Get your AI pilot to reliably run in production.
AI Systems & Agent Engineering
Most AI pilots never reach production. We engineer agentic and LLM systems that run reliably in live operations — with evaluation, governance, and a human in the loop where it counts.
What you get
- AI production-readiness assessment: where your pilot breaks, and the shortest path across the gap
- Production-grade agent and LLM systems — tool use, orchestration, retrieval, and state you can operate
- Evaluation harnesses and regression suites so quality is measured, not hoped for
- Guardrails, human-in-the-loop checkpoints, and governance for safe autonomous operation
- Cost, latency, and reliability engineering for models running under real load
Questions we get
Why do most AI pilots fail to reach production?
Industry surveys through 2025–2026 show roughly 79% of organisations have adopted agentic AI in some form, yet only about 11% run agents in production. Pilots stall on data foundations, the absence of evaluation, weak governance, and unclear operational ownership — not on the model itself. Crossing that gap is an engineering problem, and it is the work we specialise in.
Do you build agents from scratch or work with existing frameworks?
Both. We design multi-agent orchestration, MCP-based tool integrations, and retrieval systems, and we are pragmatic about frameworks — we choose the approach that is operable and testable for your team, not the most fashionable one.
How do you make an AI system trustworthy enough for production?
By treating evaluation as a first-class deliverable: explicit success criteria, regression suites, guardrails, human-in-the-loop checkpoints for high-stakes actions, and observability so behaviour can be audited after the fact.
Start here
Have a system that needs to be right?
Book a free 30-minute scoping call. We will tell you honestly whether we are the right firm for the problem — and what the shortest credible path looks like.