Your AI is in production. Now make it pass audit, and pay for itself.
We help fintech and financial-software companies already running AI in production tame the operational reality: inference cost growth, latency under transaction load, silent model drift, and the model-risk and SOC 2 observability gaps that turn into findings at your next audit.
What operational trouble actually looks like.
The signs that a fintech AI feature has crossed from “demo working” to “operational, and regulatory, problem” are usually clear once you know what to look for.
Five lights. One pattern. We exist to turn them green. →
How we work.
A structured audit-then-implement cycle that doesn’t end at delivery, because production AI in a regulated stack never does. As models drift, volume grows, and the next exam approaches, the loop runs again.
Production audit
We inventory every running AI feature, its cost, latency, drift posture, reliability incidents, and model-risk evidence gaps, so the optimization work starts from evidence, not guesswork.
What you walk away with.
Concrete audit reports, measured optimizations, and the observability and evidence infrastructure to keep what we build running, and defensible.
Production AI at scale, the engineering pattern that ships.
A measured outcome from the same operational discipline we bring to fintech AI under real load and real scrutiny.
An enterprise SaaS provider needed a production-grade RAG and vector pipeline serving thousands of in-app AI features, with strict latency SLAs and predictable cloud spend.
An MCP-based retrieval pipeline with a tuned vector store, caching and batching for inference-cost control, and observability for query latency and recall.
Production reliability was our discipline long before AI made it urgent.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where production reliability wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Real-time interactive systems, AR/XR experiences, and integration with complex existing infrastructure.
That discipline, observability, integration rigor, real SLAs, audit trails, and operating under unpredictable load, is exactly what production AI demands once it’s live inside a regulated financial stack. Most AI teams learn it the hard way, in production, the week before an exam. We brought it with us.
