AI proposal & pitch drafting
A marketing-agency business where this lands in Sales.
02 — What is actually going wrong
Proposals are written by the most senior person available, at night, from whichever old deck was closest to the brief. The good case studies live in someone's head. Turnaround is slow enough that you sometimes lose on responsiveness alone.
How it works
The brief goes in; the system retrieves the relevant past work, case studies and pricing structures from your own archive and assembles a first draft in your house format. The strategist spends their time on the argument rather than on assembly and formatting.
What good looks like
First drafts exist within hours of a brief landing. Every proposal cites the strongest relevant case study rather than the most recently remembered one, and win rate moves because you are turning up faster with a tighter argument.
03 — The problem, in money
- Proposals per month18 proposals150 proposalsPlaceholder — not a validated benchmarkStarting point
- Lift in win rate0.08 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
- Average contract value12,000 USD125,500 USDPlaceholder — not a validated benchmarkStarting point
04 — The options
05 — The call
Buy it. An off-the-shelf tool wins this one — it gets you live faster, and the math does not justify building from scratch.
06 — How this goes wrong
- Sending a draft that reads like a draft. Assembly speed only converts if someone still owns the narrative — the fastest generic proposal loses to a slower specific one.
- Hallucinated results in case studies. Every number in a proposal must trace to a real engagement; this is the failure mode that ends client relationships.
- Feeding it your losing proposals as well as your winners without labelling which is which.
- Assuming a win-rate lift you have not measured. This one input drives the whole model — hold it at zero until you have data.
07 — How you would actually do it
- 01Gather your last thirty proposals and mark won, lost and why.
- 02Extract the reusable assets: case studies with verified numbers, team bios, pricing structures.
- 03Build retrieval over that archive, not over the open internet.
- 04Draft into your real template so output needs no reformatting.
- 05Mandate a strategist rewrite of the approach section before anything is sent.
- 06Track win rate by cohort for two quarters before believing the lift.
08 — Tools worth looking at
Named for orientation, not endorsement. No affiliate arrangements, and nothing here has been paid for.
09 — Run it on your numbers
Start from industry averages, then drag the inputs to match your business. The number moves live.Drag each input to your number — the starting points are neutral midpoints, not industry data. The number moves live.
10 — The evidence · what others report
Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.
11 — Questions owners ask
Is this your business?
Get your free AI Opportunity Score — the same first pass I run at the start of every Audit.
Get Your Free Score