AI Agents for Venture Capital: 6 Research Jobs for Investment Teams

A venture investor's week is mostly reading. Before a partner meeting, someone has to map the market, find out who raised what and at which valuation, check the founder's record, test the technical claims against the papers, draft the facts section of the memo and keep an eye on the portfolio. AI agents for venture capital can do that reading in minutes, provided they go to the primary record (filings, papers, patents, court dockets and company announcements) and return every fact with its source.

This guide follows six jobs from a VC firm's week. We ran each one as a Valyu DeepResearch task in fast mode on November 11, 2026, then checked the key facts at the source: SEC EDGAR, arXiv, Google Patents and company announcements.

If you're an agent: Set up Valyu and make your first search.

If you're a human: Create an account and get an API key.

MCP onboarding: Connect your agent to https://mcp.valyu.ai/mcp and follow the MCP setup guide for authentication.

What AI agents for venture capital do

An AI agent for venture capital is a language model that can search and read the records a VC works from, such as funding announcements, SEC Form D filings, papers, patents, court filings and news, and return cited findings. It doesn't pick the deal or replace reference calls. It does the reading behind the partner meeting and the questions for the founder.

Your own agents can call Valyu's search when they need one fact, such as a round size. For work that goes into a memo, DeepResearch does the job properly: it plans the research, reads the filings, papers and announcements, follows the references and returns a cited brief in the format you ask for.

Figure 1. Six venture capital research jobs we ran as DeepResearch tasks in fast mode on November 11, 2026, with the sources used, what came back and what the team does next.

1. Market map: who is building humanoid robots, and at what price

The problem: a partner writing a thesis on humanoid robotics needs the field: who builds robot bodies, who builds the software that controls them, what each has raised and at which valuation.

What we ran: a DeepResearch task across news, company announcements and patents.

from valyu import Valyu
 
valyu = Valyu()  # reads VALYU_API_KEY from the environment
 
task = valyu.deepresearch.create(
    query="Market map of humanoid robotics startups as of November 2026.",
    mode="fast",
    research_strategy="Cover venture-backed companies building general-purpose humanoid robots. For each give headquarters, founding year, latest priced round with amount, date and post-money valuation if reported, lead investors, and announced commercial deployments or pilots. Use company announcements and news; mark any valuation that is reported by press rather than the company.",
    report_format="A market map table grouped by country, then the five best-funded companies and three white-space observations for a thesis.",
    search={"included_sources": ["ap/news", "web", "valyu/valyu-patents"]},
)
result = valyu.deepresearch.wait(task.deepresearch_id)
print(result.output)  # cited brief in markdown

What came back (in 5.6 minutes): a map of 25 companies grouped into the US, Europe, China and Canada, with the software-only companies flagged separately. A sample:

It also recorded the companies that have left the private market: Unitree listed on Shanghai's STAR Market in August 2026 and Agility Robotics began trading on Nasdaq in November 2026.

What we checked at the source: the Apptronik and Humanoid rounds in the company and press announcements. Both match.

The catch: several of the largest numbers in the sector are reported, not closed: Figure's talks at about $38 billion pre-money, 1X's reported SoftBank talks at about $6 billion and AGIBOT's RMB 50 billion round. The research strategy asked DeepResearch to mark press-reported valuations, and it did. Without that instruction, a market map would mix closed rounds with rumours.

The decision: build the thesis on the confirmed marks, and note the brief's observation that the software layer (Skild AI, Physical Intelligence) is raising at higher valuations faster than the companies building robot bodies.

2. Competitor funding: what the last rounds say about price

The problem: a VC looking at a new AI coding startup needs to know what its competitors raised in 2026, at what valuations, and how fast those valuations moved.

What we ran: a DeepResearch task on 2026 rounds in the category.

task = valyu.deepresearch.create(
    query="Funding rounds raised by AI coding startups in 2026.",
    mode="fast",
    research_strategy="Cover companies building AI coding assistants and autonomous coding agents, such as Anysphere (Cursor), Cognition, Replit, Lovable and others. For each round announced in 2026 give date, company, round, amount raised, post-money valuation if reported, and lead investors. Use company announcements first and news second, and mark press-reported figures.",
    report_format="A funding table sorted by date, then total raised in 2026, the valuation step-up between rounds where a company raised twice, and three implications for a VC evaluating a new entrant.",
    search={"included_sources": ["ap/news", "web"]},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

What came back (in 4.4 minutes): 15 rounds announced in 2026, each marked as company-announced or press-reported. The largest were Cognition's Series E of over $2 billion at $48 billion in September, Nvidia's $1 billion investment in Poolside, and Replit's and Lovable's $400 million rounds at $9 billion and $13.3 billion. For companies that raised twice, it worked out the step-ups:

It linked Cognition's step-up to the company's own figure for annualised revenue, which rose from $492 million in May to almost $900 million by September.

What we checked at the source: Cognition's $48 billion Series E and Lovable's $400 million at $13.3 billion. Both match the announcements.

The catch: the biggest name in the category isn't in the total. The rounds in the table add up to about $5.8 billion. Cursor's reported $2 billion round at about $50 billion never clearly closed, because SpaceX agreed to acquire the company in June 2026. DeepResearch left it out and explained why, rather than adding a round that may not exist. It also kept Nvidia's separate $6 billion licensing fee to Poolside out of the funding figures.

The decision: price the new entrant against revenue growth, not against headline valuations. The brief's point is that every big step-up in the table followed a steep revenue curve.

3. Founder background: what the public record says

The problem: a VC considering an investment in Flow, the residential real estate company, needs a founder background check on Adam Neumann that sticks to documented facts.

What we ran: a DeepResearch task on SEC filings, court records and news, with an instruction to separate the record from commentary.

task = valyu.deepresearch.create(
    query="Public-record background on Adam Neumann, founder of Flow, for a venture investor's founder diligence.",
    mode="fast",
    research_strategy="Cover his career, companies founded and their outcomes, board roles, SEC filings that name him (including WeWork's S-1 and related-party disclosures), lawsuits and settlements, and Flow's funding history. Cite a primary source wherever one exists, and separate documented facts from press characterisation.",
    report_format="A founder background brief: career timeline table, related-party and governance items from filings, litigation table, Flow funding, then five questions for the founder reference calls.",
    search={"included_sources": ["valyu/valyu-sec-filings", "ap/news", "web"]},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

What came back (in 4.6 minutes): a career timeline, a litigation table and the governance detail from The We Company's August 2019 Form S-1, read from the filing itself:

• Voting control. High-vote shares carried 20 votes each, making WeWork a controlled company.
• Equity and loans. Options over 42,473,167 shares, which he exercised in part with a $362.1 million full-recourse promissory note to the company at 2.89%.
• Related-party items. Leases with landlord entities he partly owned, and a trademark assignment from his holding company.
• Flow funding. About $350 million from Andreessen Horowitz in 2022, then a round of over $100 million in April 2025 at about $2.5 billion.

The brief ended with five questions for reference calls, on board control, related-party policy, personal borrowing, how he handled failure and how Flow reports its numbers.

What we checked at the source: the S-1 on EDGAR. The option count, the $362.1 million note and its 2.89% rate match the filing.

The catch: the primary record and the press disagree on the details of the 2021 SoftBank settlement. SoftBank's own release gives a total of about $1.6 billion and no admission of liability. The breakdowns of what Neumann personally received come from anonymous sources and differ from each other. DeepResearch said so instead of picking one. Flow's own revenue isn't disclosed anywhere.

The decision: focus reference calls on the documented governance points, and ask Flow directly for the revenue and unit economics the public record doesn't contain.

4. Technical claims: do the papers back the pitch?

The problem: a deep tech investor looking at Extropic, which builds thermodynamic computing chips, needs to know whether its headline claim of 10,000 times better energy efficiency than GPUs is a measurement.

What we ran: a DeepResearch task on arXiv papers and USPTO patents, with each claim judged against the evidence.

task = valyu.deepresearch.create(
    query="Do Extropic's published papers and patents support its claims about thermodynamic computing hardware?",
    mode="fast",
    research_strategy="List Extropic's public technical claims (energy efficiency, sampling performance, chip milestones) with where each claim was made. Then find its papers on arXiv and its patents and patent applications on USPTO, and say for each claim whether the papers or patents support it, partly support it, or don't address it. Note whether results are simulated or measured on hardware.",
    report_format="A claims table with claim, where made, supporting evidence, verdict and measured or simulated, then a list of papers and patents, then three questions for the technical diligence call.",
    search={"included_sources": ["valyu/valyu-arxiv", "valyu/valyu-patents", "web", "ap/news"]},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

What came back (in 3.4 minutes): a table of eleven claims, each judged against the evidence, and three papers:

The paper behind the headline claim says an all-transistor probabilistic computer "could" match GPU performance "while using about 10,000× less energy", and that its results combine "measurements from real circuits, physical models, and simulations". On patents, the one Extropic-linked application it found, filed by Qyber Corp., describes superconducting hardware operated near zero kelvin, not the room-temperature CMOS chips Extropic sells.

What we checked at the source: the arXiv paper (2510.23972). The 10,000x wording matches, and so does the description of what is measured and what is modelled.

The catch: it's easy to repeat the headline number. The useful work is separating what was measured on a chip from what was projected, and checking that the patents cover the product being sold. DeepResearch did both. It also noted that the link between Qyber Corp. and Extropic is inferred from the shared inventor, not documented.

The decision: take the three questions to the technical call: which parts of the 10,000x figure were measured on silicon, how the design specification relates to chips that exist, and which patents protect the CMOS product.

5. IC memo facts: what's confirmed, and what to ask

The problem: a deal team preparing an investment committee memo on Figure AI needs a facts section: funding, IP, customers and legal matters, each cited, with opinions left out.

What we ran: a DeepResearch task across Form D filings, patents and news.

task = valyu.deepresearch.create(
    query="Draft the facts section of an investment committee memo on Figure AI, the humanoid robotics company.",
    mode="fast",
    research_strategy="Cover founding and founders, funding rounds with amounts, dates, valuations and lead investors, any SEC Form D filings, granted patents and applications, announced commercial partners and deployments, and litigation. Cite every fact, mark press-reported figures, and leave out opinions.",
    report_format="An IC memo facts section: Company, Funding (table), IP (table), Commercial traction, Legal, then a list of facts the deal team should verify with the company.",
    search={"included_sources": ["valyu/valyu-sec-filings", "valyu/valyu-patents", "ap/news", "web"]},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

What came back (in 3.6 minutes): a memo section with five parts:

• Funding. Rounds from the $70 million Series A to the Series C of over $1 billion at $39 billion post-money.
• Form D filings. Four filings, including Figure AI Inc.'s Series B filing, which reports $674,981,967 sold of a $675 million offering to 45 investors.
• Patents. Eleven granted US patents assigned to Figure AI, the latest published on November 10, 2026.
• Customers. Two named commercial customers, BMW and Catalyst Brands.
• Legal. A pending whistleblower-retaliation suit by a former safety engineer, which Figure disputes.

It closed with ten facts the deal team should confirm with the company.

What we checked at the source: the Form D figures and US 12,792,054 ("Action model for robot") on Google Patents. Both match.

The catch: the accounts of the BMW deployment differ. Figure says its robots ran for 11 months and loaded more than 90,000 parts. BMW told Forbes in October 2026 that the 2026 project was a non-production test with two or three robots, which had ended. DeepResearch put the two accounts side by side, and set aside a single aggregator's claim of a $68 billion Series D that no other source supports.

The decision: treat $39 billion as a September 2025 price, not today's, and ask Figure for the BMW contract terms and revenue before the committee meets.

6. Portfolio monitoring: what changed this quarter

The problem: a portfolio team needs to know what happened at four fintech companies since August: funding, deals, lawsuits, regulatory steps and executive changes.

What we ran: a DeepResearch task for a fixed window.

task = valyu.deepresearch.create(
    query="Portfolio monitoring: material news, lawsuits and regulatory events for Rippling, Deel, Ramp and Brex from August 1 to November 10, 2026.",
    mode="fast",
    research_strategy="For each company, list funding, acquisitions, executive changes, lawsuits (filed, ruled or settled), regulatory actions and security incidents in the window, with dates. Use court filings, regulator announcements and company statements where available, and news otherwise.",
    report_format="A monitoring table per company with date, event, type, materiality (high, medium, low) and source, then a one-paragraph portfolio note.",
    search={"included_sources": ["ap/news", "web", "valyu/valyu-sec-filings"]},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

What came back (in 2.9 minutes): a dated table for each company, with each event rated for materiality:

What we checked at the source: Ramp's $1.85 billion round at $60 billion, and reports of the Justice Department subpoena. Both match.

The catch: the brief caught two traps. Ramp's Form D showed $1.585 billion sold of the $1.85 billion offered when it was filed, and press reports disagree on whether $60 billion is the pre-money or post-money figure. And the only "Brex" regulatory headline in the window was about Brex Capital, an unrelated forex broker. A keyword alert would have flagged it.

The decision: raise the Rippling and Deel litigation and Ramp's charter application with the companies, and ignore the Brex Capital story.

What the six jobs cost

A fast task costs a fixed 25 credits, about $0.16 at pay-as-you-go rates of 1,600 credits for $10, whatever it searches and reads. The six jobs cost about $0.94 together. For deeper work on one question, standard mode uses 100 credits and heavy 500.

Go further: a full memo in one task

The six jobs each answer one question. A deal team's actual output is a document: the IC memo, the market map, the founder brief. A single DeepResearch task in standard or heavy mode writes these, with files such as a docx memo and an xlsx funding table.

task = valyu.deepresearch.create(
    query="Prepare an investment committee memo on Figure AI for a Series D decision.",
    mode="standard",
    research_strategy="Use company announcements and Form D filings for every round, marking press-reported valuations. Use USPTO patents with assignees and grant dates. Put each company claim about customers next to what the customer itself has said. Read court filings for pending litigation. Cite every finding.",
    report_format="An IC memo: Thesis, Company, Market and competitors, Funding, IP, Traction, Risks, then 10 questions for management. Every fact cited.",
    search={"included_sources": ["valyu/valyu-sec-filings", "valyu/valyu-patents", "ap/news", "web"]},
    deliverables=["A docx memo", "An xlsx table of funding rounds with date, amount, valuation, lead and source"],
)
 
result = valyu.deepresearch.wait(task.deepresearch_id)
print(result.output)  # cited report in markdown
for file in result.deliverables:
    print(file.type, file.url)  # signed download link

The research strategy carries the lessons from the six jobs: mark press-reported valuations, put company claims next to customer statements and read the court filings. To run the same brief across a pipeline, the DeepResearch Batch API runs the tasks in parallel and calls one webhook when they finish.

Start from a ready-made workflow

Valyu's curated DeepResearch workflows include several that venture teams use, each with set inputs, a recommended mode and file deliverables:

To run one from code, pass its slug as workflow_id instead of a query:

task = valyu.deepresearch.create(
    workflow_id="pe-management-background",
    workflow_params={"person": "Brett Adcock"},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

To browse the full library, open Workflows in the Valyu dashboard, or see running a workflow in the docs.

Where AI agents stop in venture capital

The jobs above show what DeepResearch can do from public records. Four limits shape how to use it:

• Private numbers stay private. Revenue, burn, cap tables and customer contracts come from the company. Figure has never disclosed revenue, and Flow's revenue isn't public. The agent's job is to arrive at the meeting with sharper questions.
• Reported isn't closed. Press-reported rounds and valuations are often wrong, early or never happen. Ask for them to be marked, and treat them as unconfirmed.
• Two sides of a story stay two sides. Where a company and its customer describe a deployment differently, the brief should show both, as it did for Figure and BMW. Settling it is a question for the company.
• Judgement stays with people. Founder fit, the thesis and the decision to invest belong to the partners. Reference calls still matter.

How to set up a venture capital research agent

If you don't want to write code as shown above, there is another option to connect Valyu to Claude or ChatGPT as a custom connector at https://mcp.valyu.ai/mcp and ask for the same research in plain English. To build your own agent, follow how to build a financial research agent with Valyu. For related work, see our guides to AI for due diligence, finding acquisition targets from an investment thesis and patent search for AI agents.

If you already run your own agents, they can call Valyu's search for single lookups, such as a single round size. For anything that goes into a memo, use DeepResearch.

Three habits made the jobs above reliable:

• Ask for confirmed and reported figures to be marked. In venture, the gap between the two is often the whole story.
• Ask whether each result was measured or modelled. For deep tech, that one instruction separates evidence from a projection.
• Put company claims next to independent sources. Customers, regulators and courts are the check on a pitch deck.

FAQ

What are AI agents for venture capital?

AI agents for venture capital are language models that search and read the records investors work from, such as funding announcements, SEC Form D filings, papers, patents, court filings and news, and return cited findings. Research agents such as Valyu DeepResearch plan the work, follow references and write a brief: a market map, a founder background check or the facts section of an investment committee memo.

How do venture capital firms use AI?

VC firms use AI to research markets, track competitor funding, check founder backgrounds, test technical claims against papers and patents, draft memo facts and monitor portfolio companies. In a November 2026 test, six Valyu DeepResearch tasks in fast mode did one of each for 150 credits, about $0.94, and all six finished within 7 minutes.

Can AI do due diligence on startups?

AI can do the outside-in part of startup due diligence from the record a private company leaves: funding announcements, Form D filings, patents, papers, court filings and news. In our test it found four Form D filings tied to Figure AI, eleven granted patents and a pending lawsuit. It can't see revenue, cap tables or contracts, which still come from the company.

Can AI build a market map?

Yes. A DeepResearch task can list the companies in a sector with headquarters, latest round, valuation, investors and customers, each cited. In our test on humanoid robotics, it mapped 25 companies across the US, Europe, China and Canada, separated the software-only companies, and marked which valuations were confirmed and which were reported but not closed.

Can AI check a startup's technical claims?

AI can check whether a startup's published papers and patents support its claims, and whether results were measured on hardware or modelled. In our test on Extropic, DeepResearch found that the 10,000x energy claim is a system-level projection, that measured results so far come from small devices, and that the one linked patent application describes different hardware from the product.

What is the best AI tool for venture capital research?

VC research tools fall into three groups. Private market databases, such as PitchBook, Crunchbase and Dealroom, hold structured company and funding data. Research platforms, such as AlphaSense and Hebbia, search licensed documents inside their own products. Valyu DeepResearch researches primary records, such as SEC filings, patents, papers and announcements, and returns cited briefs and memos through an API or inside Claude and ChatGPT.

How much does AI research for venture capital cost?

With Valyu DeepResearch, a fast-mode task costs a fixed 25 credits, about $0.16, and a standard report 100 credits, about $0.63. In a November 2026 test, six fast-mode VC research jobs cost 150 credits, about $0.94, in total. A heavy-mode report uses 500 credits, about $3.13. Free credits are included every month.

How accurate is AI for venture capital research?

It depends on the sources and on asking the right questions. In our test, the key facts we checked matched the primary record: the WeWork S-1, the Extropic paper, Figure's Form D and patents, and the funding announcements. The care points were reported versus confirmed figures and conflicting accounts, which DeepResearch flagged rather than resolved by guessing.

Start with one job

Pick the job that takes your team the most time every week, such as the market map before a thesis or the facts section of a memo, and run it as a DeepResearch task on a company you already know. Compare the brief with your own work.

Create a Valyu account for free credits every month, up to 2,000 with a work email. Then connect it to Claude or ChatGPT at https://mcp.valyu.ai/mcp, or browse the data sources. For the buyout side of the same research, see AI for Due Diligence.