Backend / AI Engineering

Inbound Lead Engine

Full-stack containerized environment. Zero ingestion wait-time. Automated state escalation for hard failures.

Inbound Lead Engine
01 // BUSINESS PROBLEM & STRATEGY

THE PROBLEM THAT NEEDED SOLVING

High-volume lead ingestion creates bottlenecks. Needed a system resilient to AI failures and delivery issues without human intervention.

WHAT WAS ACTUALLY IMPLEMENTED

Built async engine with FastAPI + Redis. Heavy tasks (AI inference, delivery) run in background workers. Strict deduplication via SHA256. Real-time SLA enforcement (5-min gates).

02 // TECHNICAL IMPLEMENTATION & ARCHITECTURE

Async Lead Processing Architecture

Automated lead ingestion and intent analysis system designed for scalability and failure-tolerance.

Inbound Leak Flow

Reliability Layer

Core challenge: AI reliability. Implemented 'Guarded AI' pattern—pydantic-validated JSON responses with 'general inquiry' fallback. If AI doesn't respond or API is down, system doesn't break. Lead keeps moving.

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