AI-Powered RFP & Tender Response Platform
Drafts structured tender responses from past wins, pricing sheets and product docs.
Key Details
| Challenge | Bid teams rebuilt every RFP from scratch and missed reusable language from past wins. |
|---|---|
| Solution | A drafting workspace that retrieves prior answers, prices and compliance language into a structured response. |
| Technologies | GPT-4, Python, PostgreSQL, SharePoint APIs |
Technologies used
OpenAI
Python
PostgreSQL
SharePoint
Docker
Client background
A professional-services firm responding to dozens of RFPs each quarter. Winning language lived in old Word files and email; pricing sat in spreadsheets that bid writers could not query. Legal review became the bottleneck because every draft started from a blank page.
Key challenges
- No library of approved answers — teams reinvented compliance and capability language every time.
- Pricing sheets were disconnected from narrative sections, causing inconsistent bids.
- Legal review cycles stretched because drafts lacked structure and citations to prior wins.
- Tribal knowledge walked out with senior bid managers.
What we built
- Section-aware drafting UI that maps RFP requirements to reusable answer blocks.
- Retrieval over past wins, pricing sheets and product docs with freshness rules.
- Export to Word that preserves comments and tracked-change friendly structure.
- Permissioned library so only approved language ships into client-facing drafts.
Project team: 7 engineers across AI/ML, backend and domain specialists — delivery over 16 weeks.
How we delivered
01
Audit
Catalogued past RFPs, win rates and where reusable language already existed.
02
Model
Designed a section taxonomy and answer library with ownership and review states.
03
Build
Shipped retrieval + drafting workspace integrated with SharePoint.
04
Adopt
Trained bid teams and measured time-to-first-complete draft.
Business impact
40%Less drafting time
HigherFirst-pass completeness
OneLibrary of record