AI ProposalGeneration
Produce a first draft of a tailored proposal or tender response in an hour, built from your approved content, and let your senior people spend their time on strategy and pricing.
AI Proposal Generation: what the work involves
Every proposal starts the same way: someone opens the last one that was won, saves a copy, and hunts for the paragraphs that must change. Old client names survive in the footer, a case study is out of date, and the scope section is quietly copied from a different industry. Tender responses are worse, with fifty questions and a deadline, and bid managers lose evenings that could have gone toward the pricing and the real pitch.
DevKey builds a proposal workspace on top of your best material. Past proposals, case studies, team bios, methodology notes and compliance answers are indexed with metadata. Given a client brief or an RFP, the system proposes a structure, drafts each section from the closest approved content, and rewrites it for the prospect's context. Every paragraph shows its sources, unsupported claims are marked, and the document stays in a draft state until a named reviewer has checked facts, scope and commercial terms.
Core features
Approved content library
Case studies, service descriptions and standard answers are stored with owners and review dates, so drafts rely on material you trust.
Brief and RFP parsing
The incoming document is broken into requirements and questions, creating a checklist that tracks which have been answered.
Section drafting with sources
Each section is assembled from related past content and tailored to the client's industry, with links back to the originals.
Compliance matrix
Mandatory requirements are mapped to answers and flagged when no answer exists, helping avoid disqualification on a technicality.
Branded document output
Finished proposals are exported to your Word, slide or PDF templates with consistent formatting and cover pages.
Win and loss notes
Outcomes and feedback are stored against proposals, gradually showing which messages and structures work for which clients.
What we get right before launch
Invented credentials
A model might add a plausible but false certification, client or capability. We restrict drafts to library content, highlight anything outside it, and require a reviewer to confirm each claim.
Client confidentiality
Past proposals contain other clients' details. The system strips names and figures from reusable content unless permission exists, and keeps each client's material in separate scopes.
Generic text loses bids
Fluent but bland copy rarely wins. We structure the workflow so the user supplies the angle, insight and pricing logic, and the model supports with drafting and tidying instead of deciding the pitch.
Tools and technology
- Anthropic Claude
- OpenAI GPT
- LlamaIndex
- pgvector
- Python
- FastAPI
- docx and PDF generation libraries
- Google Drive API
- Next.js
Common questions, answered
Can it respond to government tenders?
It can speed up drafting and compliance mapping for tender documents, particularly repeated questions. Specific forms, certificates and pricing sheets still need your team, and every response is reviewed before submission.
Will proposals sound like us?
We tune tone using your best past proposals and a style guide, and a reviewer edits the output. It will still need a human pass, as the voice of a good proposal comes from real knowledge of the client.
What material do we need to supply?
A set of past proposals, case studies, service descriptions and standard answers is enough to begin. Cleaner, better-labelled content gives better drafts, so we help organise it during the first phase.
Does it write pricing?
It can pull standard rate cards or scope items into a table, but pricing decisions remain with your team. We keep commercial figures out of generated text unless they come from an approved source.
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Ready to start your AI Proposal Generation project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.