Solution · Revenue Operations
Proposal-to-SOW Agent: Turn Discovery Notes into a Reviewable Statement of Work
From discovery call transcript to a reviewable proposal and SOW in under an hour — with your pricing logic and brand voice baked in.
What this solution does
A proposal-to-SOW agent ingests discovery-call notes, scopes the work using your historical project library, applies your pricing rules, and drafts a brand-aligned proposal and statement of work for human review. It replaces 4–8 hours of partner-level drafting with 30–60 minutes of editing — without giving up legal precision or pricing discipline.
The buyer problem
- ›Partners and principals spending evenings drafting proposals after discovery calls
- ›Scope inconsistency between what was discussed and what gets written down
- ›Pricing drift because every proposal is re-derived from memory
- ›Lost deals from slow turnaround on competitive opportunities
- ›Junior staff cannot draft proposals at partner quality without weeks of revision
Good-fit signals
- Services firms running 5+ proposals/month
- An existing library of historical SOWs in Google Drive, SharePoint, or a CRM
- Reasonably stable scoping framework (phases, deliverables, pricing tiers)
- Discovery calls already recorded or transcribed
Pilot scope
In scope
- +Ingest discovery-call transcripts (Zoom, Gong, Fireflies, or upload)
- +Retrieval over your historical SOW library (100–500 documents)
- +Brand-voice and pricing-rule prompting calibrated with your sales lead
- +First-pass proposal and SOW draft, ready for partner review
- +Eval harness scoring against 20 historical wins
Out of pilot scope
- −Legal contract negotiation or redlining
- −Final pricing approval (always human)
- −CRM hygiene cleanup or pipeline reporting
Timeline: 4–6 weeks · Investment: $15K–$28K fixed-price pilot
Systems we typically integrate
- Google Drive / SharePoint (SOW library)
- Salesforce / HubSpot / Pipedrive (opportunity context)
- Gong / Fireflies / Zoom (discovery transcripts)
- Slack or email (draft delivery to reviewer)
- Optional: PandaDoc / DocuSign for downstream signature
Where humans stay in the loop
- Partner reviews every proposal before it leaves the firm
- Pricing outside a pre-approved band triggers a second-approver gate
- New service categories require explicit reviewer sign-off before reuse
- Every agent draft is versioned and audit-logged
KPI scorecard
| Metric | Typical baseline | Pilot target |
|---|---|---|
| Time from discovery to first proposal draft | 2–5 days | Under 2 hours |
| Partner time per proposal | 4–8 hours | 30–60 min review |
| Proposal-to-close cycle | 21+ days | 10–14 days |
| Pricing-rule compliance | Variable | 100% audited |
Risks and the controls we put in place
Agent fabricates capabilities the firm does not deliver
Retrieval restricted to approved SOW library; eval harness scores hallucination rate against ground truth.
Pricing drift undercuts margin
Pricing-rule engine runs deterministically over agent outputs; out-of-band quotes blocked until human approval.
Brand voice inconsistency
Style prompt calibrated on 20 partner-written examples; reviewer feedback loops back into prompt versioning.
Confidential client data exposure
Agent runs in your environment; no third-party model is trained on your data; per-document ACLs enforced through retrieval.
How we help
Discovery-first scoping
We start with a Workflow Triage to confirm the proposal-to-SOW workflow is your highest-ROI candidate before any build.
Calibration on your wins
We tune the agent on 20 of your best historical proposals so the output sounds like your firm, not a generic LLM.
Eval harness shipped on day one
Every prompt or model change runs through a regression suite before it touches production proposals.
Reviewer-grade UX
Partners review and approve in a clean diff view — not a chat window, not a giant prompt.
Frequently asked questions
How long does a proposal-to-SOW agent take to deploy?+
Typical pilot is 4–6 weeks: week 1 for triage and SOW library prep, weeks 2–4 for agent build and calibration, weeks 5–6 for reviewer UX and evaluation. Production handoff at the end of week 6.
Does the agent send proposals automatically?+
No. Every proposal is reviewed and sent by a human. The agent's job is to compress 4–8 hours of drafting into 30–60 minutes of high-leverage review.
What if our SOWs are messy and inconsistent?+
That is normal. Part of the calibration phase is identifying the 20–30 proposals that best represent the firm you want to be, and using those as the retrieval ground truth.
How is this different from a Word template?+
Templates fill in blanks the same way every time. The agent reads the discovery conversation, decides which deliverables apply, retrieves comparable past work, and writes scope language tailored to the prospect's industry and goals.
Will the agent leak our pricing logic externally?+
No. The agent runs in your environment with retrieval scoped to your private SOW library. Outputs never leave your tenancy until a human reviewer sends them.
Services that ship this solution
The capabilities we combine to put this pilot into production.
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AI Agent Development
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RAG & Enterprise Knowledge Systems
Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from your enterprise data to answer queries with up-to-date, accurate information. At Implement Agentic in Irvine, we build RAG systems and enterprise knowledge platforms that deliver ~91% recall@10 with hybrid retrieval — far above naive vector search.
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AI-Native Process Implementation
An AI-native business process is a workflow designed so AI agents can participate directly in the work: gathering context, reasoning over business rules, coordinating with systems, requesting human approval, taking approved actions, evaluating outcomes, and improving from feedback. Implement Agentic designs, builds, and operates these processes for Southern California businesses.
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AI Governance & LLM Evaluation
Without evaluations, prompt or model changes silently degrade quality — and you only learn from customer complaints. At Implement Agentic in Irvine, we build comprehensive eval harnesses with golden test sets, LLM-as-judge scorers, deterministic validators, and CI/CD gates — plus governance frameworks that ensure your AI agents operate within policy, compliance, and ethical boundaries.
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Insights for buyers evaluating this solution
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Score candidate workflows on impact, data readiness, governance fit, and reversibility — and write success criteria before the build. The four-factor rubric most pilots skip.
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Count direct labor reclaimed, error costs avoided, cycle-time value, and opportunity cost — and subtract the eval and ops costs every vendor spreadsheet hides. The defensible four-line model.
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