Enterprise AI

We build AI systems that hold up in real environments, where accuracy matters and data is sensitive. We start with the outcome you need and design the data flows, retrieval, models, guardrails, and operational controls that make the solution trustworthy, fast, and cost-effective. Our focus is always on practical value: internal assistants, document automation, classification and summarisation, and retrieval-augmented search over your own knowledge. Wherever your data sits, whether on-prem, in a VPC, or across existing systems, we integrate with it rather than creating new silos.

Good answers depend on good retrieval, so we design chunking, metadata, and embeddings for your content, choose the right vector store, and add caching, freshness rules, and fallbacks so results stay consistent. Quality and safety come baked in. We run evaluation harnesses with golden datasets, track offline and online metrics, version prompts, apply guardrails and policy checks, and redact sensitive data with protection against injection and leakage.

Operational reliability shapes everything. We design for latency, throughput, and cost from day one, with observability for tokens, latency, and quality signals. We support routing and graceful degradation across providers and keep dependencies swappable so you’re never locked in. When the work calls for MLOps, we provide it: dataset and prompt versioning, experiment tracking, model registries, and automated train-evaluate-deploy pipelines. Fine-tuning and distillation are used when they pay off and avoided when they don’t.

We deliver in thin, measurable slices. Each increment ships usable value, includes evaluation and monitoring, and reduces uncertainty so you can scale with confidence.