Capability
The unglamorous work that keeps AI alive
Observability, LLM drift tracking, cost optimisation, and enterprise security governance for production AI
Observability, LLM drift tracking, cost optimisation, and enterprise security governance — the plumbing that turns a working prototype into a system you can trust on a Friday afternoon.
Agent traces, latency logging, drift detection, retraining cadence, spend monitoring, incident response. None of it demos well. All of it decides whether your AI survives past launch week.
LLM & Agent Observability
Prompt, token, latency, and quality telemetry with per-trace breakdowns, so regressions surface fast and you can prove where the time and money went.
Drift Tracking & Cost Control
Model drift scoring alongside caching, routing, and right-sized model selection to keep inference budgets predictable as usage grows.
Enterprise Security Governance
Access controls, audit trails, data-residency boundaries, and PII handling policies applied across every AI surface in production.
Our approach
From audit to ROI in 3 phases
Discover & Audit
Inventory of agents, LLM calls, prompts, and dependencies, with baseline latency, cost, and quality benchmarks for every production surface.
Build & Integrate
Observability, drift detection, eval suites, and CI/CD for agents wired into your incident workflow and on-call rota within 90 days.
Ship & Measure
Continuous cost and quality optimisation with quarterly reliability reviews and budget guardrails reported by team.
Deliverables
ROI snapshot
Track cost, latency, and model drift past launch week — not just on launch day
Inference cost
MTTR on AI incidents
Quality regressions caught pre-prod
More capabilities
All capabilitiesManaged Coding
Custom software, web and mobile applications, API integrations, and ongoing development delivered as a managed service
Data Engineering
Enterprise data pipelines, data warehousing, BI dashboards, and scalable analytics platforms built for growth
AI & Automation
LLM integrations, custom AI agents, workflow automation, and intelligent tools tailored to your operational processes
Context from AI History
The ideas behind AIOps & Reliability
Agents in Production
AI stopped answering questions and started completing tasks; the demo-to-production gap was all about failure handling.
Read the story2006CUDA
NVIDIA built CUDA to sell gaming GPUs; it became the infrastructure of AI.
Read the story2020Scaling Laws
A 2020 paper found that intelligence follows a power law, and you could budget for it.
Read the story