Evidence-backed data analysis and reporting workspace

Reduce the time from question to reviewed report while keeping every figure traceable to its source. For analysts and finance teams who answer data questions and produce recurring reports, convert permitted databases, spreadsheets and files into reviewed charts, dashboards and narrative reports linked to their source rows. The benefit is a testable hypothesis, measured through reviewed reports 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 permitted databases, spreadsheets and files, then follow this sequence: 1. Accept plain-language questions over connected data. 2. Connect databases, spreadsheets and files. 3. Clean, merge, sort and organize data before analysis. 4. Select relevant columns and suitable chart types. 5. Generate charts, tables and dashboards. 6. Produce narrative reports that summarize findings. Resolve uncertain cases with qualified reviewers, approve reviewed charts, dashboards and narrative reports linked to source rows, and measure reviewed reports 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 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 figures and narrative checks remain analytical. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve data provenance, source attribution, figure accuracy and usage permissions. Analysts approve substantive changes and publication scope. One fixed data schema and approved source list; final figures and narrative 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 figures and narrative checks remain analytical. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-language questions over connected data; connect databases, spreadsheets and files. Support the third module with operator review: clean, merge, sort and organize data before analysis. 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

Buyer-owned databases, spreadsheets and permitted files. Cloud data storage, BI 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: Question and data sources, Editable analysis canvas, Report and delivery. Use a thumbnail gallery for saved analyses, a large central canvas for charts and tables, and a right-hand panel for sources, filters and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or figure. Make the task-specific outcome reviewed charts, dashboards and narrative reports linked to source rows visible beside its evidence, review state and value baseline.