AI-Native Software Built by Engineers Who Use AI Every Day

We instrument every LLM call with cost tracking, implement caching for repeated queries, tune context window usage, and use cheaper models for low-stakes tasks in multi-model pipelines. Most clients end up with AI feature costs 40–60% lower than their initial estimates.

We use RAG to ground responses in your verified data, add structured output validation, implement self-consistency checks for high-stakes outputs, and build evaluation pipelines that measure accuracy on your specific use case. Hallucination mitigation is a first-class engineering concern for us, not an afterthought.

For sensitive data, we anonymise inputs before sending to external APIs, evaluate self-hosted or on-premise models, and review each provider's data retention policies with you. For healthcare and finance clients we default to private deployment unless a public API explicitly meets your compliance requirements.

We design for perceived performance: streaming responses, progressive rendering, async processing where real-time is not required, and optimistic UI patterns. We also benchmark every integration at build time and set SLOs before going to production.

Rarely — fine-tuning is expensive, requires significant labelled data, and often underperforms well-engineered RAG + prompting. We recommend fine-tuning only when you have thousands of high-quality examples and a task that genuinely cannot be solved with prompting. We will tell you honestly if you do not need it.

We build evaluation pipelines that run your test suite against every new prompt version or model update. You get a dashboard showing accuracy trends over time. Model or prompt changes only deploy when they pass the evaluation bar.

AI systems require ongoing attention — model providers update APIs, accuracy drifts as the world changes, and new models offer better price-performance. We offer an AI maintenance plan that covers monitoring, prompt iteration, model upgrades, and evaluation pipeline maintenance on a monthly basis.