelligence outsourcing of research and development. DATA H has created its own ecosystem to enable artificial intelligence projects for a diverse set of sectors.

After the event, BigML CEO & Co-founder and President of PreSeries,Francisco J. Martin, shared his thoughts: “Having organized our 6th AI Startup Battle in only the last year and a half across the globe, it is amazing to us that humans are surprisingly open and adaptable in trusting PreSeries algorithms to assess the future prospects of startups. What started as a crazy idea has come to be seen as an obvious need. This can be attributed to the investment professionals being overwhelmed with mountains of new data created every day, which in turn highlights the acute need for objective assistance and automation.”

This edition of the battle took place on June 21 in São Paulo, Brazil, at the PAPIs Connect conference, Latin America’s 1st conference on real-world Machine Learning applications.

Our next AI Startup Battle will be in Boston (Microsoft N.E.R.D. – MIT) for PAPIs ’17 (Oct. 24-25), stay tuned on Twitter with #AIStartupBattle and @PreSeries.

Get your own VC-in-a-Box! (Voice Assistant)

If I ask you to name an important stakeholder that is often an overlooked key component of business operations, I’m sure “assistants” are not the first thing popping up in your mind. But if you look closely, regardless of their title, it’s hard to overstate their contribution to any company’s productivity by helping management staying focused on most important tasks.

As technology has advanced, the skill sets of assistants have continued to evolve to keep apace. The arrival of chatbots and better speech recognition is already drastically changing the landscape by allowing for the automation of many data intensive tasks. It is now possible for many to become their own globally-connected assistant, at a fraction of the cost of hiring a human assistant for such tasks that are better suited for software.

The latest advances in the field of Artificial Intelligence and voice recognition technology is slowly redefining our relationship to our electronic devices. Human-computer interactions are becoming more human-like and the promise of naturally conversing with your devices is just around the corner. The first wave of Voice Assistants lead by the Amazon Echo and its Alexa software appeared late 2014 and their adoption rate has shown no signs of slowing down. After conquering our homes, the battlefield has now moved to businesses as they aim to supercharge the productivity of workplace teams. Acting as task-oriented assistants, are fast becoming tomorrow’s colleagues-in-a-box.

With PreSeries we make it easy for anyone to predict the future of startups. But when fully concentrated in your work, opening the PreSeries Dashboard to get some insights can disrupt your workflow. Simply asking your question out loud, as you would with a colleague or an assistant, feels more natural and allows you to remain focused. Therefore, we built our own PreSeries for Alexa skill in order to bring startup intelligence to devices belonging to the Amazon Echo family. Main features are the scoring of startups in real-time and getting aggregate or detailed information about startups, technology areas, investors, patents, rounds of financing, and IPOs.

Once the skill is enabled, you can use your brand new VC-in-a-Box to discover all kinds of information regarding startups. Below is an example session.

You: “Alexa, ask PreSeries how many startups were founded in the US in 2016”

PreSeries: “The number of startups founded in the US in 2016 is 3857.”

You: “Alexa, ask PreSeries what is the highest scoring startup in the US in Education”

PreSeries: “The highest scoring startup in the category of education is AcmEd”

Check out our introduction video below!

Click here to add the skill to your Alexa powered device and don’t forget to check out our documentation for a complete overview of the questions you can ask your VC-in-a-box.

Calling Startups to Compete at the Sixth Edition of the AI Startup Battle at PAPIs Connect in São Paulo

PAPIs Connect 2017, Latin America’s 1st conference on real-world Machine Learning applications, is a series of localized events that run in between the annual PAPIs conference events, the International Conference on Predictive Applications and APIs. This year, PAPIs Connect goes to São Paulo, Brazil, on June 21-22, 2017, and will hold the Sixth Edition of our Artificial Intelligence Startup Battles.

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 built on top of BigML’s Machine Learning platform that provides fact-based insights and many other investment 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.

The sixth edition of the AI Startup Battle is powered by PreSeries, the joint venture between Telefónica Open Future_ and BigML.

Apply now! 

If your startup uses AI as a core enabler, this battle is your chance to participate in the 6th edition of PreSeries’ AI Startup Battle. The startups selected to compete will be able to pitch on stage, make connections at PAPIs Connect, and get unique exposure among a highly distinguished audience attending Latin America’s 1st conference on real-world Machine Learning applications. The winner of the battle will be taken into consideration by the Wayra Academy and has a chance to get invited to Telefónica Open Future’s acceleration initiatives and services that include: training, coaching, a global network of talent, as well as the opportunity to reach many Telefónica enterprises in Brazil and abroad.

When?

Thursday, June 21, 2017 at 4:30 PM BRST.

Where?

Telefônica Auditorium:  R. Martiniano de Carvalho, 851 – Bela Vista, São Paulo – SP, 01321-001, Brazil.

Application Deadline?

To compete in the battle, please fill out this form before June 11, 2017, and send a short presentation about your company (up to 2 MB) to battle@preseries.com.

For more information on previous AI Startup Battles we recommend that you visit the dedicated page including all the battles performed so far.

Spotlight Feature: Company Trends

When on the lookout for new investment opportunities, being able to identify trends is the first step on the road to uncover the next most promising industries and startups. With a growing number of early-stage companies keeping traditional markets saturated, we see competition intensifying and under-the-radar industry areas getting more attention than ever. Keeping tabs on numbers of investments, amounts invested, numbers of exits, and other relevant metrics from the startup world, is the foundation for a successful data-driven investment practice that knows when to deviate from the beaten path. The PreSeries Company Trends section is the perfect feature to future proof your investment strategy by understanding where to direct your attention to stay ahead of the pack.

Of course, not every trend matters to everyone. As a professional investor, you most likely follow an investment thesis, a strategy that helps you determine which companies to invest in. In PreSeries, the Company Trends feature aims to help you find trends that match your investment philosophy. As seen above, you can find trends for companies matching your desired criteria such as: status (Running, Acquired, Acqui-hired, IPO, Zombie, and Closed), location (Country and City), industry area (more than 700 to choose from), and date of foundation.

After applying the filters, you can choose to generate visualizations from 11 different trends charts. The Founded Companies chart represents the evolution of the number of companies founded over time. The Closed Companies chart does the same but for closed companies. The IPO and Acquired Companies charts depict the number of companies with successful exits over time.

You can also generate charts for relative values such as Time to IPO, Time to be AcquiredTime to Close and Time to be Funded. These charts are good indicators on how quickly companies reach any of these states. The sample chart above answers the question “How much time usually elapses between the founding of the companies and the IPO of the companies?”. Last but not least, for any given graph, you can download the visualization as a PNG file and the results in CSV format.

That’s it for Company Trends. Make sure to check our related post on the Spotlight Feature!

Spotlight Feature: Company Search and Rankings

Whether you are an investor on the lookout for the next promising startup, an incubator program searching for the best way to filter your never-ending stream of applications, or simply an entrepreneur gathering intel on your competitors, you need lots of data, a way to filter the noise, and uncover hidden signals. Today, most of the decision-making traditionally resulting from gut-feel and simple business rules are being slowly replaced by data-driven approaches that go beyond our limited analytical skills as humans. We are in the middle of such a revolution and today’s large trove of data coupled with cutting-edge machine learning techniques makes this possible.

But with large amounts of data, comes great anxiety. It’s one thing to have a large dataset, it’s even better when you can make sense of it and quickly derive meaningful insights. If that is what you are looking for, then PreSeries is the right fit for you. We use machine learning algorithms to analyze over 300k+ companies and generate predictive scores that represent their chances of success. The best way to search and filter these companies is through the Company Search & Rankings feature accessible from the Dashboard.

Above, is an example of the combination of filters you can apply to search for specific companies. You can specify company status, location (country and city), development stage (concept, seed, product development, market development or steady), and industry areas (e.g. “Mobile” and “Personal Health”).

Once the filters are applied, the result page ranks the companies matching the chosen criteria. By default, all companies are ranked by their overall score in the Top Companies tab (as seen above). The result page includes the following information: company name, current status, location (country and city), current stage, industry areas, overall score and recent changes in overall score. Clicking on the company name will automatically redirect you to the corresponding Company Profile page. You can find more about company profiles here. Clicking on a Country, a City or a specific Area will display all companies matching the selected criteria. The “star” and “pin” icons under the company names are quick and easy ways to add companies to your Starred Companies and Bookmarks respectively.

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The Mover and Shakers tab is similar to the Top Companies tab, but instead of ranking the companies using their overall scores (i.e., absolute values), it ranks them by the recent increase in their overall scores (i.e, relative values). Companies listed in this tab are worth paying attention to, a sharp increase in score is often the reflection of improved performances and maybe even a hidden unicorn?

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That’s it for the Company Search and Rankings! As always, we’d love to hear about your thoughts and feedback.