Evidence-backed data question answering workspace

Reduce time to a reviewed answer while keeping every number traceable to its source. For marketing and operations teams that need plain-language answers from their own data, convert connected data sources, uploaded files and plain-language questions into reviewed, source-linked answers and charts. The benefit is a testable hypothesis, measured through reviewed 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 data sources, uploaded files and plain-language questions, then follow this sequence: 1. Connect to external databases and data sources. 2. Upload files from a computer, a URL or the clipboard. 3. Ask questions in plain language instead of writing code or queries. 4. Suggest common questions to help users start exploring their data. 5. Turn data into charts and graphs for easier understanding. 6. Save, rename or delete past conversations for later reference. 7. Apply measures to keep AI-generated answers accurate and verifiable. 8. Embed the workspace into web apps, Slack or custom interfaces. 9. Control how queries are interpreted and answered for clarity. 10. Produce coherent and contextually relevant written content. 11. Adjust tone, style and length of generated text. 12. Provide live edits and suggestions to improve content quality. 13. Generate content in multiple languages. 14. Integrate with popular platforms to fit existing workflows. 15. Offer an intuitive interface that is easy for non-technical users. 16. Provide additional insights and reference material to deepen understanding of data. Resolve uncertain cases with qualified reviewers, approve reviewed, source-linked answers and charts, and measure reviewed 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 answers and text 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. One fixed data schema and approved source list; final interpretation and publication checks remain analytical. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve data provenance, source attribution, calculation accuracy and usage permissions. Data owners approve substantive changes and publication scope. One fixed data schema and approved source list; final interpretation and publication checks 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 fixed data schema and approved source list; final interpretation and publication checks remain analytical. Implement one approved input format, a bounded representative case set and the first two task modules: connect to external databases and data sources; upload files from a computer, a URL or the clipboard. Support the remaining modules with operator review: ask questions in plain language instead of writing code or queries; suggest common questions to help users start exploring their data; turn data into charts and graphs for easier understanding; save, rename or delete past conversations for later reference; apply measures to keep AI-generated answers accurate and verifiable; embed the workspace into web apps, Slack or custom interfaces; control how queries are interpreted and answered for clarity; produce coherent and contextually relevant written content; adjust tone, style and length of generated text; provide live edits and suggestions to improve content quality; generate content in multiple languages; integrate with popular platforms to fit existing workflows; offer an intuitive interface that is easy for non-technical users; provide additional insights and reference material to deepen understanding of data. 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, uploaded files and permitted research sources. Cloud data storage, BI tool import/export and reporting destinations. 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 upload, Question and answer workspace, Reviewed report and delivery. Use a thumbnail gallery for saved questions, a large central answer canvas, and a right-hand panel for sources, assumptions 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 figure. Make the task-specific outcome reviewed, source-linked answers and charts visible beside its evidence, review state and value baseline.