Your AI features are shipping. Now make them work at scale.
We help ManTech SaaS companies already running AI in production tame the operational reality: latency at the edge, cost growth, reliability under factory-floor conditions, and the observability gaps that erode customer trust.
What operational trouble actually looks like.
The signs that a ManTech AI feature has crossed from “demo working” to “operational 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 never does. As you scale to more customers and more features, the loop runs again.
Production audit
We inventory every running AI feature, its cost, latency, reliability incidents, and customer feedback, so the optimization work starts from evidence, not guesswork.
What you walk away with.
Concrete audit reports, measured optimizations, and the infrastructure to keep what we build running.
Production AI at scale: the engineering pattern that ships.
A measured outcome from the same operational discipline we bring to ManTech AI.
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, and operating under unpredictable load) is exactly what production AI demands once it’s live. Most AI teams learn it the hard way, in production. We brought it with us.
