Professional Services · Proposal generation automation
Your best proposals are still written from a blank page
Every RFP your firm receives starts the same way: a fee-earner opens a blank document, finds a vaguely similar past proposal, and spends the next three days writing something that is mostly the same as five proposals before it.
Banao builds proposal generation systems that pull from your own winning submissions, your rate card, and your current bench — so the first draft is already 70 percent done before a fee-earner touches it. Partners edit and price; the system assembles.
A 40-partner consulting firm— first-draft proposals assembled from 300 past submissions, fee-earner time on proposals cut by more than half.
The first call is free · 45 minutes · no obligation
What we build
What a Banao proposal automation deployment includes
Proposal automation is not a template library. It is a system that knows your firm's past work, your current capacity, and your clients' language — and keeps all three up to date.
First drafts from your own winning submissions
The system retrieves sections, case studies, and fee narratives from your past proposals that are closest to the current brief, assembles a structured draft, and surfaces the best-matching material — so the fee-earner edits, not writes.
RFP parsing and scope extraction
Incoming RFPs and tender documents are parsed for scope, evaluation criteria, word limits, and submission format before anyone reads them — so the proposal lead starts with a brief already mapped, not a 60-page PDF.
Pricing and fee estimate templates
Standard roles, rates, and effort estimates are pulled from your current rate card and bench data. The first-pass fee table is already populated; the partner reviews assumptions, not arithmetic.
Case study and credential matching
The system matches your past client work to the sectors, problem types, and scale requirements in the RFP, and drafts a credential paragraph for each match — cited to the matter, with placeholders where metrics are still confidential.
Multi-author coordination
Where a proposal draws on multiple practice areas or offices, the system routes sections to the right owners and assembles the full draft without a project manager chasing contributors by email.
Win/loss feedback and library maintenance
Outcomes are tracked against the proposals that generated them. Sections from winning submissions rise in retrieval weight; unsuccessful approaches are flagged. The library improves every time you submit.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack. Client names are withheld where confidentiality agreements require it.
First-draft proposals assembled from five years of past submissions
The firm was declining RFPs for capacity reasons, not lack of capability. Partners were spending two to three days per proposal writing from scratch. Banao built a retrieval system over five years of past submissions, mapped it to the firm's current rate card, and wired it into the proposal workflow — so the first draft arrives before the scoping call is done.
Cross-practice proposals assembled without a coordinator
Multi-practice proposals required a coordinator to chase four or five section owners. Banao routed sections automatically to the right owners and assembled the master draft, reducing coordination overhead and the number of submissions that went out at the last minute.
Dogfooding
Vikaas runs Banao's own proposal pipeline before it runs yours
Banao wins work the same way your firm does — proposals, pitches, and competitive bids. Vikaas, our own demand-generation AI, runs the proposal pipeline for Banao's own client development. Every incoming brief is parsed, past submissions retrieved, and a first draft assembled before a senior engineer touches it.
A firm that lives on competitive bids is a more demanding test for proposal automation than any demo environment. By the time the system reaches your firm, it has already passed ours.
Runs Banao's own proposal pipeline and demand-gen end to end.
Screens Banao's own engineering hires — every AI system we sell is already in production internally.
The honest version
When proposal automation is not the right build
Most firms that ask about proposal AI actually need something simpler — or something different. We would rather tell you in week one than in month six.
- Low proposal volume: if your firm submits fewer than a handful of proposals a month, a well-maintained template folder is cheaper than a trained retrieval system. We'll say so.
- No past submission library: proposal automation draws from your own past work. If submissions have not been saved consistently, the first three months are document recovery, not drafting automation — and that changes the ROI maths.
- Highly bespoke pitches: for a handful of white-glove relationship bids where every word is custom, the hours saved per proposal may not justify the build cost. We scope this in the Discovery Sprint before recommending a build.
How we start
How we start — prove it on your own proposals first
We don't quote a proposal automation system off a brochure. We run a test retrieval against a sample of your past submissions before recommending a build.
- 01
AI Discovery Sprint
2 weeks · fixed price
We review a sample of your past proposals, map where the hours actually go, test retrieval quality on your own archive, and hand back a baseline assessment — including whether the archive is rich enough to support automation and what the ROI model looks like. Yours to keep regardless. If you proceed, the Sprint cost is credited against the build.
- 02
Build
We ingest and index your proposal archive, wire in your rate card and bench data, and build the retrieval and assembly layer into your existing workflow tools — document management, email, or pitch software, depending on where proposals are actually written.
- 03
Production and continuous improvement
The system ships with human review at every output — no proposal goes out without a fee-earner read. Win and loss outcomes feed back into the retrieval weights, so the library gets more accurate with every submission.
FAQ
Frequently asked questions
What source material does the system draw from?
Your own past proposals, pitch decks, engagement letters, and rate cards — nothing external. We ingest your archive, index it by sector, problem type, and outcome, and retrieve from it. The system does not draw from generic databases or third-party content.
Will clients or evaluators know the draft was AI-assisted?
Not from the output. The draft is assembled from your own language, your own case studies, and your own rates. A fee-earner reviews and edits every section before submission — the final proposal is yours, written in your firm's voice.
Can it handle different formats — tenders, pitches, RFP responses?
Yes. The parsing layer reads the incoming document — whatever format — extracts scope and requirements, and selects a structure to match. Different submission types get different assembly templates, all drawn from your own best-performing examples in that category.
Our proposals contain sensitive client matter details. Where does that data go?
It stays inside a boundary you control — your own cloud tenant or an isolated environment we stand up for your firm. We do not train shared models on your proposal data, and retrieval is scoped to your firm only. The data boundary is agreed and documented in week one.
What about the pricing and fee sections — can those be automated too?
Yes, and this is often the highest-value part. We pull current roles, rates, and standard effort estimates from your rate card and bench data, and populate a first-pass fee table. Partners review assumptions and adjust — they are not starting from a blank spreadsheet.
Get started
Put your last three proposals in front of us
In 45 minutes, we will tell you whether your archive is rich enough to support automation and what the ROI maths look like. Fixed-price Discovery Sprint if you want to go deeper.
Book a Discovery Sprint