Evidence-backed data question and reporting workspace

Reduce repeated manual data pulls while keeping every answer traceable and reviewed. For analysts and operations leads who answer recurring data questions for their teams, convert connected spreadsheets, databases and cloud storage into reviewed plain-language answers, charts and reusable playbooks linked to their source queries. The benefit is a testable hypothesis, measured through accepted answers 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 spreadsheets, databases and cloud storage, then follow this sequence: 1. Ask questions about connected data in plain language. 2. Generate charts, graphs and dashboards from the result. 3. Connect spreadsheets, databases, cloud storage and other sources. Resolve uncertain cases with qualified reviewers, approve reviewed plain-language answers, charts and reusable playbooks linked to source queries, and measure accepted answers 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 outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One connected source set and one permission model; final metric definitions and publication decisions remain analytical. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, metric definitions, permission boundaries and usage permissions. Data owners approve substantive changes and publication scope. One connected source set and one permission model; final metric definitions and publication decisions remain analytical. 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 connected source set and one permission model; final metric definitions and publication decisions remain analytical. Implement one approved input format, a bounded representative case set and the first two task modules: ask questions about connected data in plain language; generate charts, graphs and dashboards from the result. Support the third module with operator review: connect spreadsheets, databases, cloud storage and other sources. 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 spreadsheets, databases and cloud storage. Cloud data warehouses, BI destinations, Slack and messaging platforms. 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 connection and permissions, Question and answer workspace, Reviewed report and delivery. Use a left-hand list of saved questions and playbooks, a large central answer canvas with chart and table views, and a right-hand panel for sources, query text, permissions 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 plain-language answers, charts and reusable playbooks linked to source queries visible beside its evidence, review state and value baseline.