A business unit knows exactly what it wants to see. Turning that sentence into a semantic model, relationships, DAX measures, and a laid-out report takes a skilled analyst days, and there are forty requests in the queue. Describe the dashboard; get back a real Power BI file your team owns and edits.
▸ "Financial dashboard: revenue trends, expense breakdown, profitability by department."
✓ preflight · connection validated
✓ pii scan · 4 columns redacted
✓ schema hash · changed, proceeding
✓ schema analyst · 11 tables, 6 relations
✓ config generator · 3 pages, 14 visuals
✓ tmdl builder · deterministic
✓ layout builder · deterministic
↻ validator · SUM on text — repaired
✓ validator · 9 measures profiled
✓ packer · report.pbip
→ opens in Power BI Desktop
The deliverable
Most "AI dashboard" tools produce an image or a hosted view inside their own product. That is a demo, not an asset — your BI team cannot version it, extend it, or fold it into an existing workspace, and you are renting access to your own analysis.
The output here is a native Power BI project folder in Microsoft's own open formats. It opens in Desktop, lives in source control, and behaves like something an analyst built — because structurally, it is.
Inside the output
Semantic model — tables, typed columns, and the relationships between them, inferred from the real schema.
DAX measures — profiled against actual data samples before packaging, not merely syntactically plausible.
Report pages — charts, cards, tables, and slicers placed in a considered layout with a theme applied.
A version snapshot — so the next rebuild can diff structurally against this one instead of overwriting blindly.
0–0s
Typical end-to-end build
0
Connectors: 5 warehouses + 14 file adapters
0
Automated tests, zero inference cost to run
0
Self-correction loops before packaging
The pipeline
Inference decides what the dashboard should contain. It never writes the output format. Deterministic builders emit Power BI's model and report files, which is why the artifact is valid rather than approximately valid.
Correctness
A dashboard that opens with broken measures is worse than no dashboard, because someone has to debug generated DAX. Each loop validates a different failure class against the real data, and safe mode guarantees that a build always returns something valid.
LOOP 01
×2The model's read of your data structure is checked against the actual source: do these table and column names exist, are the key columns real, do the inferred relationships hold? A confident misreading of the schema poisons everything downstream, so it is caught first.
LOOP 02
×2The dashboard design is cross-referenced against the validated schema. Every table reference, every column binding, and every column named inside a DAX expression must resolve to something that exists. A visual bound to an imagined column never reaches the builder.
LOOP 03
×3DAX expressions are executed against real data samples, which is the only way to catch semantic errors that parse perfectly — a sum over a text column, a divide against a column that is zero in production. Three retries, then safe mode.
If all three retries on the validator loop are exhausted, the system does not return a broken file and it does not return an error. It generates a minimal dashboard — cards and tables only, no charts — that is guaranteed to open and be correct. A user always gets something they can work from, and the failure is logged for a human to look at rather than silently swallowed.
Sources
Enterprise data is never in one place. Anything not on this list is a documented adapter away — the interface is small and deliberately boring.
Credentials are encrypted at rest before they touch the database, never stored in plaintext configuration.
The awkward sources matter most — the finance workbook and the quarterly PDF are usually where the real reporting lives.
The difference between a script and a platform is everything in this table.
| Category | Capability |
|---|---|
| Security | Encryption at rest for API keys and connection credentials — authenticated symmetric encryption, never plaintext config. |
| Deterministic PII detection and auto-redaction across email, national identifiers, card numbers, phone, and IP, with three transmission modes. | |
| SSO and SAML sign-in against Azure AD, Okta, Google, ADFS, or a custom OIDC/SAML provider. | |
| Role-based access control across 17 discrete permissions, and a filterable, paginated audit log covering 15 event types. | |
| Intelligence | Incremental rebuild — a schema hash check skips inference entirely when the source structure is unchanged. |
| Structural versioning and diff between dashboard revisions, so a rebuild is reviewable rather than a blind overwrite. | |
| Natural-language refinement — conversational edits regenerate the artifact rather than patching a rendered image. | |
| Scheduled rebuilds on an hourly, daily, weekly, or monthly cadence, with freshness tracked as fresh, stale, or expired. | |
| Operations | Documented REST API with an OpenAPI specification and interactive explorer, so builds can be triggered from your own systems. |
| Signed webhook notifications to Slack, Teams, or a generic endpoint on build completion and failure. | |
| Prometheus metrics endpoint, container deployment with Docker Compose, and usage metering for internal chargeback. |
FAQ
No — it clears the queue so they can do the work that needs them. The forty-item backlog is mostly variations on dashboards your team has already built ten times. Generating the first draft in ninety seconds means your analysts spend their time on the semantic layer, data quality, and the genuinely hard modeling questions instead of laying out another revenue-by-region page.
Schema metadata is; row-level data generally is not. A deterministic scanner detects and redacts personal data before anything is transmitted, and three privacy modes let you choose full, anonymized, or schema-only transmission per connection. In schema-only mode the model sees structure and nothing else — which for most dashboard design is all it actually needs.
Three self-correction loops run before you ever see the file. Schema analysis is validated against the real source, the design is cross-checked so every reference resolves, and DAX is profiled against actual data samples — which is the only way to catch a sum over a text column or a divide against a production zero. If retries exhaust, safe mode emits a guaranteed-valid minimal dashboard instead of a broken file.
Usually nothing in inference. A schema hash check runs before anything else. If the source structure has not changed, the previous configuration is reused and the artifact is repackaged without calling a model at all. Since most scheduled rebuilds are refreshing data against a stable schema, the steady-state cost of keeping dashboards current is close to zero.
Yes — there is a documented REST API with an OpenAPI specification and an interactive explorer. Builds can be kicked off from a data pipeline, a scheduler, or a chat command, and signed webhooks report completion or failure back to Slack, Teams, or a generic endpoint. A command-line interface is available for environments where running a web service is not wanted.
Not the clean one. The request with the ambiguous grain, the four date columns, and the finance workbook nobody wants to touch. That is the one that tells you whether the pipeline is real.
Written on this
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