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Customer Story: The Regis Company

· 3 min read
Amelia Wampler
Amelia Wampler
Co-founder & COO, Limitr

The Regis Company builds immersive, case-based training simulations for enterprise learning programs, including AI avatars that learners talk to in real time. Ahead of a fixed-price rollout to a Fortune 500 customer covering more than 100,000 users, Regis risked carrying the cost of any AI usage the contract didn't price in. They chose Limitr to meter and enforce that AI usage inside their existing backend, and to open a path to usage-based billing on the contract.

  • Industry: enterprise learning and training simulations
  • Use case: metering and enforcing real-time AI avatar, chat, and text-to-speech usage across learners, authors, and facilitators
  • Scale: a Fortune 500 contract covering more than 100,000 users
  • Why Limitr: faster to production than building in-house, without changing how Regis's product works for its users

The Company​

The Regis Company is an enterprise learning platform that helps organizations build immersive, case-based training simulations — including AI-driven avatars that learners interact with in real time as part of their programs.

The Challenge​

Real-time AI avatars are a compelling experience for learners, and a variable cost for the platform that runs them. A major enterprise deal — a contract with a Fortune 500 company covering more than 100,000 users — was about to go live on a fixed-price basis, and without metering and enforcement in place, Regis risked being on the hook for all AI usage above what the contract priced in. Their engineering team considered building tracking and enforcement in-house, but needed something production-ready fast enough to avoid the exposure.

Why Regis Chose Limitr​

  • Speed over building in-house. Regis needed a usage-tracking and enforcement layer live before their next enterprise rollout, and didn't have the time to build and harden one internally.
  • Fit for a complex, multi-level consumption model. Regis needed to track usage across multiple types of users (learners, authors, facilitators) and multiple AI providers and modalities (chat, text-to-speech, and real-time avatar rendering) — not just a single metered API.
  • Flexible, non-prescriptive architecture. Regis wanted to avoid a rigid platform that would force changes to how its own product worked for its users. Limitr's policy-based approach — building enforcement into Regis's existing backend rather than routing users through a separate UI — let Regis keep full control of the end-user experience while still getting granular tracking and real-time enforcement.
  • Path to variable, usage-based pricing. Limitr gives Regis a way to move their enterprise contracts from fixed-price arrangements to accurate, consumption-based billing once tracking is in place, so AI usage can become something they bill for rather than absorb.

The Decision​

Regis's engineering and business leadership moved quickly once they saw the platform: rather than spend cycles building a custom tracking and enforcement layer, they saw Limitr's real-time, policy-based approach as a purpose-built fit for exactly the problem they were about to face at scale. Limitr's architecture was flexible enough that it didn't ask Regis to compromise the end-user experience. That combination of speed, flexibility, and a clear path to monetization made the decision straightforward.

See What Limitr Can Do for You​

If you're absorbing AI consumption costs with no way to track or bill for them, Limitr can help you get ahead of it — before your next big rollout puts you on the hook. Book a call to see how it fits your use case.