AI & Intelligent Systems
The boring infrastructure that keeps AI impressive
The platform layer that keeps AI systems reliable, measurable, and affordable at scale.
AI that works in week one and degrades by month six wasn't engineered — it was demoed. Models drift, prompts rot, costs creep, and nobody notices until a customer does. MLOps is the discipline that makes AI a system instead of a stunt.
- AI releases gated by evidence, not vibes
- Degradation caught by dashboards before customers
- Unit economics that survive scale
Capabilities
What the work actually involves
LLMOps platforms
Prompt versioning, model routing, response caching, and rollout controls for LLM estates.
Evaluation & regression testing
Automated quality gates on every change — accuracy, safety, latency, and cost as first-class metrics.
Model serving & scaling
Inference infrastructure sized to your load curve, from serverless bursts to GPU fleets.
Drift & quality monitoring
Production behaviour watched continuously, with alerts before users feel the decay.
Cost governance
Token budgets, caching strategy, and model-tier routing that cut AI bills 30–70% without quality loss.
Before you ask
Questions about mlops & ai infrastructure
Next step
Bring us the problem. We will bring the architecture.
A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.
- No sales deck
- An engineer on the call, not an account manager
- NDA before you share anything