Deal Brain reads a new brief, pulls the comparable jobs you have already delivered with their real hours and margins, scores the tender's complexity, and drafts the first offer. From your history, not a blank page.
A quote starts from a blank page. There is no coded library of past proposals, assumptions and outcomes to start from.
Proposals live in individual inboxes and drives. Each seller re-derives complexity, wording and margin logic from memory.
What was quoted and what delivery later discovers the work required drift apart. Nobody sees the gap until the job is done.
"No institutional memory today. Every proposal relies on individual context."
Leadership ask, discovery interviewsReads the brief, pulls the nearest delivered jobs with what actually happened: scope, assumptions, hours quoted against hours spent, margin, won or lost.
Tender complexity is scored against that history, so a hard job looks hard before it is priced, not after it is delivered.
A consistent first draft from a coded proposal library, in house wording, every assumption written down. Commercial approval stays with people.
No comparable found: it goes to a delivery-team review instead of a guess at scope. Their answer becomes the next comparable.
It compounds. Every quote and its outcome feeds back in as comparable-job data. The thing the business lacks at the point of commitment today is what Deal Brain accumulates with every use.
Every idea logged in discovery that points at this tool, and the station on the drawing where it lands.
Store coded offers, assumptions and outcomes so comparable proposals can be found and reused.
Founder register 01 RetrieveFind comparable work, assumptions, scope and outcomes before a new quotation is prepared.
Founder register 01 RetrieveA consistent first draft from a controlled proposal library, with commercial approval kept with staff.
Founder register 03 DraftSpeed up proposal generation and aggressively win more clients.
Founder atlas 03 DraftCapture the judgement in a few senior heads: surface similar past jobs when a request lands, draft quotes and feasibility assessments from history, score tender complexity.
Opportunity document 01 + 02An agentic proposal builder: a new hire generates a 90% proposal from historical deals. Assumes access to proposal history.
Comments, wild future 03 DraftThe same agent, already promised in the partner brief's wave one. Deal Brain seeds it rather than competing with it.
Use-case inventory, partner briefs 01 to 04No institutional memory today. A new hire should generate a 90% proposal from historical data; wins on pricing accuracy and new business.
Leadership asks CompoundsDraft proposals straight from the technical specification that arrives with the request.
Sales focus group 02 + 03Response platforms draft from a content library. Proposal builders template and track. Language-industry systems price by word count. Estimating from actuals exists in construction and professional-services software. None of them reads what a documentation job actually cost you.
Loopio for library-driven first drafts, Qwilr for structured and trackable proposals, PandaDoc for content blocks with approval. Each covers the writing half of the job.
Estimating from a completed-job library is normal in construction estimating and in professional-services platforms such as Deltek and Kantata. Not in documentation or translation services.
Plunet generates Global Content offers from rate cards today. Deal Brain reads its outcomes; it does not replace it. AI-native response tools start around $30k a year and still draft from text.
No. It removes the blank page and gives every quote a documented, checkable starting point. Commercial judgement stays with the people who own the margin.
The judgement that lives in a few senior heads becomes retrievable. The target set in discovery: a new hire drafts a 90% proposal from historical deals in their first week.
They draft from a content library. Deal Brain drafts from outcomes: what a job actually cost against what you quoted. That data exists nowhere else, and no vendor can ship it.
Sales, Engineering, Experience and Global Content each raised it independently. No other idea in the discovery converged this hard.
Nothing to buy and nothing to migrate. The first version runs on a folder of past offers and the outcomes you already know.
Build the schema. Ingest ten real proposals with their assumptions and outcomes, or a synthetic set if the real ones are still in inboxes.
Add retrieval over the library and the first-draft screen. Score complexity on the ten and compare it with what delivery remembers.
Proposal history from Sales. Ten offers is enough to start; two hundred is where it gets interesting.