Fund-accurate AI for venture capital
Your team already uses AI. Nucleus makes it fund-aware and verifiable — so every TVPI, MOIC, and DPI that reaches an LP is cited to the source page, not confidently wrong.
A confident wrong number is a trust event
A partner asks the team's AI for Fund II's TVPI before an LP call. The answer comes back instantly, confidently — and it's Fund I's number. It's plausible, so nobody catches it. The LP does. Across 80+ investments, decks, returns models, and a data room, vintages bleed together and figures drift off by a decimal. Generic AI tools cap out at ~20 documents, can't tell a Q1-24 report from the live returns model, and can't show you how they reached the number.
See how Nucleus catches AI hallucinations
Chat with any AI provider. Nucleus saves every conversation and verifies it against your knowledge base.
How VC teams use Nucleus
LP due diligence & DDQ responses
An LP sends a 60-field DDQ. Nucleus drafts every answer from your past submissions and data room — first draft in minutes instead of three days, each one cited to the source.
Fund-accurate answers
Ask for Fund II's TVPI and get Fund II's number, never Fund I's. Every figure is checked against that fund's own documents before it reaches an LP.
Quarterly LP reporting
Revenue, runway, and headcount pulled from your returns models into your existing template — the quarter-over-quarter narrative written for you, every number cited.
Deal screening & thesis fit
Every inbound deck scored against your real thesis, pass/invest history, and portfolio overlaps — so 100% of deal flow gets a first pass, not a generic summary.
Follow-on & exit decisions
One question reasons across the returns model, board decks, and the LPA — and surfaces the LP email and prior IC discussion you'd have forgotten — every input cited.
Fund & legal documents
Ask which LPs have MFN provisions and get a clause-level answer cited to the executed version. Misquoted or fabricated clauses are caught before a negotiation.
Pick your workflow
Connect your fund’s documents once. Then every job below runs inside the AI you already use — every figure cited back to your sources, and an honest refusal when the source isn’t there.
This is your data doing this.
Two to three days. Five to ten of your documents. Your rubric, your accuracy bar. Nothing leaves your tenancy.
Run it on your documents →Ready to ground your AI in truth?
Join teams using Nucleus to eliminate hallucinations and build AI systems they can trust.