Capabilities
Production AI
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.
01
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.
02
What's included
- Model and prompt evaluation
- Cost and latency optimisation
- Guardrails
- Monitoring and failure handling
- Security
- Human escalation
- Model-provider resilience
03
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 no single vendor can take you down.
04
Representative technologies
Evaluation frameworksObservabilityGuardrailsCachingModel routingCloud infrastructure
05
Frequently asked
- How do you know if quality is dropping?
- We instrument the system with evaluation and monitoring so drift is caught by measurement, not by complaints.
- What if a model provider has an outage?
- We design for provider resilience with routing and fallbacks so the system keeps working if one provider fails.
- Can you reduce our inference costs?
- Often, yes. Caching, routing, and right-sizing models usually make cost predictable without hurting quality.
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.