Bridge the gap from sim to reality
We build the training data infrastructure your robotics team shouldn't have to build alone.
Frontier
AI Lab
Leading
Smartphone Maker
Major
Social Platform
Top GenAI
Assistant Maker
Leading
Search Engine
Leading Global
Ecommerce
Frontier
Robotics Studio
Iconic
Smart Eyewear
World-Leading
Creative AI Platform
Premium
Automotive AI
Top-20
US Mortgage Lender
Major
Crypto Trading Platform
Global
Banking-as-a-Service
Emerging
BNPL Fintech
Global
Card Issuer
Tier-1
FinCrime Portfolio
WHY THIS MATTERS
Three forces making GenAI solutions harder to ship for robotics
Simulation only takes a robot so far, and real-world training data is what closes the gap between the lab and the warehouse floor.
Robots are shipping faster than data can follow
542K
Industrial robots were installed globally in 2024, a figure that doubled over 10 years. Every new installation needs training data the internet cannot provide, including grasp quality, defect taxonomies, and manipulation sequences.
IFR World Robotics 2025 (Sep 2025)
A single-task robot dataset costs $50K–$200K
$50K+
That is the total cost for a production-grade teleoperation dataset before accounting for engineering overhead. Operator training alone takes 20–40 hours before demonstrations are consistently high-quality.
Robotics Center of Silicon Valley, 2026
Physical AI goes high-risk under EU AI Act
Aug 2028
The EU AI Act classifies AI embedded in physical products such as robots, drones, and medical devices as high-risk. Training-data documentation requirements for safety components take effect at the August 2028 enforcement date.
EU Council & Parliament AI Omnibus agreement, May 2026
PROVEN OUTCOMES
What production-ready robotics AI delivers
These are real client-of-record results from robots running in the field, not numbers from a simulator.
94.7%
Top-1 gesture classification accuracy across 16 classes cleared the bar for shipping hand-tracked UI on a flagship spatial headset.
99.1%
Defect detection reached this accuracy on an automotive assembly line at 847 units/hour throughput. Surface scratches, dents, and paint anomalies are identified at 30fps.
<1.8%
The demographic disparity gap in gesture AI stayed this low, enabling simultaneous rollout across 20 markets without bias-driven recall risk.
OFFERINGS FOR ROBOTICS COMPANIES
Training data built for how robots actually fail
Each program targets the edge cases where robots break in the real world, from rare defects to unpredictable human behavior.
Gesture and manipulation intelligence
We build ego-centric and exo-centric video data for hand tracking, object manipulation, and spatial interaction. The 16-class gesture taxonomies include XR edge-case variants.
Visual defect detection
We build labeled defect corpora across lighting, angle, and material variations. Each defect class includes 50K+ images with OEM-spec severity grading
Robot procedure and assembly video
We generate step-by-step instruction videos from CAD files. Manufacturing engineers verify the robot arm trajectories, tool callouts, and safety narration.
ROS2 and embedded systems debugging
We cover ROS2 node bugs across humble/iron/jazzy distributions, along with CUDA kernel race conditions, memory leaks, and warp divergence patterns. These programs reach 88.4% root cause accuracy on robotics/HPC debugging
Multimodal sensor fusion
We deliver speech-to-action command parsing in noisy environments, video verification of robot arm tasks, and temporal action grounding across modalities.
DEEP DOMAIN SOLUTIONS
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Multimodal evaluation
We test gesture video, depth maps, LiDAR point clouds, and force-torque signals across modalities simultaneously.
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SFT / CoT demonstrations
Engineers author reasoning chains for robot manipulation planning, assembly sequencing, and failure diagnosis.
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Data annotation
We apply bounding box, keypoint, and temporal segmentation labels on manipulation video. Each frame is annotated for action success or failure.
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Expert preference
Robotics engineers rank manipulation trajectories so your planner picks the grasp that works, not the one that looks smooth.
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Red teaming
We run adversarial probing for sensor spoofing, sim-to-real failure modes, and safety boundary violations in physical AI.
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how we deliver
How these offerings get delivered
Three numbers explain why these programs reach production: domain scientists on the data, rigorous defect annotation, and activation in under 48 hours.
Expertise | Domain Scientists
100+
domain scientists & robotics specialists
Roboticists, manufacturing engineers, computer vision researchers, and ROS2 specialists. No anonymous crowd workers on safety-critical robotics data; every annotator is credentialed and auditable.
Quality | Defect Annotation
50K+
images per defect class
We capture these across lighting conditions, camera angles, and material variations. A multi-annotator consensus protocol handles safety-critical ambiguous cases, and OEM specification docs drive severity grading.
Speed | Gigsourcing Activation
<48 hrs
program activation SLA
100+ countries, 150+ languages, robotics-domain specialist pools pre-qualified and ready. The Gigsourcing Platform activates programs before your procurement cycle ends.
TRUST & COMPLIANCE
The safety frameworks robotics programs are built inside
These frameworks are not checked after the fact. They are built in from the first data spec, so your safety team sees audit trails, not surprises.
Robotics regulatory coverage
EU AI Act · Annex I (Safety Components)
EU Machinery Regulation 2023/1230
GDPR · Biometric Data
OSHA · Collaborative Robot Safety
ISO 10218 · Industrial Robots
ISO/TS 15066 · Collaborative Robots
Certifications
SOC 2 Type II
ISO 27001
ISO 17100 (multilingual)
CFPB 12 CFR 1024.41
CFPB 12 CFR 1024.41
CFPB 12 CFR 1024.41
CFPB 12 CFR 1024.41
INSIGHTS
Latest from the Firstsource team
Insights from the field, real operations, real outcomes, and perspectives from the people making it work in live operations.
Case Study
Penetration testing using GenAI enhances platform safety and trust for an online marketplace for short and long-term homestays and experiences
Discover how a global marketplace for homestays and experiences partnered with Firstsource to proactively secure its platform against identity and listing fraud through GenAI-powered penetration testing, bolstering user trust and safety.
Case Study
Delivering 1 million AI tasks in 5 weeks: Firstsource enhances GenAI model with 98% accuracy
Goal: A leading tech company partnered with Firstsource to enhance their virtual assistant's GenAI model using Reinforcement Learning from Human Feedback (RLHF). The goal was to improve accuracy and reliability by training the model with high-quality, annotated data, creating and verifying multi-turn conversations across multiple domains within a tight deadline. How we made it happen:Our tailored approach ensured precise execution at scale. Here's how:
Case Study
Rapid improvement in GenAI models using high quality, multilingual STEM content, with 100% quality compliance for a global tech giant
Firstsource delivered 120K+ high‑quality STEM content items in 10 languages for a GenAI model, achieving full compliance and rapid onboarding of expert creators.
Get Started
Build robotics AI that works in the real world
Tell us what you're building. A robotics program lead replies inside one business day.
- Talk to a robotics program lead
- Sample robotics dataset returned in 5–10 business days
- Compliance docs (SOC 2, ISO, GDPR, EU AI Act) on request
- NDA before any data exchange