Measured improvements in resolution time, deflection and analyst productivity
Predictable unit economics per inference, per document and per call
A reusable retrieval layer that remains valid across model upgrades
We open with a partner-led discovery - one week of interviews, data-room reads and stakeholder mapping. The output is a one-page thesis, not a deck.
Three to four weeks of solution design: architecture, control catalogue, build plan, success metrics, change posture. Reviewed with your model-risk, security and finance leads.
Joint Moweb-and-client pods deliver in 2-week iterations, with hard exit criteria for each release. Audit-pack evidence accumulates with every PR.
We co-run the system through the first three quarters. Hand-over is a transfer of practice, not just code.
We are model-agnostic. Engagements run on Azure OpenAI (GPT-4.1, o-series), Anthropic Claude (Opus and Sonnet), Google Gemini, AWS Bedrock and open-weight models on Llama and Mistral families. Selection follows accuracy, latency, cost and data-residency requirements - not loyalty.
Every system we ship has a closed-loop evaluation harness with regression suites for hallucination, citation accuracy, bias and PII leakage. We pair this with retrieval guardrails, structured output schemas, human-in-the-loop checkpoints and rate-limited fallback paths.
The lakehouse, contracts and lineage your AI roadmap silently depends on.
Operating model, controls and assurance for AI under the EU AI Act, NIST and ISO 42001.