Movokai
Investor data rooms that check themselves for contradictions
For: Founders raising capital in Germany, Austria and Switzerland, and the advisers who carry several raising clients: fractional CFOs, startup lawyers.
Founders preparing a raise produce a stack of paperwork: cap table, term sheet, founder agreement, option plan, financial model, pitch deck. Almost none of it is checked against itself. A share count that does not add up or a runway claim the model does not support is the kind of contradiction diligence finds, and it can sink a round.
Movokai generates the full data room from a resumable interview instead of a long form. Nothing is built until the founder confirms each fact. Every figure is computed from one source, so when the pre-money valuation changes, price per share, post-money, ownership and the financial model all follow.
The core of the product is the check. A fixed set of 13 named rules compares documents with each other. HARD findings are arithmetic contradictions; REVIEW findings need a human to decide which figure is right. A rule that lacks input is reported as not evaluated, never as a pass. The result can be exported with a consistency report to hand to investors.
It is built for founders and advisers raising in Germany, Austria and Switzerland, including fractional CFOs and startup lawyers who handle several clients. Movokai is clear about its limits: it does not yet check public registries, cannot read scanned documents and does not give legal advice.
18
documents in a generated data room
13
published consistency rules
€49/mo
Starter plan, one company
What it does
18-document investor data room generated from a guided, resumable interview
Human confirmation gate: no document is generated until each asserted fact is confirmed
Single-variable propagation: valuation, price per share, cap table and financial model recompute from one input
13 published deterministic cross-document rules (6 HARD, 7 REVIEW), each reported as fired, passed or not evaluated
Consistency certificate with ruleset version and integrity hash, exportable with the full room
Fact provenance: values extracted from uploaded documents must match a verbatim quote found in the source
Adviser workspaces: invite editors and viewers per company, with every confirmation recorded against a named person
Plans priced per company, every plan running the full ruleset
How we architected it
- Next.js 15
- React 19
- TypeScript
- Supabase (Postgres, Auth, Storage, RLS)
- Anthropic Claude
- Stripe
- pdf-lib
- JSZip
- Vercel
- 01
Next.js web application (auth, company workspaces, interview, confirmation gate, generate, room viewer, export) deployed on Vercel
- 02
Supabase in the EU region for Postgres, authentication and storage; tenants isolated with row-level security, service-role access kept server-side for storage only
- 03
Append-only schema: confirmed facts, findings and certificates are insert-only, enforced by database triggers rather than application policy
- 04
A deterministic data-room harness generates the documents, and a separate financial engine computes the model, cap table and term-sheet figures from one input object
- 05
A language model (Anthropic) is used only at the natural-language edge, turning interview answers into structured fields; it never computes numbers, compares documents or assigns severity
- 06
Certificate module issues a versioned record of every rule's outcome, hashed so edits after issuance are detectable
- 07
Stripe handles subscriptions; exports are packaged as PDF and ZIP
Want something built like this?
Every build runs the same five gates: spec, architect, build, review, ship. It starts with a 48-hour audit.