AI & Agent Development
Once value is established, the focus moves from proving an idea can work to engineering a system the business can depend on.
The goal is an AI system that is accurate, reliable, secure, controllable, and commercially viable in the real world, not simply one that performs well in a demonstration.
Every system involves trade-offs between accuracy, latency, cost, privacy, control, and operational complexity. We make those trade-offs explicit and add intelligence or complexity only where it measurably improves the outcome.
Models and architecture
Teams starting with AI can use capable frontier models to establish a strong performance baseline quickly. From there, we evaluate whether parts of the system can move to smaller, open-weight, specialised, or purpose-built models while preserving quality.
Models are benchmarked against the actual work they need to perform. Complex reasoning may justify a frontier model, while classification, extraction, routing, or repetitive domain-specific tasks may be handled more efficiently by smaller specialised models.
Inference economics are considered from the beginning. Model choice, reasoning depth, token usage, caching, context size, and the number of calls materially affect latency and cost at scale.
Where measurable improvement requires it, our work can extend into fine-tuning, post-training, distillation, reinforcement learning, or other specialised model development.
Context, data and retrieval
A model is only as useful as the context available when it has to understand, decide, or act. That context may come from documents, databases, APIs, applications, real-time data, task state, retrieval systems, or memory.
The challenge is giving the model the right information, at the right time, with the right permissions. We design around relevance, freshness, access control, reliability, and efficiency.
Information is retrieved, filtered, compressed, cached, summarised, or discarded where appropriate. For longer workflows, we distinguish temporary task context, persistent state, and long-term memory so the system retains what matters without accumulating stale information.
Workflows and agents
Not every AI problem needs an agent. Some are best solved through deterministic workflows where AI performs defined tasks inside a structured process. Others require agents that reason across multiple steps, use tools, maintain state, and adapt to changing conditions.
When agents are appropriate, we engineer the harness around the model: context, tools, state, memory, routing, validation, retries, fallbacks, permissions, execution limits, model routing, latency thresholds, token budgets, observability, and human escalation.
Business rules, permissions, validation, and high-consequence actions can remain deterministic while AI is used where reasoning, interpretation, or adaptability creates value. The goal is not maximum autonomy; it is the right level of autonomy for the consequence of the task.