AI-assisted analysis
AI-assisted analysis grounded in governed data.
Ask why performance changed, what drove it, whether the data is trustworthy, and where profit is leaking. Supported answers keep the metric definition, comparison periods, source coverage, readiness, and limitations attached.
- Supported business questions, not unrestricted warehouse chat
- Approved metrics and explicit comparison periods
- Readiness, warnings, and caveats returned with evidence
Supported question patterns
Start from a business question, stay inside the evidence.
Each question follows a supported analysis pattern with defined metrics, periods, dimensions, and evidence requirements. Unsupported questions return limitations instead of an invented answer.
What changed?
Compare supported marketing or commerce metrics across explicit current and comparison periods, including dimensions that are available for the selected sources.
“How did spend and conversions change month to date?”What drove it?
Rank supported dimensions by absolute metric change and inspect persisted marketing opportunities, winners, losers, or movers when those outputs are ready.
“Which campaigns drove the decline in conversions?”What does this metric mean?
Explain a discovered metric’s definition, role, aggregation behavior, source coverage, availability, and known non-additive or partial-coverage caveats.
“Is this ROAS comparable across these sources?”Where is profit leaking?
When Commerce Intelligence is ready, inspect modeled profit waterfalls, margin leaks, product profitability, recommendations, and supported period comparisons.
“Which products have the weakest contribution margin?”Can I trust the data?
Bring source health, ingestion status, freshness, coverage, semantic issues, and readiness evidence into the same review as the metric.
“Are there coverage gaps behind this result?”What can we prepare for review?
Prepare bounded context for decision packs, dashboard drafts, export validation, creative briefs, catalog suggestions, and policy simulations without publishing them.
“Draft a review pack with evidence and next checks.”Availability follows the account. A question can be answered only when the connected sources, semantic contracts, required fields, dates, currency behavior, and product readiness support it.
How answers earn trust
A conclusion should show how it was reached.
Metric Hive resolves the permitted account context, confirms available metrics, applies explicit comparison periods, and returns the evidence needed to review the conclusion.
- 01
Resolve the permitted scope
The analysis is limited to the selected workspace and the sources available to that account.
- 02
Confirm the metric and grain
Available dimensions and metrics retain their definitions, aggregation behavior, date basis, currency behavior, and compatibility warnings.
- 03
Compare explicit periods
The analysis uses declared current and comparison windows and preserves missing-field, partial-coverage, and readiness warnings.
- 04
Return an evidence receipt
Rows, summaries, definitions, coverage, warnings, and caveats make the resulting answer reviewable.
Answer context
A useful answer explains its footing.
The tool response is designed to preserve the details that make a marketing or commerce conclusion inspectable rather than merely fluent.
What was measured
Metric identity, label, role, aggregation, and applicable semantic caveats.
Where the result applies
Authorized account, selected source identifiers, dimensions, and filters.
Which periods were compared
Explicit date ranges and the date behavior attached to the underlying dataset.
How complete the evidence is
Readiness, field availability, source health, coverage, warnings, and missing data.
How currency was handled
Original-currency behavior or supported conversion options, with unsupported modes blocked.
What remains unknown
Unsupported fields, unavailable outputs, non-additive totals, and causal claims stay explicit.
Hard boundaries
Assistance without unbounded authority.
Supported analysis can explain observed changes and prepare review material. It cannot turn incomplete data into certainty, silently broaden scope, or execute decisions in external systems.
Inside the analysis surface
- Approved semantic fields, filters, dates, sorting, and bounded row limits
- Marketing comparisons, scorecards, metric explanations, and drivers
- Available Commerce Intelligence and persisted decision evidence
- Data Health, readiness, source status, and non-secret metadata
- Unsaved draft context and deterministic simulations for human review
Outside the analysis surface
- Raw SQL, arbitrary table names, invented joins, or silently broadened account scope
- Incomplete or incompatible evidence presented as a complete conclusion
- Publishing exports, starting ingestion, or changing connected systems
- Automatic campaign, budget, price, inventory, feed, or creative writeback
- Manufactured answers when data, readiness, or governed evidence is missing
Evidence is not automatically causality.Performance changes, attribution, modeled contribution, and observed correlations must keep their documented meaning. The assistant should not call a change causal or claim proven lift unless governed evidence explicitly supports it.
Delivery
Business capability, delivered through Metric Hive MCP.
AI-assisted analysis defines the supported business questions, evidence, and interpretation boundaries. Metric Hive MCP provides the separate technical connection to compatible AI clients.
Separate responsibilities
Analysis outcomes → MCP delivery
Business questionsEvidence receiptsTechnical connection
Use this page to understand what the analysis can support. Use the MCP page for endpoint configuration, authorization, client compatibility, and security boundaries.
Questions
What “AI-assisted” means here
What makes an answer reviewable?
The answer should preserve the metric definition, applicable scope, comparison periods, source coverage, readiness, warnings, and known limitations.
Can the assistant query any connected data?
No. It can use fields and combinations exposed as available by the semantic discovery and query-planning services. Unsupported fields or incompatible combinations return limitations instead of an invented query.
Can it change campaigns or publish work?
No. The hosted tool surface does not perform automatic provider writeback. Draft and simulation workflows remain unapproved review inputs, and export planning does not create, schedule, or run an export.
Does an observed change prove causality?
No. Descriptive comparisons, attribution outputs, modeled contribution, and correlations retain their documented meaning unless governed causal evidence explicitly supports a stronger claim.