Open egocentric datasets give you 2D video with no depth, no pose, no tactile signal. Humanoid training requires all three. How do you build a multimodal setup that captures what open data structurally cannot?

Simulation environments need real geometry. Building them by hand requires a full production team — scanning them from reality requires three tools and one field visit. How do you turn a lidar sweep and 150 photographs into an IsaacSim-ready scene?

CFA-level cases, multi-step calculations, and professional English, all at once. 20–25% hiring conversion, no in-house domain expertise on the ops side. How do you maintain expert consistency when the domain leaves no room for approximation?

3,500 math problems, three difficulty levels, every solution step checked, not just the final answer. We brought in olympiad students and university instructors to stress-test model logic.

City streets in 3D: thousands of objects, overlapping geometries, no margin for misclassification. 3,000 point clouds processed in 19 days at 99% accuracy. What does it take to make raw spatial data reliable enough for robotics?

Seven days from raw Hindi audio to a controlled, production-ready transcription system. Expert benchmark, automated SERP scoring, and a vetted team deployed without delay.

Arabic is not a single operating language. Dialects vary so strongly that speakers from different regions may struggle to understand each other. At the same time, the client needed consistent, comparable results across tasks.

Testing biometrics with frontal-only masks hides real weaknesses. We developed fabric mask samples for true multi-angle evaluation.

Capturing emotion at scale required more than cameras. We built a system that made it consistent, synchronized, and repeatable.

Real-world print and replay attacks were gathered through ongoing attempts to bypass a live system.

Children’s faces change faster than biometric models adapt. We collected real facial data across ages 7 to 15 to track that change over time.

How do you capture subtle differences in male hair loss at scale? We collected 350 multi-angle photo sets, labeled with expert precision using the Norwood Scale.

Can large-scale female hair loss data be gathered ethically and precisely? Yes, through careful participant guidance and expert labeling.

Collecting 20,000 palm photos sounds easy until you try it. We managed scale, verification, and logistics to deliver a clean dataset.

How do you annotate 200,000 trees with species, height, and crown data from aerial imagery to enable precise forest monitoring?

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.

To train violence detection models, synthetic-looking footage is not enough. We created 200 realistic conflict scenarios with complex movement, occlusions, and crowded environments using multi-camera 4K recording.

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.

We transformed frequent buyer questions into structured intent data, enabling an AI assistant that improves response quality and user satisfaction across the marketplace.

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.