Case study · Real-time customer requirements analysis
Discovery-to-scope in hours: a Newport Beach SI's real-time requirements agent
A Newport Beach systems integrator wanted senior consultants out of note-taking and back into selling. Implement Agentic built a real-time discovery agent that captures calls, extracts requirements, links them to past delivered solutions, and produces a scoped proposal draft within hours instead of weeks.
- 9h → 45min
- Consultant time per discovery
- 3.4×
- Proposal throughput
- +18 pts
- Discovery-to-close conversion
- 10 wks
- Pilot to production
The challenge
- Senior consultants spent ~9 hours per discovery synthesizing notes and hand-matching requirements to past projects.
- Tribal knowledge of 'what we built for ClientY in 2024 in Z weeks' lived in three people's heads.
- Proposals lagged the call by 2–3 weeks; competitors were responding in days.
The stack
- LangGraph
- Anthropic Claude Sonnet
- Granola
- Unstructured.io
- LlamaParse
- Neo4j (LazyGraphRAG)
- Linear MCP server
- Langfuse
The closed loop, applied
Capture
Granola recorded every discovery call. A custom MCP server pulled past project docs from Notion and Google Drive.
Normalize
Unstructured + LlamaParse parsed past project artifacts; entity extraction tagged technologies, outcomes, durations and customer industries.
Memory
A Neo4j knowledge graph linked projects ↔ customers ↔ technologies ↔ outcomes. Semantic memory captured the firm's solution-engineering heuristics.
Reason
LangGraph agent ran requirement extraction, past-project matching, gap analysis and scope drafting as sub-agents.
Act
MCP servers drafted a scoped proposal in Google Docs, opened a Linear epic, and posted a summary to Slack within 30 minutes of call end.
Evaluate
Langfuse + a golden set of 60 past discoveries scored extraction precision; consultant edits fed back as a weekly DPO fine-tune.
"We stopped losing deals to 'they got back to us first.' The agent drafts a scoped proposal before the consultant has finished writing the recap email."
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