Startup accelerators and incubators usually run between one and three batches a year, some of them more. To select the participants of a single batch even smaller programs have to review more than two-hundred applications.
Data on applicants is often stored in structured databases (Excel, CRMs, or text forms). Those resources work fine for data on stable companies, but most startups pivot, sign term sheets, get traction, get PR coverage, move countries, join accelerators, and many of them fail.
Inevitably aggregated data and filled forms become quickly out-dated.
We aim at fixing those inefficiencies by building a startup encyclopedia made of data and knowledge resources that self-generate.
To do that we use knowledge-graphs and proprietary Natural Language Processing and Deep Learning algorithms to integrate openly available data-sets with the various startup resource available on the web.
Where data-sets aren’t available we turn into monitoring millions of company websites and social media to leverage unstructured data for the classification of relevant startup data.
Tracking conversations and prospective startups
Scouting teams are busy having conversations with founders at various events: seminars, conferences, meetups, and 1on1s.
Many founders have solid startups and all the intention of joining a program, but things can get in the way and they won’t finalize their application. That doesn’t make their ventures any less worthy.
Regardless of how prospective their companies are, rarely accelerators attempt at tracking progress after their first conversations. That’s mostly because routinely checking-in with each founder is rather difficult.
We help keep track of prospective companies through:
- smart alerts that are triggered based on company events
- updating data-sets that feed our Query Tool
- data automatically synched into our articles
Tracking Portfolio’s progress
Your portfolio keeps growing with each new investment, and that makes it more challenging to follow each companies’ trajectory.
We help you keep track of the portfolio’s status at any point in their journey.
Doqume’s sourcing services help scout startups in niche markets and in any geography. Whether you’re an accelerator, incubator, innovation lab, or a startup ecosystem player you have an interest in finding prospective projects, and founders. We help you discover them even before they get into other databases.
To do that we leverage our proprietary NLP topic prediction models to identify newly created innovative startups and classify them for you so that you can focus on running your day-to-day operations.
We’ve designed a powerful and simple tool for querying startups and ecosystem data.
Use our powerful query-tool to get data on your accelerators’ portfolio, to list startups you’ve previously met with, filter them by updated location/market/stage/etc., identify investment trends of other programs, list LPs/VCs or any other investment firm, and much more.
And all from within a single platform accessible to all your team.
We research and write about startups and topics you are interested in.
Out knowledge bases are built to ensure data stays up-to-date. We do that by tying data and articles together so that changes in one are automatically updated in the other.
Doqume is a developing body of knowledge. Think of Wikipedia in its infancy. Thanks to our technology we also grow very fast, but we have to take into account that we might lack certain topics.
For this reason, we’ve integrated a function for requesting specific researches. This is to help us prioritize areas where there might be certain gaps in entities covered and data-sets available.
Our alerting tool allows building alerts on company events by specifying the company you want to receive alerts about and the specific event that interests you.
For example, you can be notified when one of the startups you’ve interviewed for the batch has had a PR launch, or presented a new product on Hackernews.
With Doqume we want to build a single place of reference for the research of startups and their ecosystems. One that integrates various open data sets, open online communities, social media, online information, and individual or corporate-owned knowledge bases.
A knowledge-map glued by semantic technology and deep learning to provide a central place of reference for doing and sharing research of innovation-building organizations.