AI VC Benchmark: 11 AI Agents on Venture Capital Tasks
Partnering with early stage VCs, we converted two analyst workflows into benchmarks with human-verified ground truth and scored 11 AI agents on them. See the tasks, results and the scoring method:
Venture capital benchmark results
Each of the 11 models ran each task once. Scores are out of 100. Kimi K3 produced no scorable deal-sourcing run and is recorded as 0.
Both tasks require access to fresh data and reasoning skills:
- Deal sourcing: VCs have specific criteria and our partners wanted to identify early-stage AI companies built by founders from a specific country
- Competitor identification during due diligence
Diaspora deal sourcing results
Most of the field cleared half the points and landed in a broad middle band, with a wide gap between the leader and the tail. The rubric explains the shape. A row earns points when every criterion carries evidence, so an agent scores at the pace it can verify founder origin, founding year, and funding, not at the pace it can list plausible names. The qualifying companies were founded within the previous 18 months, which puts them past the reach of training data, so the spread reflects search depth and verification discipline rather than memorized knowledge. Even the leading agent left a substantial share of the points on the table, meaning full recall of a fresh, evidence-gated gold set remains out of reach for current agents working under a time budget.
Competitor mapping results
The distribution is split into three tiers, each mapped to a scoring mechanism. The top pair covered several of the target’s competitor categories and caught part of the close-peer group. The middle band is what the rubric produces when an agent maps the well-known analyst and review names cleanly and misses the small, recent peers nearest to the target: solid execution, penalized twice through lost recall points and flat miss penalties. The near-zero tail is the most instructive, because a score that low does not require an empty file. Penalties for missed peers and for returned non-competitors can cancel everything a tidy CSV earns, so a confident, well-formatted map of the wrong companies ends up worth the same as no map at all.
Why results of the two tasks diverge
Sourcing rewards mechanical verification of five stated criteria; competitor mapping demands a judgment about where a niche research firm’s market ends, with no criteria list to check against. That judgment separated the field far more than formatting or speed did, and it is the reason the leader changed between columns while several agents swapped positions sharply. Strength in one research workflow says little about the next one, so teams selecting an agent should test it on the workflow they plan to automate rather than relying on a single leaderboard.
Venture capital benchmark methodology
Each task ships as a spec file we authored, which fixes the analyst role, the output file, the tool access, and the time budget before any run starts. The table summarizes those specs:
The time limit caps each run: an agent must research, verify, and deliver its CSV within that window, and deal sourcing gets the longer budget because it verifies dozens of candidate companies against five criteria while competitor mapping investigates a single target.
Both place the agent in a role a junior analyst holds at a venture capital fund, and both require a CSV with atomic cells, resolving source URLs, and no commentary. All facts are judged as of a frozen snapshot date, and gold sets are re-verified quarterly because funding data drifts.
Task 1: Diaspora deal sourcing
The agent acts as a deal-sourcing analyst at an early-stage fund focused on diaspora founders. It must return AI companies that meet all of these investment criteria as of the snapshot date:
- Funding below $5M. Total disclosed capital raised, not a valuation or revenue figure. If funding is undisclosed, the company qualifies if positive evidence shows the amount falls below the threshold. Otherwise, it must be excluded.
- Founded in 2025 or 2026. Anything earlier fails.
- Founder origin with public evidence. At least one founder was born in a specific country, educated at an institution in this country, or born to a specific nationality.
- Headquartered abroad. The company itself sits outside Türkiye.
- AI-focused. Infrastructure-layer and application-layer companies both qualify.
Each row pairs every figure with a source URL the agent opened. A fabricated or unreachable URL invalidates the row it supports.
How it is scored
Scoring runs against a hidden ground truth: a gold set of verified qualifying companies the model never sees. Four dimensions apply:
- Recall against a verified gold set of qualifying companies.
- Precision against a trap set of near-misses, each failing exactly one criterion. Named disqualifiers carry fixed penalties.
- Data accuracy on a fixed random sample of rows drawn with a recorded seed, so a scoring pass can be reproduced: headquarters, founding year, funding within tolerance, and sources that support the claim beside them.
- Format compliance: column order, atomic cells, numeric funding, valid CSV.
Padding a list with plausible-but-unverified companies loses more in precision than it gains in recall. The design rewards fewer, fully verified rows.
Task 2: Competitor mapping
The agent conducts commercial due diligence on a target company for a potential investor. For the scored run, we pointed the agents at our own company: the target is AIMultiple itself, which let us verify the ground truth against first-hand knowledge of who competes with us. The task: identify the target’s direct competitors, evidence each with public data, and return them as a CSV document.
The scope rules define what counts as a competitor:
- Substitutability. A company qualifies when it offers a substitutable product or service to overlapping customers, in any category where the target competes.
- Full category coverage. The target operates across multiple categories. The agent must cover competitors in every one of them, not the most obvious one alone.
- Exclusions. Companies the target writes about, partners with, or lists, but does not compete with. Adjacent players in a neighboring category. The target itself.
A thorough map for a target of this breadth runs 8 to 15 competitors. Each row includes the competitor’s LinkedIn page, total disclosed funding (or Undisclosed), headquarters country, founding year, and LinkedIn headcount, with a source URL beside each metric. Two reasoning columns sit alongside the metrics: a differentiator versus the target and a rationale for inclusion, each required to be specific rather than generic. A funding figure must be backed by a funding source.
How it is scored
Scoring runs against a hidden ground truth, organized by competitor category. Five dimensions apply:
- Precision. Every returned non-competitor reduces the score. Named disqualifiers, such as SEO tools and the AI model vendors, that the target merely benchmarks carry fixed penalties. A soft anti-padding guard reduces excess weight when a single category accounts for most of the found set. Competitors carry two weight levels. A small set of must-not-miss peers, the firms closest to the target’s core work, weigh three times a standard entry. A genuine competitor absent from the ground truth still earns credit after expert review, so an incomplete gold set does not punish a correct find.
- Graduated from penalties. Independent of recall weight, each must-not-miss peer the agent fails to return subtracts a flat penalty sized by closeness. Missing the target’s nearest peers is treated as a core due diligence failure, not a routine recall gap.
- Funding accuracy. Sampled rows checked within tolerance, with Undisclosed and N/A (public) accepted as correct answers where they apply.
- Metric evidence and reasoning. Required metrics present, atomic, sourced, and accurate within tolerance. The differentiator and rationale cells are scored for specificity against boilerplate.
Why the criteria resist pattern matching
Both tasks are built to block the shortcuts agents take under time pressure:
- Guessing fails. Where a figure is not public, the scored answer is an evidenced gap, not an estimate. An invented number costs points twice, once on accuracy and once on precision, so admitting the unknown outperforms filling it.
- The answers sit past the first search page. Both gold sets lean on young or thinly documented companies that training data has not caught up with, so scores depend on live, layered search rather than recall.
- Evidence beats inference. A plausible signal, a familiar-sounding name or a high-ranking page, does not establish a fact; a citable record does. Both rubrics score the source next to the claim, not the claim by itself.
- Completeness and precision pull against each other. Padding with unverified names costs more than it earns, while an overly cautious short list loses recall. The scoring pushes agents toward the balance a fund expects from an analyst: every finding verified, no genuine finding skipped.
Building the tasks
Each benchmark began as a real workflow within our business development and research operations. We converted original analyst specs into task files with a fixed structure: role, eligibility rules, exact output columns, tool access, and a time limit. Rubrics live in separate files with per-criterion points: format and arithmetic run through automated checks, and judgment calls go to a human reviewer.
Three design rules carried across both tasks:
- Every figure needs a source the agent opened. Bare homepages and search-result pages do not count.
- Undisclosed beats invented. Writing Undisclosed for a private pre-seed round scores; inventing a number costs points twice, once for accuracy and once for precision.
- Atomic output. One value per cell, exact column order. Structure violations cost points before content is judged.
What the rubrics measure
The rubrics share a scoring philosophy across both tasks:
- Recall against verified ground truth. Each qualifying company or competitor in the gold set was confirmed by a human researcher against public records before any model was scored.
- Precision with named traps. Both ground truth sets carry near-misses that fail exactly one criterion and are named disqualifiers with fixed penalties, so pattern matching without verification gets caught.
- Negative points for fabrication. A hallucinated funding figure or a dead source URL subtracts points instead of scoring zero. Honest gaps beat confident errors.
- Drift management. Funding, valuations, and headcounts change. Each rubric carries a snapshot date, marks drift-prone data, and requires re-verification before every scoring pass.
- Mixed checking. Recall, precision, and format checks run automatically. Founder-origin evidence and inclusion rationales go to a human reviewer.
Low scores come from making the task genuinely hard, not from crushing good answers with caps. Recall, precision, accuracy, and honesty each pull on a different failure mode, so a model cannot compensate for weak research with confident formatting.
Add as preferred sourceWhy venture capital work tests AI agents well
Venture capital firms run on analyst labor. Deal flow screening and due diligence are multi-step research tasks. Each one requires web research, source verification, and structured output. Each one also produces plausible-looking wrong answers, which makes them hard to fake and useful to score.
These properties make the VC analyst work a strong benchmark domain:
- Verifiable ground truth. Funding rounds, founding years, and headcounts can be checked against public records.
- Fabrication pressure. Models that cannot find a figure tend to invent one. The tasks expose this directly.
- Real economic value. A fund that automates parts of due diligence changes its own cost structure. The benchmark measures work someone pays for today.
Further readings
- AIM Agentic Marketing Benchmark
- AI-Based Stock Trading: Which Gen AI Tool Is Better
- Agentic AI Finance Benchmark
FAQs
No. The tasks map to work junior analysts do: sourcing, competitor mapping, comps. Investment decisions, partner judgment, and founder relationships sit outside their scope. AI compresses research time; humans make the decision.
Public companies have audited financials, which makes valuation a lookup exercise. Private early-stage startups force the agent to estimate, state a basis, and label uncertainty. That is where fabrication pressure is highest and where real due diligence effort is put in.
Do these benchmarks show AI replacing venture capital analysts?
No. The tasks map to work junior analysts do: sourcing, competitor mapping, comps. Investment decisions, partner judgment, and founder relationships sit outside their scope. AI compresses research time; humans make the decision.
Why do the tasks target private companies?
Public companies have audited financials, which makes valuation a lookup exercise. Private early-stage startups force the agent to estimate, state a basis, and label uncertainty. That is where fabrication pressure is highest and where real due diligence effort is put in.
Cite this research
Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.
@misc{phd2026,
author = {PhD., Ezgi Arslan, and Kalelioğlu, Berk},
title = {{AI VC Benchmark: 11 AI Agents on Venture Capital Tasks}},
year = {2026},
month = jul,
howpublished = {\url{https://aimultiple.com/ai-vc}},
note = {AIMultiple. Retrieved July 21, 2026}
}
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