SaaS / AI
Autonomous RFP Proposal Generation in Hours Instead of Days
Attach any RFP. The system assesses fit, assembles the team, and generates a complete costed proposal.
The challenge
Responding to an RFP is a research project disguised as a writing task. Before a single sentence gets written, an account manager has to assemble the raw material: which team members fit and are available, what their bios say, which past projects are relevant, the current rate structure, and a realistic timeline. That information lives in a dozen places.
The result was predictable. Days spent per response. Inconsistent quality, because output depended on how much time the account manager had. Missed deadlines, because RFPs arrive on someone else’s schedule. And no ability to scale.
Worst of all, the effort was front-loaded onto opportunities that might not be a fit at all. Teams burned days on RFPs they should have declined in the first hour.
What we built
An autonomous RFP proposal engine that unifies the entire knowledge surface of the company into a single intelligent pipeline, then generates complete, submission-ready proposals from an attached document.
A unified company knowledge base
Company profiles, employee portfolios, project histories and rate structures consolidated into one queryable layer. Instead of the AI inventing plausible credentials, it retrieves real ones.
Autonomous fit assessment
A user attaches any RFP. The AI reads it and independently assesses fit against actual capabilities and past work, before any proposal effort is spent. Bad-fit RFPs are identified in minutes.
Intelligent team assembly
The system matches RFP requirements to the employee portfolio and assembles the right talent mix, pulling real bios, credentials and relevant project experience.
Complete proposal generation
A full proposal, not an outline: team breakdowns with bios and roles, deliverable timelines mapped to requirements, cost structures from live rate data, and relevant past project evidence. Ready to submit.
Live knowledge base querying
Every response is grounded in a live query. When rates change or a project completes, the next proposal reflects it automatically: no stale boilerplate.
How it works
Attach: the user uploads any RFP document, in whatever format it arrived.
Parse: the system extracts requirements, scope, constraints and evaluation criteria.
Assess: the AI evaluates fit against actual company capability and past work.
Assemble: the right team is composed from the employee portfolio.
Generate: a complete proposal is produced with team, timeline and cost structure.
Ground: every claim is checked against the live knowledge base for accuracy.
Results
Proposal turnaround reduced from days to hours
Consistent quality independent of which account manager owns the response
Fit assessment completed before effort is invested
Proposal capacity decoupled from headcount
Every response grounded in current, accurate company data
See it in action
Watch an RFP go in and a complete proposal come out: autonomous fit assessment, team assembly from the live portfolio, and a fully costed deliverable timeline.
Technology
FAQ
How does it avoid inventing credentials or experience?⌄
Generation is grounded in retrieval. The system queries the live knowledge base of real portfolios, project histories and rate structures, it assembles from actual records rather than plausible fiction.
Can proposals still be edited before submission?⌄
Yes. The engine produces a complete, submission-ready draft; teams retain full control to review and adjust before it goes out.
What happens when rates or team availability change?⌄
The knowledge base is the single source of truth. Update it once and every subsequent proposal reflects the change automatically.
Does it decide which RFPs to pursue?⌄
It assesses fit against real capabilities and past work before effort begins, so the pursue-or-decline call is made with evidence in minutes.
Losing days to RFP responses?
If your team burns days assembling proposals by hand, there’s an engine that does it in hours.