AI is live across the firm. Now make it dependable.
We help consulting firms already running AI inside the practice tame the operational reality: spend that outruns billable value, drafting tools that drift off-house-standard, knowledge systems that surface stale answers, and the governance gaps that put partner trust and client confidentiality at risk.
What operational trouble actually looks like.
The signs that AI inside a firm has crossed from “promising pilot” to “operational problem” are usually clear once you know what to look for: in the spend, the work product, and where your people’s hours go.
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 AI in a live practice never does. As you roll it across more teams and into more client offerings, the loop runs again.
Practice audit
We inventory every AI tool live in the firm: its spend, the quality of its output, the data it touches, and where it sits relative to governance, so the optimization work starts from evidence, not anecdote.
What you walk away with.
Concrete audit reports, measured optimizations, and the governance to keep what we build dependable across the firm.
AI at scale, governed: the engineering pattern that ships.
A measured outcome from the same operational discipline we bring to AI inside a consulting firm.
An enterprise SaaS provider needed a production-grade retrieval pipeline serving thousands of in-app AI features, with answers tied to current source material 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 answer 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 reliability and reputation weren’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, governance, real quality bars, and operating under demanding stakeholders) is exactly what AI inside a consulting firm demands once it touches client work. Most teams learn it the hard way, after a draft goes out wrong. We brought it with us.
