Engineered capability
AI Business Systems
Operational AI embedded directly into the workflows where decisions are actually made — engineered for production, not for demo theatre.
02 — Engineering practice
How we engineer it.
Operational AI is not a feature you add to a system. It is a discipline that has to be engineered into the operating model — into the data, the workflows, the controls, and the audit trail. We design AI systems that run in production under the same rigor as the rest of the platform.
Our engagements begin where decisions live: clinical triage, financial underwriting, supply allocation, sports tactical analysis. We map the decision surface, model the operational constraint, engineer the inference path, and instrument every prediction for explainability and continuous evaluation.
We treat the model as one component among many — surrounded by data quality monitors, drift detectors, feedback loops, and human review where regulation or risk demands it. The result is AI that is auditable, recoverable, and accountable, with the operational maturity to run continuously under SLA.
03 — Engineering surface
The technologies behind this capability.
A selection of the platforms, frameworks, and tools our principals engineer with in production engagements.
Models & Providers
- Anthropic Claude
- OpenAI
- Open-weight LLMs
- Hugging Face
- Custom PyTorch
Orchestration & RAG
- LangChain
- LlamaIndex
- Vector stores (pgvector, Pinecone)
- Temporal
- Ray
Production Surface
- MLflow
- Weights & Biases
- Grafana / Prometheus
- Evidently
- OpenTelemetry
Engage on this capability
Scope this capability against your operating model.
Briefings are scoped within five working days and led by a senior architect.
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