Coming soon
SLM-Works Data Extractor
Structured data from messy sources
Converts semi-structured operational content into validated records, reducing manual copy/paste and reconciliation work.
How it works
Step 1
Ingest source text or OCR payload.
Step 2
Extract target entities based on your schema.
Step 3
Validate data shape and required fields.
Step 4
Return standardized output for downstream automation.
Example
Example input
Mixed inbox of claim forms, status emails, and scanned attachments.
Example output
{ claimant_name: '...', claim_id: '...', incident_date: '...', amount: 1240.50, confidence: {...} }
Key features
- Multi-field extraction across structured and unstructured sources
- Built-in per-field confidence scoring
- Batch processing mode for back-office throughput
- Multi-language input normalization
Rollout guidance
- Start with one strict schema and expand gradually.
- Define fallback queue for low-confidence records.
Ideal for
FAQ
Is OCR included?
The model expects text input. OCR can be added as a preprocessing step in your ingestion pipeline.
Want this model in your stack?
We can scope a deployment blueprint, evaluation set, and integration plan for your data and infrastructure constraints.