Your external teamfor AI engineering

We set the expected volume, the target cost per request, and the reliability metric before any code is written. That turns viability into an engineering decision instead of a hunch: you get an explicit go / no-go, a recommended architecture, and the data you will actually need. If cost per request will not survive traffic growth, better to know before building.

Automatic evals, a golden set, human review, and guardrails with fallback when the model fails. On the multi-agent system for expert legal reports we added a double anti-hallucination layer: deterministic rules for dates and inconsistencies against the medical assessment scale, plus automatic evaluation against real reports, section by section. Tracing, logs, and alerts run from day one.

Cheap routes, caching, batching, mixed models routed per task, and per-feature limits. The order matters: you do not cut costs by picking the cheap model, you cut them once your evaluation system proves the cheap one breaks nothing. That is how the legal report system reached −42% cost per report with no loss of precision, and how document classification held 95.5% top-5 accuracy with 17% fewer LLM calls.

Data classification, PII redaction, access policies, and full traceability. When requirements demand it, the deployment goes on-premise: that is how we built Irontec’s call-analysis platform, where speech engine and model choices were driven by privacy constraints, with asynchronous workers and Kubernetes. We also run on-premise AI in regulated environments such as ENS and the EU AI Act.

AI Gap Analysis is one context meeting, a written report, and a second meeting to walk through it: recommended stack, the data and datasets you will need, predictable risks, a phased roadmap, and a cost baseline. Agent MVP adds the first working version of the agent, a golden set, quality checks and monitoring, and an explicit guide to what is left for production.

Yes. We work with your product and engineering teams on the stack you already run, not a new one, and close with a technical handover and documentation so the system stays operable without us. With Qamarero it took several months of joint work to get the voice agents wired into their CRM and table management, running in production.