Production AI engineering
Getting an AI system to work once is not the same as running it in production. We add the evaluation, monitoring, cost controls, and provider resilience that keep a system dependable under real load.
What production AI engineering involves
A prototype proves an AI system can work. Production requires it to keep working: on inputs nobody tested, under load, through model updates and provider outages, at a cost that stays inside budget. The gap between the two is mostly unglamorous engineering, and it is where many AI projects stall.
We start by measuring. An evaluation set built from real inputs tells you how good the system is today and catches regressions when anything changes. Production monitoring tracks quality signals, latency, error rates, and cost per request, so drift is noticed on a dashboard rather than by a customer. With measurement in place, improvements become safe to make.
Then we harden. Guardrails check inputs and outputs for the failures that matter in your domain. Failure handling and human escalation cover what automation should not decide. Model routing and fallbacks let the product move to another provider when one has an outage or changes its price. Caching and right-sized models bring cost under control without giving up quality.
Problems we solve
- A working prototype has no evaluation, monitoring, or cost controls.
- Quality drifts and no one notices until a user complains.
- A single model provider is a single point of failure.
What's included
- Model and prompt evaluation
- Cost and latency optimisation
- Guardrails
- Monitoring and failure handling
- Security
- Human escalation
- Model-provider resilience
How we approach it
- 01
Evaluate
Build model and prompt evaluation against real inputs.
- 02
Instrument
Monitor quality, latency, and cost in production.
- 03
Harden
Add guardrails, failure handling, and human escalation.
- 04
De-risk
Add provider resilience so the product does not depend on a single vendor.
Decisions that matter
Measure before optimising
Cost and latency work without evaluation is guesswork, because it is easy to make a system cheaper and quietly worse. Every optimisation is checked against the evaluation set before it ships.
Guardrails that fit the risk
A copywriting tool and a system that touches customer accounts need different safeguards. We identify the failures that would actually cause harm and put checks there, rather than adding generic filters everywhere.
No single point of failure
Model calls sit behind a provider-agnostic layer with routing and fallbacks, so the system can fail over during an outage and move to a better or cheaper model without a rewrite.
When it is the right fit
A good fit when
- An AI feature is live or about to launch, and you have no systematic way to measure its quality.
- Inference cost or latency is growing faster than usage or revenue.
- The system depends on a single model provider with no fallback.
Probably not when
- The AI system is still an idea. Start with an AI MVP to validate it before hardening it.
Representative technologies
Production AI FAQs
How do you know if quality is dropping?
What if a model provider has an outage?
Can you reduce our inference costs?
What does an evaluation set look like?
Can you work on a system another team built?
Have a project that needs Production AI?
Tell us what you are building. We will show you what it takes to ship it to production, and reply within one business day.