Keep pace with patient demand

Train models on high-quality image data to help your clinicians work more efficiently and effectively.



All the way back in 2011, a pilot model successfully matched lung nodules from separate image scans 62% faster than a panel of radiologists.



Experts say that fusing clinician capabilities with ML-powered diagnosis could save healthcare providers $3 billion-per-year by 2026.

Image tagging

Categorize different images by specified metadata.

Image tagging & validation

Collect image annotations, confirm objects/images have been accurately tagged and source necessary corrections.

Slash administrative costs

Use NLP and speech data to build models that eliminate administrative redundancy.


Time back

With tools to digitize forms and order tests/prescriptions, doctors and nurses get back the 20%-50% of the day they spend performing administrative tasks.



Automating administrative duties could slash annual administrative spending in half by 2026.

Named-entity tagging

Train models to extract key elements of documents and text.

Speech collection

Collect variant expressions of identical intents within a clinical setting.

Speech transcription

Teach models to turn real-life speech into highly accurate transcriptions.

Talk to patients 1:1 with virtual nursing assistants

Outline your requirements and let our team handle text collection and annotation to build the foundations of a virtual nursing assistant.



With AI enhanced virtual assistants available anytime, anyplace, providers can proactively ensure patients get their questions answered and follow treatment regiments.



By automating routine interactions and freeing up nurses to handle complex inquiries, healthcare providers could save $20 billion per year by 2026.

Named-entity tagging

Train your virtual nurse to identify and act upon designated keywords.

Text collection & validation

Teach your bot to understand the myriad of nuances in how different people might express the same idea or intent.

Speech transcription

Build the foundations for voice capabilities with speech-to-text transcription.

Success Stories

Mastercard’s R&D Labs needed unique, multi-lingual text data that covered 20 designated payment scenarios in English and Spanish, and they needed it fast.

Keeping a nation’s lights on means constantly inspecting electricity poles for damage. EDP partnered with DefinedCrowd to improve Asset Performance Management processes.

A global electronics maker came to DefinedCrowd with the goal of building more inclusive facial recognition models, requiring accurately annotated images with highly specific criteria.

A Fortune 500 Tech company needed comprehensive speech training data in French that accounted for a wide range of dialects, requiring diverse data in terms of age, gender and regional dialects.

A visionary Fortune 500 Tech company leveraged sentiment analysis models to dig beyond surface-level understandings to extract granular-level insights.

Smart companies see the pile of unstructured text floating through the digital realm as a strategic goldmine of consumer insights.