ModelRefs / Prospect Qualification — Canonical Workflow

Prospect Qualification — Canonical Workflow

Prospect Qualification: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.

Overview

Prospect qualification applies a reasoning model to inbound lead records, scoring each against ideal-customer-profile dimensions such as company size, tech stack, buying authority and urgency signals. The model produces a structured scorecard with a verdict and per-criterion explanation, so reps can triage instantly and managers can audit decisions. Disqualified leads receive a hold reason; qualified leads are enriched with suggested talk tracks and routed to the assigned rep with an auto-brief on why they fit.

Implementation profile

Categoryreasoning-models
Implementation maturityproduction
Primary use casesreasoning, extraction
Deployment optionsmanaged-api, hybrid
Architecturesserverless-api, managed-container, hybrid-private-cloud

Candidate models with published references

Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.

Benchmarks relevant to this workflow

  • Chatbot Arena ELO — LMSYS crowd-sourced pairwise preference leaderboard across open and closed models.
  • MT-Bench — LMSYS multi-turn chatbot benchmark across 8 categories, GPT-4 graded 1–10.
  • MMLU — Massive Multitask Language Understanding — 57 academic and professional subjects.

These are the benchmarks whose task shape bears on this workflow. Each one only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Prospect Qualification — Canonical Workflow.