Deterministic Inbound Qualification & Webhook Router

We built an autonomous inbound qualification prototype to test whether deterministic scoring beats conversational LLMs for inbound lead triage.

Heuristic scoring gates and instant DNS verification beat open-ended LLMs for inbound qualification.

1. The Baseline Problem

Manual form review queues often created a 4-hour delay before high-intent prospects received calendar booking links.

The problem with most inbound qualification isn't intelligence—it is latency. When high-fit prospects submit a form, human review queues introduce friction.

In this lab spike, we evaluated three architectures: pure conversational chatbot, unconstrained LLM summary, and deterministic rules paired with targeted enrichment.

2. What Was Built

A webhook listener that validates business domain records, cross-references enrichment APIs, and routes qualified leads directly to Calendly.

The pure LLM approach suffered from inconsistent latency (8-15 seconds) and occasional misclassification on non-traditional email domains.

The hybrid solution: deterministic criteria (verified email MX record, company headcount tier, clear budget trigger) evaluated in <200ms. High-fit leads are immediately redirected to calendar booking.

Unconstrained LLM prompts frequently miscategorized unusual company domain extensions (.so, .ai). We replaced free-form prompting with deterministic regex rules and verified DNS MX record checks.

If your organization is dealing with similar operational bottlenecks, explore our related engineering services or book an architecture audit.