Governed AI
Ground AI applications and agents in data the organization already trusts.
Enterprise AI is not a model demo. It is retrieval, features, permissions, evaluation, and serving on top of a lakehouse the business would already use for decisions.
If you would not put the data on an executive dashboard, do not put it in an agent.
AI programs stall when they are built beside the data platform instead of on it. Permissions are copied. Documents are ungoverned. There is no evaluation loop. Production never arrives.
Context is unofficial
Prompts are stuffed with extracts that have no owner, lineage, or refresh policy.
Agents ignore identity
A service account can see more than the user invoking the system.
No production standard
Quality is anecdotal. Cost is unattributed. Rollback is undefined.
Outcomes
AI on governed data products
Tables, documents, and features published through Unity Catalog.
Agents with authorization
Tool use and retrieval respect the same identity model as analytics.
An evaluable system
MLflow, tracing, and monitoring exist before broad rollout.
How the architecture works
01
Trusted context
Gold data products and governed indexes—not a parallel document swamp.
02
Runtime
Model Serving, Agent Bricks, and application boundaries with explicit tools.
03
Controls
Evaluation, tracing, cost, and the ability to disable a workload.
What we implement
- Enterprise RAG architecture
- Feature and context design
- Agent architecture
- MLflow evaluation and tracing
- Model Serving
- Unity Catalog for AI assets
Where this shows up
Internal knowledge assistants
Answers grounded in governed operational and analytical data.
Predictive + generative systems
Classical ML scores combined with governed language interfaces.
What should be measured
The business case is built on a baseline, not a promise. These are the numbers this solution is accountable to.
- Task completion quality against a human baseline
- Time saved per workflow instance
- Escalation and override rates
- Cost per resolved task
The first sensible pilot
One governed assistant with a narrow job
One workflow, one user group, governed context, human approval on actions, and an evaluation loop — measured against how the work is done today.
Questions
Do we need a large language model project to start?
No. Many high-value systems are predictive models or retrieval over governed data. Language interfaces are added when the context is already trustworthy.
Will you claim accuracy percentages?
No. Published vendor benchmarks are not our case studies. Evaluation is designed per system, with the organization's data and risk tolerance.
Related
AI & ML
How to Prepare Enterprise Data for AI Agents
Agents inherit whatever you give them: permissions, definitions, and quality. Preparing data for agents is lakehouse work, not prompt work.
10 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused