Data Extraction Services · Gega Infotech
Human-in-the-Loop Annotation
Skilled operators reviewing, labelling, and correcting where models alone cannot be trusted — medical charts, legal exhibits, multilingual scans.
- ✓Founded 2002 — over two decades of delivery
- ✓ISO 27001 & ISO 9001 certified
- ✓Standard turnaround: 24 hours
- ✓Clients across US · CA · UK · AU · NZ · UAE
What this service covers
Medical and Clinical Data
Annotating clinical notes, radiology reports, and pathology findings for AI training — where automated systems flag ambiguous or complex cases, our operators resolve them accurately.
Legal and Regulatory Documents
Court filings, regulatory submissions, and legal exhibits annotated for entity extraction, classification, and relationship labelling models.
Multilingual Content
Annotation across multiple languages for NLP and translation model training. We confirm language coverage for your dataset before the job begins.
Ongoing Review and Retraining
We do not just label a one-off dataset. We work alongside your model as a standing review function — catching edge cases, correcting drift, and feeding clean labels back into your pipeline.
How it works
Send the documents
Via SFTP, shared drive, or direct system access — whatever your team already uses. We fit around your setup, not the other way around.
We extract and structure
Operators work to your exact field specifications. Every record goes through a second-pass quality check before it leaves our team.
Receive clean data
Formatted to your spec, delivered back the same way it arrived. Standard turnaround is 24 hours — often less.
A typical engagement
AI Healthcare Company — United States
A company building a clinical NLP model needed human review on the cases their model was least confident about — roughly 15 percent of the daily volume. Gega provided a standing team of operators trained on clinical terminology to review and correct model output on a daily basis, feeding corrected labels back into the training pipeline.
Who sends us this work
AI and machine learning teams, data labelling operations, and technology companies building models that handle complex or regulated document types where automated labelling alone is not sufficient.
Common questions
We treat onboarding as a formal process. We review your guidelines, run a calibration batch with your team, identify any ambiguities, and agree on edge case handling before full-scale processing begins. We document everything so the approach stays consistent as volume changes.
We work within your existing annotation platform. We do not require you to change tools or export your data to a different environment. If you do not have a platform yet, we can discuss options.
We run regular inter-annotator agreement checks and review samples from every operator on the team. When accuracy drops on a specific label type, we address it directly rather than letting drift accumulate.
Ready to clear the backlog?
Tell us what you have. We’ll tell you honestly whether we’re the right fit.
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