AI Agents
Scoped AI agents with defined tools, least-privilege credentials, human approval on write actions and a full audit trail of every call.
AI & Data
Turn operational data into decisions, with governance designed in from the start.
The problem, in practice
A pilot worked on curated data and did not survive contact with the real thing.
The data exists across systems that do not agree on what a customer is.
Nobody can explain to an auditor how a model reached the decision it reached.
What we deliver
Enterprise AI & RAG
Retrieval-augmented generation over your own documents, with permission filtering at retrieval time, citations and an evaluation set your experts agree.
Computer Vision
Vision models on your camera and sensor feeds: annotation, edge inference, confidence thresholds, human review and drift monitoring after go-live.
Predictive Analytics
Forecasting and risk scoring on your own history, with point-in-time features, honest backtesting, drift monitoring and a benchmark against the simple method.
Data Platform & Analytics
Warehouse or lakehouse, tested transformations, a semantic layer and real governance, so reports agree with each other and every metric has an owner.
AIOps
Correlation, anomaly detection and noise reduction across metrics, logs and traces, with runbook automation kept under explicit human approval.
How you get it
AI & Data, delivered the way you need it
The same domain looks different depending on who runs it. Pick the delivery model that matches how your team is set up — each one is a real engagement, not a package name.
Technologies we work with
Categories, not logos. We name what we build with; we do not claim a partnership we have not signed.
- RAG
- LLM Orchestration
- Vector Databases
- MLOps
- Data Warehousing
- BI & Analytics
Questions we are asked
Do we have enough data?
Often the question is whether the data is consistent rather than whether there is enough of it. The assessment looks at that first, because volume rarely rescues a definition problem.
Can our data be used to train someone else's model?
Not without your written agreement, and the architecture is designed so it is a decision rather than a default. Where a third-party provider is involved, its data handling is part of what we put in front of you.
How do you know whether the system is good enough?
By agreeing what good enough means for the specific task before building, then measuring against it. A system evaluated only by whether it impresses in a demo has not been evaluated.
What if the answer is that AI is the wrong tool here?
Then we say so. A rules engine or a fixed report is frequently the correct answer, and reaching it in an assessment costs far less than reaching it in production.
Does this have to run in the cloud?
No. Where residency or sensitivity requires it, models can run in your own environment. That constraint changes the design and the cost, so it belongs in the conversation at the start.
Start with an assessment
The fastest way to a useful answer is a short, scoped look at what you already have.