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Workbooks for everyday work.

Lead reports, due diligence documents and spreadsheets.

Start a workbook

First workbook free.

Customer Stories
> find 50 leads for our industrial pumps and draft an outreach email to each

6 minutes later: qualified B2B sales leads across five product lines.

And every source it cited was really where it said it was. Not most of them. Every one.

See the run →

Works inside Claude, Codex and Cursor.

Your workbooks are there in the apps you already work in: ask a question, start a report, or update a spreadsheet right where you're at.

  • Claude Code
  • Claude
  • Codex
  • Cursor
  • Hermes
  • Any MCP app

Written from primary sources and your own files.

A document is only as good as what it was written from. So a workbook goes to the primary sources that matter to you: SEC filings, FDA labels, clinical trial registries, customs records, procurement awards.

And to the files you already keep. Connect Google Drive, Dropbox, OneDrive or SharePoint, and it works from what is there.

If a source you need is missing, we add that data hose. That is our enterprise work: talk to us.

Every line cites the page or row it came from.

Google Drive
Dropbox
OneDriveOneDrive
SharePointSharePoint

How it works.

  1. 01

    Describe it. One sentence saying what the document is.

  2. 02

    It does the work. It reads the primary sources and your own files, and cites each one.

  3. 03

    Several AI models share it. Each takes the part it does best: gathering, writing, checking.

  4. 04

    It updates itself. When a source changes, the workbook changes with it and tells you what moved.

  5. 05

    You change it with a sentence. Ask in plain English and it rewrites the part you meant.

Connect your AI agent with MCP.

Connect the hedwigai MCP and your agent can ask, list, start and change workbooks. You speak in sentences; it makes the calls.

A short answer from your workbooks and programs, with the source it rests on. Nothing to wait for.

Is Moderna a biotech with a public float above $5B?

Your agent

ask · Is Moderna NASDAQ:MRNA a biotech company with public float above $5B?

{
  "answer": "yes",
  "claims": [{ "figure": "public float", "value": "$9.6B",
               "as_of": "2026-06-28" }],
  "sources": ["MRNA 10-K, cover page",
              "biotech-diligence-7fq2 · §Moderna"]
}

Yes. Moderna's public float was $9.6B as of June 28, per the cover of its 10-K.

Lead reports, due diligence and spreadsheets.

A workbook is one document with one job. You don't maintain it; it arrives researched and finished, and it keeps itself up to date.

Lead reports

Who to sell to, and why now. Customs records, procurement awards and project wins, qualified against your own criteria, with an outreach email drafted for each.

US importers of heavy engineering equipment.

Diligence documents

The pack on a company, a target or a supplier. Filings, labels, trial results and investor decks, gathered and checked, with every claim linked to its source.

Diligence on eleven biotech companies.

Spreadsheets

Trackers, budgets and models. Change an assumption in plain English; the numbers move and it tells you which ones did.

The client list nobody has updated since March.

You can check every line.

Sources on every claim

Each line traces to the filing, page or row it came from.

Every update says what changed

When a source moves, you see which lines changed and which source moved them.

Shared with your team

Invite the people you work with, and they can read the workbook and ask for changes too.

You are asked only when it matters

It stops for a decision or an approval, and otherwise gets on with the work.

Tested on real work. Every score published.

Each test is a real job, scored against a checklist written before the run started. We publish every result, including what it missed.

Where researchers are moving: traffic, discovery, users and revenue of research tools in 2026

General AI assistants are taking the searching and the writing; the tools that hold a researcher's own work keep them.

MeasureClaudeQuillBotOverleaf
Google searches, Sep 202668M5.0M3.35M
A year earlier7.5M9.1M3.35M
See where they are moving →

What people and their agents pay for: x402, Stripe and marketplace data by community

Installing is free. Following the money: what each community pays for, and what agents buy themselves.

Measurex402ApifyTrustMRR
What is paid forPer requestPer resultSubscription
Last 30 days$1.09M$1.7M paid out (Apify's figure)$157k, OpenClaw makers
Follow the money →

Which MCP servers, connectors and skills each community uses

Eleven communities, from developers to people running an agent for their own life, and the tools each plugs in.

MeasureDevelopersMarketersCreators
Most-searched tool, Aug 2026Playwright 60.5kMeta Ads 12.1kBlender 49.5k
Its searches in Jan 202690.5k72012.1k
See each community →

Reference checker: check every reference in a thesis before you submit it

Does each work exist, are its authors and year right, and does the sentence citing it say what it says.

MeasureGPT-3.5GPT-4
References that don't exist55%18%
Real references with an error43%24%
Check a draft →

The AI agent landscape: who uses which agents, where, why, and with what tools

Six groups of people, eight places agents live, and the tools they plug in, from every public measure there is.

MeasureChatGPTClaude CoworkOpenClaw
Kind of agentChat appDesktopMessaging
Google searches a month, Aug 20261.12B368k450k
Read the landscape →

Who uses which AI coding agent, counted five ways

Surveys, router tokens, signed GitHub work, npm installs and stars, side by side: Claude Code leads two and is second on two.

MeasureClaude CodeCodexCursor
Developers using it at work, May–July39%16%12%
OpenRouter tokens, 30 days to 13 Oct32.6T11.9T1.1T
Read the scoreboard →

SubQ, jev, Perplexity's Decider and Bespoke's Nimble on biotech diligence memos: finding the sentences a column is about

Four decision models judge every sentence of ten biotech diligence memos for five columns, scored against sentences a person marked.

MeasureSubQjevDeciderNimble
Found, mean of five columns at section length64%65%66%68%
Mechanism of action, found at section length57%51%61%54%
Read the comparison →

Website traffic for a list of domains: hedwigai priced against DataForSEO, SpyFu, Semrush and Ahrefs

124 domains' monthly search traffic for $0.0164, against $0.0269 from DataForSEO and $0.062 from SpyFu plus its $119 plan.

MeasurehedwigaiDataForSEOSpyFu
124 domains$0.0164$0.0269$0.062
1,000 domains$0.104$0.132$0.500
See the prices →

How AI harnesses work, shown on one research workbook

The six terms behind every agent, each shown as the MCP tool that drafts a workbook from primary sources.

MeasureFirst runAfter review
Model calls112120
Wall clock12 min 39 s11 min 50 s
Read how it works →

Twenty current models, every provider, every rate

Closed models cost the same everywhere and sell speed; an open model can cost six times as much from one host as another.

MeasureGLM-5.3DeepSeek V4.1 FlashGPT-6 Astra
Hosts selling it52497
Cheapest, per 1M read / written$0.35 / $1.10$0.09 / $0.18$10 / $50
Read the prices →

Leads with a reason to write: hedwigai ask, Apollo and treg on one brief

40 leads each, every one checked: the right person, an inbox that accepts, and a dated reason to write.

MeasureaskApollotreg
Usable leads13 of 407 of 406 of 28
Cost per usable lead$0.037$0.27$0.23
Read the comparison →

Time machine: leak-free RL environments, graded by what actually happened

A feature: pick a date, and every source answers as it would have on that day.

MeasureRL playerA coin
Score, clock before the answer is measured0.7470.750
Score, clock after it (a leak)0.7620.750
Read the design →

Self-hosted decisions for ask: Perplexity's open Decider in place of jev on 92 company questions

The decisions inside hedwigai ask made by Perplexity's open Decider on two NVIDIA RTX 3090s, against jev.

Measureask with jevask with Decider
Questions right, of 928989
Wrong answers12
Read the comparison →

Biotech due-diligence document gathering: coverage across 11 companies

Eleven companies of diligence documents, gathered and scored.

Companies covered11
Mean coverage, 0 to 10.971
Read the numbers →

US importer search for heavy engineering equipment: coverage across 5 product lines

Five product lines of importers found, qualified and gated.

Product lines covered5
Mean coverage, 0 to 10.956
Read the numbers →

Aircraft records completeness: an evaluation method (no models scored yet)

Whether a records package is complete — the method, before the scores.

Methods described8
Kinds of defect9
Read the method →

Clinical decisions: jev and ask on System One, PubMedQA, SciFact and a 370-decision set

EdgeEvals' System One board, PubMedQA, SciFact, and a clinical set built to System One's rules.

Measurejevask
Our 370 clinical decisions, right94.6%93.0%
PubMedQA, right (best EdgeEvals scored: 62.2%)77.0%74.4%
Read the comparison →

Executive lookup for US-listed companies: Claude Opus 5.5 and ask on 95 questions

Who runs a public company, and how to email them: a frontier model from memory against hedwigai ask.

MeasureaskClaude Opus 5.5
Questions right, of 955963
Small companies right, of 20139
Read the comparison →

Rental comps: floor-plan rents and amenities on 14 listings, five assistants compared

Rent per floor plan and amenities for US rentals: hedwigai realestate-us against Claude, GPT, Gemini and Perplexity with web search.

Measurerealestate-usBest of four assistants
Floor-plan rents right, of 262623
Amenities claimed the listing doesn't state12129
Read the comparison →

Company screening from SEC filings: jev, Vela 2.0 and ask on 92 yes/no questions

Yes-or-no questions about public companies, answered by two decision models alone and by hedwigai ask.

Measureaskjev
Right, of the questions answered98%79%
Wrong answers, of 92219
Read the comparison →

A same-model decider above an agent's writer: one task, two configurations

The same document and the same instruction, run with and without a decider above the writer.

MeasureWith a deciderWithout
Wall clock23m 36s3m 46s
Model calls10434
Read the comparison →

Defect localization in industrial images: four frontier models and a trained localizer

Four frontier models asked to put a box around what they just described.

MeasureTrained localiserBest of four frontier models
Mean overlap with the defect (IoU, 0 to 1)0.4210.109
Read the numbers →

Company lists in ask: a small model trained to put the right companies first, tested on 33 new questions

Ask for a list of companies and get ten, each with its source. How the ten are chosen, and how much better it got.

MeasureTrained for listsGeneral-purpose model
Correct, of the companies in the top 10s62%55%
Correct companies in the top 10s142117
Read how it works →