- 58% faster dev cycles
- 26% lower token spend
- 47% faster processing
Production AI engineering
AI systems that hold up in production.
Architecture, implementation and evaluation for software teams shipping real AI features.
When the prototype works but production exposes problems with reliability, cost, latency, retrieval, memory or integration, we diagnose the system, measure what is failing and build the fix.
Track record
Work that shipped.
Selected engagements from the founder’s record.
- 89% lower pipeline cost
- 270% faster execution
- 70% faster transcription
- $250k Series A secured
What we do
Evaluation, release gates and cost attribution wired into your existing stack.
Your AI works in the demo. Production is different.
AI systems rarely fail for one reason. Quality degrades across retrieval. Costs grow with context. Latency compounds across model calls. Memory becomes inconsistent. Edge cases appear that nobody tested.
We work on the engineering underneath the AI feature — so your team can understand what is happening, measure whether it is improving, and operate it reliably at production scale.
Need development without client data leaving your environment? We can scope that as an optional add-on, not the default. Local compute is limited, so feasibility and cost depend on the workload.
Failure modes
Where AI systems break.
Start small, prove it, then scale what works.
Every engagement can begin with a focused technical step before a larger implementation. The aim is to establish what is failing, why it is failing, what should change and what the next stage will require.
01Assess
Architecture diagnosis.
02Validate
Focused technical sprint.
03Build
Production implementation.
04Optimize
Quality, latency and cost.
05Embed
Specialist or fractional support.
Start with a technical scope, not a six-month commitment.
Tell us what is not working, what you have already built and what the system needs to do. We will scope the engineering problem around the evidence available.
The first step should make clear: what is failing, why it is failing, what should change, how success will be measured and what implementation is likely to take.
How we engineer production AI.
Measure before optimizing
Know what quality, latency and cost look like before changing the system.
Design for failure
Production systems need explicit handling for edge cases, regressions and degraded model behavior.
Keep the system inspectable
Teams should be able to understand what the system is doing and where it is failing.
Earn complexity
Use the simplest architecture that gives the reliability and control production requires.
Industries
Where AI meets running systems.
Have an AI feature that needs to work in production?
Tell us what isn't working. We'll help scope the engineering problem, identify the risks and define a concrete first step.