Approval Inbox
Every drafted section waits in a review queue. The AI surfaces the draft; a named person approves before it joins the submission.
CONCEPT · BUILD-READY · SIMULATED DEMO
The RFP Response Engine is designed to ingest a requirements document, extract every obligation and evaluation criterion, match each section to your library of past winning proposals, and draft a response for a human to review and approve before submission. No more starting from a blank page under deadline.
Concept build. We scope it with you before anything is written.
Proposal teams · Requirements extracted automatically · Section-by-section human approval · Compliance checklist built from the RFP itself
Concept demo with simulated data. Shows the governance model: AI drafts each section, a person approves before submission.
Three beats. Every step is simulated demo data. The governance model is the point.
Jun 9 · RFP received by upload or email · Concept demo, simulated data
The system would be designed to accept an RFP document by upload or email attachment. It identifies the document type, confirms it is an RFP, and queues it for extraction. The proposal team gets a notification that a new RFP is ready to process. Click "Extract Requirements" to continue.
Document identified: RFP for Managed IT Services, City of Meridian (illustrative). Deadline: Jun 23.
Ready for requirement extraction. No content has been read or processed yet.
Concept demo · simulated data · fictional document
Jun 9 · Extraction complete in seconds · Concept demo, simulated data
The system would read the full document and pull every requirement, evaluation criterion, and compliance obligation into a structured checklist -- built directly from the RFP's own language. The proposal team can see exactly what is mandatory, what is scored, and what is optional before a single word of the response is drafted.
Click "Draft Executive Summary" to see a section drafted for approval.
Concept demo · simulated data · fictional requirements
Jun 9 · Draft ready for proposal team review · Concept demo, simulated data
The system would draft the Executive Summary by matching against past winning proposals in your library that scored well on similar municipal IT RFPs. The draft pulls your proven language, adapts it to this RFP's specific evaluation criteria, and presents it for review. The proposal writer edits, approves, or replaces it section by section. Nothing submits without a human sign-off.
Section 1 of 6 · Executive Summary · Matched from: "Metro Transit IT Services Win (2024)" (illustrative)
We are a managed IT services provider with demonstrated experience supporting municipal operations in environments with the compliance, uptime, and public accountability requirements that define public-sector IT work.
Our proposed engagement for the City of Meridian covers the full scope of Section 2 of the RFP: helpdesk services, infrastructure management, cybersecurity monitoring, and strategic technology advisory. Our P1 incident response commitment is a 30-minute acknowledgement and 4-hour resolution target for severity-one events.
Source match: 84% similarity to prior winning section · Adapted for this RFP (illustrative)
Reviewer: K. Patel, Proposals (simulated) · Jun 9, 10:21 AM
Concept demo · simulated data · fictional proposal text
Demo is a concept illustration. All document names, requirements, and draft text are fictional and invented for the purpose of showing the workflow. No real RFP or proposal data is shown.
Concept build. Every capability below uses "designed to" framing because this system does not yet exist in production. We scope and build it with you.
The system would be designed to read the full document -- however it arrives -- and extract every requirement, evaluation criterion, and compliance obligation into a structured checklist built from the RFP's own language. Mandatory items, scored items, and optional items would be categorized automatically so the proposal team sees exactly what they are responding to before drafting begins.
✓ Accepts PDF, Word, or email attachment · Mandatory vs scored vs optional categorized · Checklist built from the document's own requirements language
Before drafting, the system would search your library of past proposals -- specifically the ones you won -- for sections that match the current RFP's requirements and evaluation criteria. The closest matches surface for each section, along with a similarity score, so the writer can see what the draft is based on and decide whether it is the right source.
✓ Library of your past wins as the source · Per-section matching with similarity scores · Writer sees the source before accepting the draft
The system would draft each required section individually, adapted to the current RFP's specific language and evaluation criteria. Each draft would appear in the proposal team's review queue as a separate item, not as a monolithic document to accept or reject all at once. A writer reviews, edits, approves, or replaces each section on its own merits.
✓ One draft per required section · Each section in the approval queue independently · Writer edits, approves, or replaces without affecting other sections
The checklist would be a living document throughout the drafting process. As sections are approved, the checklist updates to show which mandatory items have been addressed and which remain open. Before submission, the system would flag any mandatory requirement that has not been covered in an approved section, preventing a disqualifying omission.
✓ Live compliance tracking as sections are approved · Pre-submission flag for any uncovered mandatory requirement · Exportable for internal review
No section would reach the final submission document without a named team member reviewing and approving it. The audit trail would record who approved what and when, which is useful both for internal accountability and for any debriefs after the award decision. The AI drafts; a person is accountable for every word that goes out.
✓ Named approver required per section · Full audit trail of approvals · No section included in submission without human sign-off
The RFP Engine would be governed the same way as every build we do: a section-by-section approval inbox so a named team member confirms every piece of content, a full audit trail of every extraction and draft action, and a kill switch that pauses processing in one click. Read how we govern our AI.
Approval Inbox
Every drafted section waits in a review queue. The AI surfaces the draft; a named person approves before it joins the submission.
Audit Trail
Every extraction event, every match, every approval would be timestamped and logged. You can reconstruct exactly how the submission was built.
Kill Switch
One click pauses processing. The drafts in the queue remain available. Nothing changes until you re-enable the system.
Want one like this in your operation?
We scope the build with you, confirm the price in writing, and nothing starts until you have seen the full plan.
Scope and price confirmed in writing before any build begins.