Production AI breaks at the control layer.
Most AI failures in production are not about the model alone. They come from weak controls around the model: prompts buried in code, tools with broad access, no budget limits, poor traces, and no evals to catch regressions.
Model drift becomes an outage
When providers deprecate models or change behavior, hard-coded systems break slowly and expensively.
Tool sprawl creates risk
As MCP servers, APIs, and internal tools pile up, permissions and authentication drift out of control.
Runaway costs show up too late
One bad loop or fan-out path can turn into a surprise bill before anyone notices.
Bad outputs are hard to debug
Without traces, you cannot see what happened across prompts, models, tools, and user context.
Quality slips after release
If you are not running evals before and after release, users become the test suite.
The seven control layers of production AI.
Every engagement uses the same seven-layer framework. It gives you a practical way to design, review, and harden a production AI system.
How we work.
We help at four levels: audit, harden, build, and operate.
What you get.
Every engagement is tied to the same production standard. You get a clear baseline, targeted fixes, and an operating model your team can keep using after delivery.
Who we work with.
We work with product teams, SaaS companies, AI-native startups, internal platform teams, and regulated environments. The common thread is simple: the system has to work for more than one user, and failure has a cost.
See where your system breaks before users do.
Take the AI Production Scorecard to get a fast baseline across the seven layers. If you want a deeper review, book an architecture session and we will turn the score into a hardening plan.
analyzing 7 layers...
✓ produces: overall score
✓ produces: layer breakdown
✓ produces: critical flags
✓ produces: next-step recommendation