Sales & Business Development

Turna800-pageRFPintoadraftedresponsewithprovenlanguagepulled,contentgapsmarked,andcompliancematrixbuilt.

An 800-page RFP lands on a Friday. The AI reads every section, extracts every requirement, and checks each against past proposals and the knowledge base. Where a requirement matches something that’s been won before, the proven language is reused. Where it doesn’t, it flags the gap so writers focus where it matters. Forms are pre-filled, and response checklists are prepared to make sure nothing is missing. Before submission, using the team's contribution, the agent rebuilds the compliance matrix against the final draft to double-check that every requirement is addressed and nothing was lost in edits.

Key Takeaways

82% effort reduction

An 800-page RFP processed into a submission-ready first draft over a single weekend — no analyst overtime required.

Compliance verified twice

The compliance matrix is built during drafting, then rebuilt against the final version. Nothing slips between edits.

Proven language reused

Requirements matched against past winning proposals. Proven language is pulled in automatically — writers focus on novel sections.

Gaps flagged, not guessed

New requirements with no strong match are surfaced explicitly so subject-matter experts spend time where it matters.

Win rate improves over time

Every submitted proposal feeds back into the knowledge base. The agent gets sharper with each cycle.

Forms and checklists pre-filled

Licensing forms, certifications, and compliance checklists are populated from structured company data — no copy-paste.

The bottleneck isn't writing. It's reading.

Most teams lose the first three days of an RFP cycle just understanding what's being asked. The agent reads every page, decomposes every requirement, and cross-references each one against your past proposals and knowledge base — before a single writer opens a document. When the team sits down to tackle an RFP, they're not starting from scratch. They're reviewing a structured draft where 70-90% of the requirements already have proven responses, gaps are clearly marked, and the compliance matrix is already built. The work shifts from production to judgment: refining language, adding strategy, making the case. That's where humans win deals.

One call to launch the full pipeline

Point it at the RFP documents and your proposal library. The agent reads every page — rendering images where needed — cross-references requirements against your past responses, then produces a first-draft and a compliance matrix. Every decision is traceable.

python
import dspy
from predict_rlm import File, PredictRLM


class RespondToRFP(dspy.Signature):
    """Read every page of the RFP (rendering images where needed),
    cross-reference requirements against the proposal library, then
    produce a first-draft response and a compliance matrix."""

    rfp_documents: list[File] = dspy.InputField(
        desc="RFP PDFs and addenda"
    )
    proposal_library: list[File] = dspy.InputField(
        desc="Past proposals, certifications, and capability docs"
    )
    response_draft: File = dspy.OutputField(
        desc="First-draft response in DOCX format"
    )
    compliance_matrix: File = dspy.OutputField(
        desc="Requirement × status × evidence — Excel"
    )


rlm = PredictRLM(
    RespondToRFP,
    lm="openai/gpt-5.4",
    sub_lm="openai/gpt-5.1",
    max_iterations=30,
)

result = rlm(
    rfp_documents=[
        File(path="ACME_RFP_2026.pdf"),
        File(path="Tech_Specs_Annex.pdf"),
        File(path="Compliance_Addendum.docx"),
    ],
    proposal_library=[
        File(path="proposals/"),
        File(path="certifications/"),
    ],
)

print(f"Draft:  {result.response_draft.path}")
print(f"Matrix: {result.compliance_matrix.path}")

Built on predict-rlm — open source. github.com/Trampoline-AI/predict-rlm

What the team receives Monday morning

The agent produces two deliverables. First, a Word document structured to mirror the RFP's own section numbering. Matched requirements contain proven language pulled from past winning proposals — with source attribution so reviewers can verify. Gaps are marked with structured placeholders that include the original requirement text, suggested approach notes, and a priority flag. Second, an Excel compliance matrix mapping every single RFP requirement to its response location, compliance status, and any reviewer notes. The matrix is rebuilt against the final draft before submission, catching anything that shifted during the editing process.