Entity Extraction
Parsing unstructured text to pull out structured values like vendor names, amounts, dates, or ticket priorities.
Entity extraction converts raw text into structured fields—names, amounts, dates, IDs—so systems can act on them. It replaces manual reading and retyping with model-driven parsing.
In operations, it powers invoice capture, contract review, ticket triage, and email routing. Extracted fields feed approvals, payments, and CRM updates without human copy/paste.
It fits into workflows as an upstream enrichment step: ingest text, extract key fields, validate against schemas, then pass clean data to downstream systems. The payoff is speed and fewer errors versus manual entry.
Frequently Asked Questions
What documents are best suited for entity extraction?
Invoices, receipts, contracts, support emails, resumes, and forms with semi-structured text. Results improve when templates are consistent or examples are provided.
How do I improve extraction accuracy?
Use schemas, validation rules, and examples. Normalize inputs (OCR quality), handle multiple layouts, and add post-processing (regex, lookups) for edge cases.
How do I handle low-confidence fields?
Route to human review, request clarifications, or re-run with stricter constraints. Store confidence per field and set thresholds by risk.
Can I run extraction on images or scans?
Yes, with OCR first. Use high-quality scans, deskewing, and language hints to improve OCR, then extract entities from the text.
How do I validate extracted data?
Apply type checks, allowed value lists, cross-field checks (totals vs line items), and external lookups (vendor master) before writing to systems.
What about privacy and PII?
Mask or avoid storing sensitive fields, use secure processing, and restrict access. Prefer on-prem or private models for regulated data.
How do I handle multilingual documents?
Detect language, use multilingual models, and localize validation rules (date/currency formats). Test per language before scaling.
Can I fine-tune models for my forms?
Yes, but start with prompt-based or schema-guided extraction. Fine-tune when you have labeled examples and stable layouts to justify the effort.
How should I monitor extraction quality?
Track field-level accuracy, exception rates, and correction patterns from human review. Retrain or adjust prompts when drift appears.
Agentic AI
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A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
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A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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