AI Automation · Confidential client
Multi-Agent Lead Generation Engine
Production multi-agent system that handles inbound leads end-to-end across text, voice, and real-time phone — at ~1/250th the cost of the prior workflow.
- Lead conversion rate
- >50%
- Cost per conversation
- $5.00 → $0.02
- Channels
- Text · Audio · Phone
- Integrations
- HubSpot · Salesforce · X-Time
01 · Problem
What we were asked to solve
The prior workflow paid roughly $5 per qualified conversation across human SDRs, scheduling overhead, and CRM hygiene — and capacity capped at office hours. The brief was to handle inbound at any hour, across channels, qualify professionally enough to protect the brand, and write back cleanly to the customer's existing systems of record.
02 · Approach
How we built it
We built a multi-agent runtime on LangChain with prompt and tool routing, RAG over the client's own product and policy documentation, and first-class integrations with the schedulers and CRMs already in use. The same engine answers across text chat, audio, and real-time phone — the channel is a transport detail, not a separate product.
03 · Architecture
The system, in five lines
- LangChain-based multi-agent orchestration with prompt and tool routing
- RAG over business documentation for grounded, in-policy answers
- Channel-agnostic intake — text, audio, real-time phone — through a shared agent core
- Native integrations with HubSpot, Salesforce, and X-Time scheduling
- API-first deployment with cost and latency budgets enforced per turn
04 · Stack
What it runs on
- LangChain
- Multi-agent orchestration
- RAG
- Real-time voice (telephony)
- HubSpot · Salesforce · X-Time
- API-first deployment
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