Document generation engine

Drafting a complex agreement means assembling a template, a term sheet, supporting tables, and years of accumulated judgment into one coherent document. The engine settles the conflicts between them before it writes, then produces the draft chapter by chapter — with every model call running on hardware you control.

How it works

You giveThe template and the term sheet, plus any tables, comments, or supporting documents that travel with the deal. Everything arrives in DOCX and converts into a form a model can actually read.

The system doesThe assembly. It turns the deal documents into a structured questionnaire, reads the template and the deal data against each other to surface every semantic conflict, retrieves the closest precedents from your own library, then writes the document one chapter at a time.

You stay in controlOver every conflict, before a word is written. Each arrives as a yes-or-no question with the consequence of each answer spelled out and the underlying clause cited. A free-text instruction outweighs everything the system inferred, and any chapter regenerates with a comment attached.

You getA complete first draft in about the time a single chapter used to take, assembled from the versions you approved.

Inside the system

08 components

Deal data intake

The template, the term sheet, and the supporting documents upload in DOCX and convert into machine-readable form.

Structured questionnaire

A free-form document becomes a questionnaire with typed fields: text, numeric, conditional, single- and multi-select, and expandable lists that load their own dictionaries. Building one takes minutes, and completed questionnaires are stored and reused across matters of the same type.

Conflict detection

Template and deal data are read against each other and every semantic contradiction surfaces. Each becomes a yes-or-no question with the consequence of each answer stated and the underlying conflict cited, around twenty on a typical document.

Precedent retrieval

Each chapter of the template is matched against your own document library, and the closest precedents come back with it to shape the structure of what gets written.

Priority instruction

A free-text comment carries the highest weight in the generation context, so a specific instruction overrides everything the system worked out on its own.

Chapter-by-chapter generation

Writing runs one chapter at a time, roughly two minutes each, so a thirty-chapter draft completes in about half an hour. Output renders into formatted HTML for review.

Versioned regeneration

Any chapter regenerates with a comment attached, several regenerations run in parallel, and every take is saved as its own version.

Final assembly

You pick the preferred version of each chapter and the finished document assembles from the takes you approved.

Who it's for

Good fit

Legal teams producing agreements from template libraries

Any organisation where client documents cannot reach an external API

Practices with a precedent library worth drawing on

Teams where senior time goes to assembly rather than judgment

Document types repeated often enough for a questionnaire to pay back

Regulated environments where cloud AI never clears review

Not a fit

Short agreements where drafting is faster than configuring

One-off documents with no template behind them

What you can configure

Your templates and precedentGeneration draws on your own document library, so drafts carry the language and structure your practice has settled on.

What counts as a conflictWhich contradictions between template and deal data are worth surfacing, and how each question gets framed.

How the questionnaire is shapedField types, conditional logic, and the dictionaries each list loads, per document type.

Which model runs itSized to the hardware you have, and swapped for a larger one when you upgrade, without rebuilding the system around it.

What we need from you

HardwareOne GPU that can host the model. The whole stack is built to run inside your perimeter.

Your templatesThe templates you draft from today, in the form your team actually uses them.

Your precedent libraryPast documents worth drawing on.

One reviewerSomeone who can say whether a chapter reads like your practice wrote it, a few hours a week during the build.

Example use cases

Drafting an 80-page agreement with conflicts resolved upfront

An associate spends days mapping a term sheet onto a long template and chasing every clause where the two disagree. An AI engine raises each conflict upfront as a yes-or-no question, then drafts the agreement in the firm's own template.

Often deployed with

Live

In-editor document assistant

An AI assistant inside Word, on models you host yourself.

ReadsA long document open in Word, on your own servers.

ReturnsSummaries, checks, and tracked-change edits in the side panel.

~15 minto check a document

See the solution →

Live

Due diligence engine

Cut a due diligence review from 50 hours to 5.

ReadsThe questionnaire and hundreds of returned documents.

ReturnsA status per question with quotes and source links.

50h → 5hper document package

See the solution →

Start the draft where the judgment starts.

Book a demo →