Stop bad data from producing bad decisions — for humans and AI.
Your team debates whose numbers are right. Your AI models train on stale snapshots. Your analysts wait days for data engineering to build a new report. We fix the foundation: one lakehouse, one semantic layer, automated quality checks, and self-service BI that both humans and machines trust.
Microsoft Fabric, Power BI, Purview, Azure Data Factory, dbt.
What’s included
Lakehouse architecture
A Fabric OneLake lakehouse with medallion layers (bronze → silver → gold). Clean, versioned, queryable data — not a dump of CSVs in blob storage.
Automated data pipelines
Ingestion from your source systems with incremental loads, error handling, and monitoring. When a pipeline fails, you know within minutes — not when someone complains about a stale dashboard.
Semantic layer
A Power BI semantic model with business-logic measures, relationships, and row-level security. One source of truth — your KPIs match whether you're in Power BI, Excel, or an AI agent.
Data quality rules
Automated checks for completeness, uniqueness, freshness, and referential integrity. Failures alert before downstream consumers see bad data. Typical catch rate: 90%+ of data issues within 15 minutes of occurrence.
Governance & lineage
Purview integration for classification, lineage tracking, and access controls. Know where every column comes from, who changed it, and who can see it.
Self-service BI
Reports and dashboards your team can explore and extend without filing a ticket. Fewer bottlenecks, faster decisions, and analysts spend time on analysis instead of waiting for data.
Why this is an AI prerequisite
Bad data in → bad AI out
RAG copilots and AI workflows are only as good as the data they access. Duplicate records, stale snapshots, and ungoverned schemas don’t just produce wrong dashboards — they produce wrong AI outputs that your team acts on.
One truth for humans and machines
When your Power BI revenue number matches what the AI agent computes, stakeholders trust both. A shared semantic layer eliminates the “my numbers don’t match” meeting that burns 2 hours every Monday.
Governance = speed, not bureaucracy
When data is classified, access-controlled, and lineage-tracked, teams self-serve safely. Less gatekeeping, faster iteration, lower compliance risk. Your security team says yes faster when they can see the controls.
Data & privacy
- Permissioning: row-level security in Power BI and workspace-level controls in Fabric ensure users see only what they should.
- PII handling: Purview auto-classification labels PII columns. Policies enforce masking or access restrictions by sensitivity level.
- Data boundaries: all data stays in your Fabric tenant and Azure subscription. We configure — we don't host.
Timeline & investment
Blueprint
10 days
Data audit + architecture
Build
4 – 10 weeks
Lakehouse + BI + governance
Investment
$30K – $120K
Depends on source count
What we need from you
- • Access to source systems (databases, APIs, file shares) and their schemas
- • A data steward or business analyst who can define key metrics and business rules
- • Fabric / Power BI Premium or F-SKU capacity (we help provision if needed)
- • Weekly 30-minute check-ins during the build phase
Security & guardrails your CISO will approve
Every AI system we ship includes these controls — in the first deploy, not a future phase.
Tool-call allowlists
The AI can only call tools you explicitly approve. Every external integration is registered with typed schemas — no unapproved operations, no unstructured side effects.
Schema-enforced outputs
Every response to a downstream system is validated against a JSON Schema before delivery. Malformed output is caught and logged, not silently propagated.
Eval suites in CI/CD
Regression tests, red-team prompts, and accuracy benchmarks run on every pull request. If eval scores drop below threshold, the merge is blocked.
Production observability
Latency P50/P95, token costs, error rates, and output drift — all in dashboards with configurable alerts. You see problems before users report them.
Human-in-the-loop gates
Configurable confidence thresholds route low-certainty decisions to a human reviewer before execution. The threshold is tunable without a code deploy.
Immutable audit trail
Every LLM call — inputs, outputs, token counts, tool invocations, cost, latency — is logged in an append-only store. Ready for compliance review or incident forensics.
Stop funding pilots that never ship.
A 10-day paid Blueprint gives you an architecture doc, risk register, costed backlog, and ROI model — artifacts you own and can act on immediately.
Get a 10-day paid BlueprintCedarNexus is an independent company and is not affiliated with Microsoft. Azure, Azure OpenAI, .NET, Microsoft Fabric, and Power BI are trademarks of Microsoft Corporation.