AI Optimisation
Improving accuracy, reducing latency, and cutting API costs on AI integrations that are already live.
Most AI integrations degrade over time or cost more than they should — not because the model got worse, but because the prompts were designed for a prototype and never revisited, the caching layer was never added, or the error handling lets bad outputs through without flagging them. Optimisation work fixes the integration, not the model.
Accuracy optimisation starts with a structured evaluation against a sample your team has labelled. From there we identify whether the issue is prompt design, model selection, context quality, or post-processing — and fix the right thing rather than the most obvious one.
Cost optimisation typically involves caching responses for repeated inputs, batching requests where latency isn't critical, right-sizing model selection (a smaller model often performs identically on structured tasks), and adding token budgets to prompts that are currently unconstrained.
What we optimise
Accuracy
Evaluated against your own labelled samples — prompt, model, context, or post-processing fixed at the source.
Latency
Response time reduced through caching, streaming, and request parallelisation.
API Cost
Usage reduced through caching, batching, right-sized models, and token budgets.
Reliability
Error handling and fallbacks that stop bad outputs reaching your users.