How do you annotate 100,000 license plates with dozens of nuances — from Arabic characters to regional codes — and still meet a two-week deadline?

We built a scalable sentiment annotation pipeline that handles sarcasm, ambiguity, and domain-specific nuance — enabling smarter brand analysis and customer insight.

We annotated 90 minutes of video footage from a factory entrance surveillance system, reducing the number of frames from 50-60 […]

We annotated 132,000+ objects in 11,000 aerial images—streamlining urban planning data with scalable workflows and tailored class logic.

From contracts to inheritance certificates, we annotated 6,000+ legal documents with high precision and custom validation logic.

How do you annotate shelves packed with thousands of ever-changing products? We built a high-speed pipeline to handle real-time updates and ensure merchandising insights stay current.

In marketplaces, speed and clarity drive conversions — and buyers expect instant answers.
To meet this demand, one of the top classified platforms set out to build an AI assistant capable of handling frequent questions with precision. Unidata provided the annotated intent data that became the foundation for smart, context-aware responses — helping users get what they need, faster.

How do you teach AI to recognize when a student is cheating during an exam? By accurately annotating 6000 images of real exam scenarios — and that’s exactly what we did.

We successfully completed a project annotating construction equipment, labeling approximately 5,000 images using object detection methods. Our approach ensured high accuracy and fast turnaround, fully meeting the client’s requirements.

How do you segment every single object in a cluttered interior photo — 30+ classes per image? We designed a multi-step annotation pipeline to handle complexity without losing precision.

We helped a mining company quickly train a model to detect ore granularity and oversized fragments directly on the conveyor belt—cutting processing delays and freeing up internal resources.

Our team supported the development of a reply suggestion system by annotating thousands of user dialogs — focusing on tone, relevance, and linguistic nuance.

We annotated and validated thousands of chat messages to train an AI model that now filters unsafe, abusive, or inappropriate content while keeping conversations natural and fast.

We helped structure the chaos of online listings — enabling cleaner product cards through expert annotation and smart grouping.

We handled complex, real-world audio by combining automation with expert oversight — capturing every voice, pause, and interruption.

From scenario planning to annotation, we supported a full-cycle dataset build for a CV model trained to detect physical aggression in public spaces.

We completed 80 hours of high-complexity audio transcription without relying on pre-labeling — leveraging a scalable workflow designed for accuracy, consistency, and speed.

AI meets urban planning: our dataset enabled the automation of waste collection, reducing costs and improving municipal services.

With clear guidelines and a sharp execution strategy, we delivered a high-quality dataset tailored for hair loss classification tasks.

Unidata collected 750+ unique audio samples of children’s emotional expressions — enabling emotion recognition in family-focused apps.

Fast-tracked annotation of 363,000 banking calls with strict privacy — boosting NLP automation for debit, credit, and deposit queries.

Our custom dataset powered the transition from manual picking to AI-assisted harvesting — optimizing yield through data-driven ripeness detection.

We built a reliable dataset for biometric system testing — fast, compliant, and ready for integration.

From zero to 99% model accuracy in 28 days: we sourced, staged, and annotated video footage for urban weapon detection systems.