Case study · Proposal-to-SOW automation
Proposal-to-SOW cycle: 11 days → 38 hours for an Irvine SaaS leader
An Irvine-based B2B SaaS firm (50–200 employees) was losing late-funnel deals because SOWs took 11 business days to draft. Implement Agentic built a closed-loop proposal agent on LangGraph + Anthropic Claude with a clause library distilled from 4 years of past SOWs.
- 11d → 38h
- Proposal-to-SOW cycle
- +28%
- Sales-team capacity
- $420K
- Annualized labor reallocated
- 12 wks
- Pilot to production
The challenge
- Proposal-to-SOW cycle averaged 11 business days; ~17% of late-stage deals stalled while waiting.
- Three different SOW templates in active use; pricing logic lived in a single SE's head.
- Past won/lost SOWs sat in a Google Drive folder no one searched.
The stack
- LangGraph
- Anthropic Claude Opus + Haiku
- LlamaParse
- pgvector
- Anthropic Memory
- Granola
- Salesforce MCP server
- Langfuse
The closed loop, applied
Capture
Granola captured every discovery and scoping call. Drive + Salesforce backfilled 4 years of historical SOWs and deal data.
Normalize
LlamaParse extracted clauses; an enrichment pipeline tagged each by industry, dollar size, win/loss and SE owner.
Memory
Anthropic Memory stored per-customer preferences and recurring objections; semantic memory captured the firm's house style.
Reason
LangGraph agent drafted SOWs through Requirements → Scope → Deliverables → Pricing → Risk → T&Cs with a mandatory human checkpoint.
Act
MCP server wrote drafts directly into Google Docs and updated Salesforce opportunity stages.
Evaluate
Langfuse traced every span. Won/lost outcomes fed back into the clause-library scorer weekly.
"Implement Agentic replaced four months of proposal drafting with a closed-loop agent that learns from every won and lost deal. Our SOW cycle dropped from 11 days to 38 hours."
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