Multi-source data extraction and stewardship console

Reduce manual extraction and cleaning work while keeping a reviewable record of every change. For data teams and operations staff who collect and clean data from websites, files and internal systems, convert permitted web pages, documents, spreadsheets, images and connected sources into reviewed, structured datasets with source references. The benefit is a testable hypothesis, measured through accepted records per operator hour and correction rate after export; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted web pages, documents, spreadsheets, images and connected sources, then follow this sequence: 1. Extract structured data from permitted web pages. 2. Highlight and select specific page elements for extraction. 3. Read PDFs, spreadsheets and images with vision models. 4. Detect and remove inaccuracies and inconsistencies. 5. Enrich datasets with additional relevant fields. 6. Accept plain-language instructions to clean, transform, enrich and merge data. Resolve uncertain cases with qualified reviewers, approve reviewed, structured datasets with source references, and measure accepted records per operator hour and correction rate after export 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. Final schema decisions, data rights checks and consequential actions remain with the data owner. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, data rights, privacy and usage permissions. Data owners approve schema changes and external sharing. One approved source type and one export schema; final schema decisions and data rights checks 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 source type and one export schema; final schema decisions and data rights checks remain with the data owner. Implement one approved input format, a bounded representative case set and the first two task modules: extract structured data from permitted web pages; detect and remove inaccuracies and inconsistencies. Support the remaining modules with operator review: read PDFs, spreadsheets and images with vision models; enrich datasets with additional relevant fields; accept plain-language instructions. 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 websites, documents, spreadsheets, images and internal databases. Cloud storage, spreadsheet and database import/export, and destination applications. Start with file exchange and validate destination specifications before promising direct writes. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Source and template setup, Extraction and cleaning workbench, Review queue, Dataset library and export. Use a searchable list of datasets and runs, a central table or notebook view for records and transformations, and a right-hand panel for source references, field mappings and comments. Let users compare raw and cleaned versions side by side. Display draft, changes requested and approved states. Provide a shareable dataset view with comments anchored to the relevant record. Make the task-specific outcome reviewed, structured datasets with source references visible beside its evidence, review state and value baseline.