Autonomous Revenue Ops β€” System Card

This repository is a system/model card for an AI-assisted revenue operations architecture. It does not claim to contain a newly trained foundation model.

Purpose

The system evaluates structured lead qualification output and applies deterministic authorization policy before any business action is allowed.

Intended use

  • revenue operations automation
  • CRM triage and lead routing
  • agentic workflow evaluation
  • human-in-the-loop approval design
  • portfolio/reviewer demonstrations of production AI controls

Decision boundary

AI may propose qualification fields such as score, confidence, ICP fit, intent, urgency, evidence, and risk flags. Deterministic software decides whether the system may auto-route, requires human review, needs more evidence, enters nurture, or blocks outreach.

Limitations

The public proof uses synthetic inputs and simulated external side effects. It is not evidence of live customer production performance. Production validation requires real CRM integrations, authentication, monitoring, incident handling, privacy controls, and measured operational outcomes.

Evaluation

The companion dataset contains synthetic regression cases covering autonomous routing, human review, low-confidence research, risk overrides, consent blocking, and nurture behavior.

Five-level production-AI proof

This project now reports proof using:

L1 Runnable β†’ L2 Reproducible β†’ L3 Capability-Validated β†’ L4 Production-Candidate β†’ L5 Production-Validated.

The GitHub repository computes the level with make proof. The public Hugging Face Space, Dataset and system card support the inspectable capability layer, while the project intentionally caps its public claim at L3 β€” Capability-Validated until live CRM operations and target-environment recovery evidence exist.

Specification: https://github.com/h00w/autonomous-revenue-ops/blob/main/PROOF_MODEL.md

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