LENSENTIAL SCOUT PREVIEW
Startups, founders, investors and research, in one graph.
Lensential Scout finds young companies in public records, merges every sighting into one record per company, and links the people, investors, programs, universities and research around it. Every fact carries its source. Today it covers Texas, with a national view of robotics research.
Lensential Scout is a preview. It is built only from public records, such as SEC Form D filings, federal grant and contract records, university and program web pages, company websites, and OpenAlex. It covers Texas companies, Texas researchers in robotics, AI and defense technology, and US researchers in robotics. Labels marked heuristic are machine reads and can be wrong; every fact links to its source. Lensential Scout does not recommend for or against any investment. To correct or remove an entry, write to hello@lensential.ai. Our privacy policy says how we handle these records.
- 4,280Texas companies, one record each
- 32public-source collectors
- 1,076Texas robotics researchers in the spinoff pipeline
- 6,805US robotics researchers in the expert search
Counts as of October 14, 2026. They change with every refresh.
ASK THE GRAPH
The questions it answers today, and the ones it will.
Every question below runs on the same graph. The tag says what you can do in the preview today.
Which AI-native seed companies in Austin show a fundraising or hiring signal?
A saved filter you can edit. Each company links to its SEC filing or its careers page.
LIVE IN PREVIEWWhich Texas companies raised up to USD 6 million in the last 120 days?
Read from SEC Form D filings, each one linked.
LIVE IN PREVIEWWhich startups won an SBIR or STTR Phase II but have not filed a Form D?
The data is in the graph; the filter is not built yet.
NOT YET BUILTWho are the leading US researchers on actuators for robots?
The expert search ranks researchers for a plain question, with the score broken down.
LIVE IN PREVIEWWho outside the company could judge this founder's technical claims?
The expert search, leaving out the company's own founders. It feeds the product and build checks in Lensential Venture, and anything it finds is checked against the primary record before it goes into a paid paper.
LIVE IN PREVIEWWho runs this company, who funds it, and where does each fact come from?
One profile per company, every line with its source and date.
LIVE IN PREVIEWWho invests alongside a fund you name?
Ask in plain language; each answer shows the links it used.
INVITED USERSWhich Texas funds raised a new vehicle this year, and where are they already invested?
The latest fund filing appears on each investor's record.
PARTLY LIVEWhich accelerators feed which venture funds?
A two-step question over programs, companies and investors. The data is in the graph; the view is not built yet.
NOT YET BUILTWhich Texas robotics researchers are close to starting a company?
A spinoff pipeline with an itemized score of how ready each research result is to become a company.
LIVE IN PREVIEWWhich robotics inventions can be licensed at Texas universities, and who invented them?
Technologies listed for licensing, with their named inventors.
LIVE IN PREVIEWWhich universities turn research into companies?
A census of 48 Texas institutions, graded on what they can commercialize.
LIVE IN PREVIEWUSE CASES
What it does today.
Funnel discovery
Saved filters over Texas companies, with pass, fail or unknown on every criterion, and an export shaped for funnel triage in Lensential Venture.
Finding the right expert
Four themes today: robotics in Texas and the US, AI and machine learning in Texas, and defense technology in Texas.
Who co-invests with whom
Company profiles show who co-invests in each investor's companies, and plain-language graph questions are open to invited users. Named investors appear on about one Texas company in five, so the answers are still thin.
Portfolio questions
Each investor's record lists its linked companies and its fund filings. Round-by-round participation is not in the data yet.
Thesis discovery
Filters, categories, and similar companies on most profiles. Market maps and trends over time are not built yet.
Tracking accelerators and universities
Twenty or more program and university rosters, a spinoff pipeline and licensable inventions. Refresh is a manual step today, and there are no alerts yet.
HOW IT WORKS
From public records to one record per company.
1
Collect
One collector per public source, openly identified, never behind a login, every run logged.
2
Resolve
Sightings from different sources merge into one company, and every field keeps the list of where it came from.
3
Enrich
The company's own website is read for what it says it does, who founded it and whether it is hiring.
4
Classify
Category, stage and AI-native are assigned by rule, and every such column is marked heuristic.
5
Filter
Your filter is a short template you own. Each criterion returns pass, fail or unknown.
6
Export
The shortlist leaves as a workbook shaped for funnel triage in Lensential Venture, with every source link.
Lensential Venture does the analysis. See Lensential Venture.
EXPERT SEARCH
One question, ranked researchers, the score broken down.
Ask a plain question, such as who leads in actuators for robots. The expert search turns it into research topics, searches paper abstracts and ranks researchers, showing every part of the score. Each comes with papers, inventions, grants, any company on record and a link to a public page.
A lower rank means less evidence in these sources, not less expertise.
SOURCES AND RULES
Public records, with the source on every fact.
Built from
- SEC Form D filings, five years, every US state
- SBIR and STTR awards, and federal research contracts
- Accelerator, incubator and fund portfolio pages
- University spinoff rosters and technology-licensing portals
- Company websites and public job boards
- OpenAlex, NSF and NIH records for research
Kept as rules
- No LinkedIn, Crunchbase or PitchBook data.
- No email addresses or phone numbers stored or shown.
- No personal traits inferred: no gender, age, ethnicity or nationality.
- Machine labels say they are heuristic, and unknown is shown as unknown.
- Failing a filter means a poor fit with the template, not a weak company.
- No recommendation for or against any investment.
PREVIEW ACCESS
Ask for full access.
Tell us who you are and what you would ask it first.
Guests can try the preview now, with a daily limit; plain-language graph questions are open to invited users. It is a demo, not a finished product. Our privacy policy says how we use what you send.