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Executive Summary

Many SaaS and platform companies adding paid AI features are leaking revenue because usage is hard to measure and attribute accurately across client environments, microservices, and hybrid/on‑prem deployments; instrumentation bugs, billing pipeline mismatches, and double‑digit API price changes all make reconciliation error-prone and create frequent disputes with customers and cloud providers. This problem is material for a large addressable cohort—roughly 100,000 companies—and leads directly to margin erosion, invoicing disputes, and failed audits when per‑use billing is the product model. You could build an enterprise-grade AI usage observability and billing reconciliation platform that combines lightweight SDKs and agents, an append‑only signed usage ledger (cryptographic attestations per request), and a reconciliation engine with pre-built connectors to billing systems (Stripe, Zuora) and cloud providers. Combine that with a dashboard for allocation and alerts, deterministic cost models, and an audit export so finance teams can settle disputes; priced around the $60K ACV implied by the market, it targets mid‑to‑large vendors who sell AI features per request. Timing is favorable: the market is ~ $6.0B (100,000 companies × $60K ACV), LLM monetization is driving more per‑use billing, model API pricing has proven volatile, and the move toward hybrid and on‑prem inference increases demand for consistent cross‑environment measurement and independent proofs. Those trends create both urgency and budgets for a reconciliatory layer that protects margins and simplifies vendor/customer trust. To stand out you will need provable, tamper‑resistant evidence (signed logs or hardware anchors), end‑to‑end tracing from client to model, and deep finance integrations so the product is usable in audits—not just dashboards. Challenges include getting broad instrumentation adoption, handling legacy architectures and privacy constraints, and convincing customers to trust an independent meter; success will hinge on a focused initial vertical (top 1,000 platform vendors) and a clear ROI playbook for recovering lost margin.

Executive Summary

Prevent AI revenue leakage with accurate, tamper-proof usage tracking

Executive Summary

Prevent AI revenue leakage with accurate, tamper-proof usage tracking

Executive Summary

Market Validation

Market Opportunity

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