Who should use the Extract entities from documents workflow?
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Science & Healthcare
Practical execution plan for extract entities from documents with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Actionable entity data delivered to the target system or team.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Actionable entity data delivered to the target system or team.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Upstage to a clean, uniform text corpus ready for entity extraction. Then, you pass the output to Prodigy to a documented schema and guidelines that enable consistent entity labeling. Then, you pass the output to DEEPCRAFT™ Studio to a high-quality labeled dataset that can train or evaluate an extraction model. Then, you pass the output to Together AI to a trained entity extraction model with measured performance metrics. Then, you pass the output to Affinda to a complete set of extracted entities across all documents, stored in structured format. Then, you pass the output to Rossum to validated extractions with known error rates and improvements applied. Finally, Flare is used to actionable entity data delivered to the target system or team.
Document Ingestion and Preprocessing
A clean, uniform text corpus ready for entity extraction.
Define Entity Schema and Annotation Guidelines
A documented schema and guidelines that enable consistent entity labeling.
Annotate a Gold-Standard Dataset
A high-quality labeled dataset that can train or evaluate an extraction model.
Train or Configure an Entity Extraction Model
A trained entity extraction model with measured performance metrics.
Run Batch Extraction on Full Document Set
A complete set of extracted entities across all documents, stored in structured format.
Validate and Refine Extractions
Validated extractions with known error rates and improvements applied.
Export and Integrate Extracted Entities
Actionable entity data delivered to the target system or team.
Collect all source documents (PDFs, Word files, plain text) and convert them into a uniform machine-readable format. Apply OCR if needed for scanned documents, then clean the text by removing headers, footers, and irrelevant formatting.
Why Upstage: Upstage provides document parsing and digitization, which directly covers OCR and text extraction from documents.
Specify the types of entities to extract (e.g., drug names, dosages, patient symptoms, lab values) and create a schema with attributes. Write clear annotation rules to ensure consistency across documents.
Why Prodigy: Prodigy is a dedicated annotation tool for named entity recognition and text classification, ideal for defining entity schemas and creating annotation guidelines.
Manually label a representative sample of documents (e.g., 100-500 pages) with the defined entities. Use a collaborative annotation tool to track inter-annotator agreement and resolve disagreements.
Why DEEPCRAFT™ Studio: DEEPCRAFT™ Studio offers data collection and annotation, suitable for creating a gold-standard dataset with labeled entities.
Use the annotated dataset to fine-tune a pre-trained NLP model (e.g., BioBERT, spaCy NER) or configure a rule-based system. Split data into training/validation/test sets and iterate on hyperparameters.
Why Together AI: Together AI allows fine-tuning pretrained models on custom data and deploying them, directly supporting entity extraction model training.
Apply the trained model to all remaining documents in the corpus. Process documents in batches to manage memory, and output results as structured data (e.g., JSON, CSV) with confidence scores.
Why Affinda: Affinda automates document processing workflows and extracts data from various document types, ideal for batch extraction on a full document set.
Sample a subset of extraction results and manually verify accuracy. Identify systematic errors (e.g., missed entities, false positives) and adjust the model or post-processing rules accordingly.
Why Rossum: Rossum provides data extraction, document classification, and validation, directly supporting the validation and refinement of extractions.
Format the final entity data for downstream use (e.g., database insertion, API feed, or dashboard). Generate summary statistics and documentation for stakeholders.
Why Flare: Flare can create autonomous AI agents that integrate with external tools, APIs, and databases, enabling export and integration of extracted entities.
§ Before you start
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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