Build a Custom Lead Qualification Agent with Python

Most teams start with their CRM's built-in automation tools. HubSpot's workflows, for example, are good for simple if/then logic. But they cannot handle multi-step, asynchronous processes. A workflow that needs to call an external enrichment API, wait for a response, and then pass that data to another service for scoring often requires brittle, timed delays that fail intermittently.

A regional insurance agency with 6 adjusters tried to build this in Salesforce Flow. Their process required checking three external government databases before assigning a claim. The flow hit governor limits on API callouts and couldn't manage the state of a claim that took 2 minutes to process. The system was so unreliable they reverted to manual processing within a month, with adjusters spending an hour each day on data entry.

These platforms are fundamentally stateless. They can trigger actions but cannot manage a process that evolves over minutes or hours. They lack sophisticated retry logic for failed API calls and cannot escalate complex cases to a human while keeping track of the context. This forces teams into manual workarounds that defeat the purpose of automation.

Syntora approaches lead qualification automation by first mapping your existing process into a state diagram. This involves using a framework like LangGraph, where each step, from initial data enrichment to final routing, would be represented as a node in a graph. This orchestration layer helps manage workflow logic. An engagement typically begins with creating a webhook endpoint, often with FastAPI, configured to trigger the qualification process upon a new lead entry in your CRM.

Each node within this graph would execute a specific task using Python functions. For lead enrichment, the system would call external APIs such as Clearbit, using async httpx requests for efficiency. For lead scoring, the Claude 3 Sonnet API could be used to analyze form submissions and available company data against a detailed rubric defined by your team. API responses would be cached in a Supabase Postgres database to reduce redundant lookups and optimize resource use.

The delivered system would be packaged as a Docker container and deployed on a serverless platform like AWS Lambda for event-driven execution, which provides scalability. State would be persisted in Supabase after each step, allowing the agent to manage workflows that might span varying durations without timing out.

A crucial component would be a supervisor agent. This agent monitors the primary qualification workflow. If a lead's processing encounters repeated failures or results in an ambiguous score, the supervisor would collect all relevant data and post it to a designated channel for human review. This human-in-the-loop escalation ensures that complex or problematic leads receive appropriate attention and are not lost. Syntora would work with your team to define these escalation rules and integration points.