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AI due diligence with a source behind every finding

Data rooms read by agents, findings judged by experts

For Mapline.AI we built an agent that cut real estate due diligence from weeks to minutes, and every finding cites the page it came from.

Book a free consultation How we build agents
deal-review

  1. Deal leadYour team

    The seller uploaded the data room for the warehouse site. Can the review agent check zoning and leases?

  2. Review agentAgent

    1. dataroom.list_files leases, permits and surveys
    2. parser.read_layout tables and scanned exhibits
    3. search.retrieve filtered by site and type
    4. gis.get_parcel zoning, wetlands and roads

    Draft report ready. One lease allows a use the current zoning does not, so it needs your review.

    Needs a personLease use conflicts with zoningFinding in the warehouse site report
    Approved by Deal lead

  3. Review agentAgent

    1. report.share findings with cited pages

    Report shared with the deal team. Each finding links to the page it came from.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

Due diligence means weeks of reading before a decision.

Agents read the documents and draft the findings, and your experts decide what they mean.

  1. 01

    Where it stallsData rooms hold piles of mixed, messy documents.

    What we do Agents parse and classify every file, scanned exhibits and tables included, and index them by topic.

  2. 02

    Where it stallsReviewers miss what sits in a different file.

    What we do An agent cross-checks facts across documents and flags conflicts, such as a lease that breaks zoning.

  3. 03

    Where it stallsFindings are hard to trace back to a source.

    What we do Every finding cites its page, and retrieval is filtered by asset and document type.

What we build

What we build for due diligence

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Reviews we support

  • M&A targets
  • Real estate deals
  • Vendor onboarding
  • Investment memos
  • Lending files
  • Regulatory filings
  • Data room documents
  • Contract portfolios

How we build it

  • Parsing for data rooms and scans

    Parsing for tables, exhibits and scans, chosen on your hardest documents.

  • Retrieval filtered by asset and type

    Search limited to the right asset, location and document type, then re-ranked by source.

  • Data room and records connections

    Read access to your data room, GIS data and public records, with the permissions you set.

  • Review rules, traces and encryption

    Risk findings stop for an expert, sensitive data is encrypted and every step is traced.

Not seeing your case? Bring the data room or file set that slows you down and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

AI due diligence, answered

What is AI due diligence? +
AI due diligence uses agents to read the documents behind a deal or an asset and draft the findings. They parse leases, permits, contracts and filings, pull structured facts such as zoning or key dates, cross-check them and write report sections with a source for each point. Your experts review the findings and decide what they mean for the deal.
Which due diligence work can AI automate first? +
Reviews you repeat on many assets with a similar checklist, such as real estate site diligence, vendor checks or contract review in a data room. The checklist becomes the structure of the agent's report. Start with one asset type, test the agent on reviews your team has already finished and compare its findings with theirs before it runs on a live deal.
Have you built an AI due diligence agent? +
Yes. For Mapline.AI, a US real estate startup, we built an agent that runs site due diligence across US municipalities. It uses LlamaParse Premium for difficult tables, Milvus with municipality and document-type filters, custom re-ranking and parcel data from GIS. Real estate due diligence fell from weeks to minutes, and Mapline.AI's staff update zoning data without waiting on us.
How do you stop an AI due diligence agent from inventing facts? +
The agent writes only from retrieved documents and structured data. Retrieval is filtered by asset, location and document type, so the agent works from the files for that site. Facts such as zoning or acreage come from GIS data and go into the prompt directly. Each finding cites its page, and traces from live runs show what the agent read.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
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