Atlas is an Outcome Verification and Attribution tool for AI products, with the core goal of mapping the outcomes delivered by AI products to the agent actions and workflows that drive those outcomes.
Main problems it solves:
- Outcomes are presented only as totals: you see just a count each month, know the number is moving, but do not know why.
- Attribution relies on guesswork: which agent step, workflow, or model change affected a metric often exists only in someone's memory or in notes that are never rerun.
- Pricing decisions lack a basis: without knowing what is producing outcomes, you cannot judge which outcomes are worth pricing, nor can you defend your pricing.
How to use it:
- Add a lightweight Atlas snippet in a sandbox environment.
- Import relevant product, agent, CRM, and analytics data.
- Set the outcomes you want to track and the evidence supporting those outcomes.
- Break down attribution by workflow, segment, and release version.
- Start in the sandbox; Atlas is designed to support testing and learning before going live in production.
Target scenarios:
- Teams that want to understand which parts of an AI product truly produce outcomes.
- Teams already using outcome-based contracts in the market, who want clarity on what they are charging for and want to validate through data before proceeding to formal deployment.
Other details: the data shown in the materials is for illustrative purposes. Regarding the specific supported platforms, integration methods, pricing, availability, etc., the materials do not currently provide this.
