Beyond demos.
AI that ships.

Yes. We support retrieval-augmented generation (RAG) on your private corpus, parameter-efficient fine-tuning (LoRA, QLoRA), and full fine-tunes on dedicated infrastructure when scale or latency requires it. We also build evaluation harnesses against your data so you can see quality before, during and after training, not just after deployment.

Layered defences: input sanitisation and structured prompts, allow-list tool calling, output validators, sandboxed execution for code-running agents, separate trust zones for retrieved content vs. user input, and red-team evals run nightly against known attack patterns. Critical actions always require human-in-the-loop confirmation.

You swap it in. Every system we ship is model-agnostic by design: the orchestration layer routes to whichever model wins your evals, and the application layer never knows which one ran. Migrations that take other teams months are a config change for ours.

Always. We sign mutual NDAs and DPAs before any sensitive data flows. AI inference can run in your tenant, in a dedicated VPC, on private endpoints (AWS Bedrock, Azure OpenAI, Vertex AI), or on self-hosted open models whatever your residency and compliance posture requires. We never use customer data to train shared models.

Eval-driven from day one. Every capability ships with a graded evaluation suite typically 200–500 cases covering happy paths, edge cases, hostile inputs and known failure modes. Evals run nightly on real production traffic samples. Quality drift triggers alerts; no model promotion happens without passing the latest eval set.

Integrate, almost always. Most engagements start by adding AI capabilities to systems you already run ERPs, CRMs, support stacks, BI tools, internal apps through APIs, SDKs or embedded widgets. A full rebuild only makes sense when the underlying system is itself the bottleneck, and we will tell you honestly when that is the case.