ConsultingFirmAutomatesProposalDraftingwithLangGraph.js
How a management consultancy built a LangGraph.js multi-agent workflow to automate first-draft proposal generation — retrieving relevant past engagement work from Qdrant, drafting structured proposals, and routing them for principal review.
Key Benefits
How a management consultancy built a LangGraph.js multi-agent workflow to automate first-draft proposal generation — retrieving relevant past engagement work from Qdrant, drafting structured proposals, and routing them for principal review.
- First draft: Automated
- Past work: Retrieved
- 10 Wks: Delivery
The Challenge
Consultants at a management firm spent significant time preparing proposals for new engagements. Most proposals had a similar structure but required retrieval of relevant past project summaries, approach documents, and pricing precedents from previous engagements — stored across a mix of shared drives and document management systems. The process was repetitive: find relevant past work, adapt the methodology section, populate the scope and team, apply current pricing, and format for client delivery. Each proposal took one to three days of consultant time to produce.
The Solution
Techseria built a LangGraph.js multi-agent workflow for proposal generation. The process: a consultant enters a brief description of the prospective engagement (sector, scope type, estimated scale) — the agent retrieves semantically similar past engagement summaries and approach documents from a Qdrant knowledge base, drafts a structured proposal document using the firm's template and current pricing data, and routes the draft to the responsible principal via a Teams notification for review and approval before client delivery. State persistence within LangGraph.js allows the workflow to pause at the human review gate, accept edits, and continue to final formatting. All proposal drafts and human edits are logged for quality improvement of the retrieval model. Delivered in 10 weeks from Discovery Workshop.
Impact by the numbers
- 10Weeks
- Full DeliveryDiscovery Workshop to production deployment in 10 weeks, including knowledge base ingestion, testing, and rollout.
Results
How a management consultancy built a LangGraph.js multi-agent workflow to automate first-draft proposal generation — retrieving relevant past engagement work from Qdrant, drafting structured proposals, and routing them for principal review.
- Proposal First Draft Time: Faster - First draft generated in minutes from engagement brief input rather than days of manual research and writing.
- Past Engagement Work: Retrieved - Relevant past project summaries and methodology documents retrieved semantically from the Qdrant knowledge base.
- Consultant Time Per Proposal: Freed - Consultant time on proposal preparation redirected from information retrieval and formatting to client-facing review.
- Human Approval Gate Built-in: ✓ - Principal review step integrated as a stateful pause in the LangGraph.js workflow — not a separate process.
- Full Proposal Audit Trail: Logged - Every draft, edit, and approval step logged for quality review and continuous improvement of retrieval accuracy.
- LangGraph.js
- LangChain.js
- Qdrant Vector Database
- Azure OpenAI Service
- Node.js / TypeScript
Technologies Used
Client Voice
"We had years of engagement methodology and past project work that consultants couldn't retrieve quickly when writing proposals. The LangGraph.js workflow Techseria built doesn't replace the consultant — it does the retrieval and first draft so the principal can focus on the client-specific customization and quality review. Proposal preparation time has reduced significantly, and the quality of the first draft is consistently better than starting from a blank template."