Evidence-backed data question and reporting workspace

Reduce time to a traceable answer while keeping the organization's data and definitions. For analysts and operations leads answering recurring business questions from company data, convert connected databases, uploaded files and business definitions into reviewed, source-linked answers, dashboards and reports. The benefit is a testable hypothesis, measured through reviewed answers accepted per analyst hour and corrections after publication; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect connected databases, uploaded files and business definitions, then follow this sequence: 1. Ask questions in plain language and return answers without SQL. 2. Aggregate and analyze connected data to identify themes and insights. 3. Search connected sources with AI and retrieve relevant records. Resolve uncertain cases with qualified reviewers, approve reviewed, source-linked answers, dashboards and reports, and measure reviewed answers accepted per analyst hour and corrections after publication against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers and narratives for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final metric definitions and publication decisions remain with the data owner. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve data access boundaries, source attribution, metric definitions and usage permissions. Data owners approve substantive changes and publication scope. One approved database and one file source; final metric definitions and publication decisions remain with the data owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One approved database and one file source; final metric definitions and publication decisions remain with the data owner. Implement one approved input format, a bounded representative question set and the first two task modules: ask questions in plain language and return answers without SQL; aggregate and analyze connected data to identify themes and insights. Support the third module with operator review: search connected sources with AI and retrieve relevant records. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned databases, CRMs, ERPs and cloud file storage. Warehouse and BI destinations, export formats and embedding targets. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Data connections and definitions, Question and analysis workspace, Dashboard and report delivery. Use a list of saved questions and datasets, a large central answer canvas with the generated query, chart and source rows, and a right-hand panel for definitions, filters and comments. Let users compare answer versions side by side. Display draft, changes requested and approved states. Provide a share link with comments anchored to the relevant chart or number. Make the task-specific outcome reviewed, source-linked answers, dashboards and reports visible beside its evidence, review state and value baseline.