Raw-source data preparation and stewardship library

Reduce repeated data-preparation work while keeping source rights and review visible. For data and ML engineers preparing raw sources for model training and LLM workflows, convert licensed raw sources, schema rules, source guidance and quality thresholds into reviewer-approved clean structured datasets with per-field trust scores. The benefit is a testable hypothesis, measured through accepted dataset records per preparation hour and downstream model errors traced to data defects; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect licensed raw sources, schema rules, source guidance and quality thresholds, then follow this sequence: 1. Ingest licensed raw sources and record usage rights. 2. Clean and normalize raw records against declared schema rules. 3. Generate synthetic records for underrepresented cases. 4. Build customizable pipelines with ordered processing steps. 5. Extract primary content blocks using semantic analysis and density scoring. 6. Emit markdown-llm, text-llm and html-llm output formats. 7. Emit traditional HTML, JSON and Markdown formats. 8. Attach per-field trust scores to every structured record. 9. Cross-check fields against multiple references and re-query when trust is low. 10. Apply user-defined domain priorities and ignore lists. 11. Retain run memories and reuse successful extraction scripts. 12. Deprioritize sources that repeatedly fail validation. Resolve uncertain cases with qualified reviewers, approve reviewer-approved clean structured datasets with per-field trust scores, and measure accepted dataset records per preparation hour and downstream model errors traced to data defects 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 fixed schema and licensed source set; final data quality and rights checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, usage permissions and data protection. Named owners approve schema changes, dataset releases and downstream use. One fixed schema and licensed source set; final data quality and rights checks remain human. 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 schema and licensed source set; final data quality and rights checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: ingest licensed raw sources and record usage rights; clean and normalize raw records against declared schema rules. Support the third module with operator review: generate synthetic records for underrepresented cases. 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 source repositories, authorized APIs and permitted public sources. Cloud storage, ML frameworks and data warehouses. Start with file exchange and validate destination specifications before promising direct pipeline integration. 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 intake and rights, Editable preparation preview, Dataset release and delivery. Use a searchable library of sources and runs, a large central table for records and fields, and a right-hand panel for schema rules, source guidance and comments. Let users compare raw and cleaned versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant record or field. Make the task-specific outcome reviewer-approved clean structured datasets with per-field trust scores visible beside its evidence, review state and value baseline.