Ship-ready. Benchmark-proven. Expert-built.
Domain PhDs, not crowd workers, build the training data your model ships on.
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 AI labs
Synthetic data, a shrinking English share, and rising regulation are all reshaping what frontier models can safely train on.
Data quality: new bottleneck
$30B+
Global spend on AI training data is estimated to reach this level by 2030, up from ~$2.4B in 2023, as labs shift from quantity to expert-curated, domain-specific datasets.
Grand View Research, AI Training Dataset Market (2024)
Benchmarks are saturating
90%+
Frontier models now score above 90% on standard benchmarks like MMLU, yet still fail on novel, multi-step reasoning tasks that require domain expertise to evaluate.
Stanford AI Index 2026
Multimodal is the next frontier
65%
of AI labs are now training multimodal models that process text, image, audio, and video simultaneously, which requires training data infrastructure that spans every modality.
Stanford AI Index 2026
PROVEN OUTCOMES
What expert-built data delivers in production
These are real client-of-record results from frontier programs, not benchmark scores from a lab.
100K+
We created these STEM data units in 16 weeks across Physics, Chemistry, Maths, and Biology with a <0.5% rejection rate.
140K+
We collected and annotated these multimodal assets in 24 weeks to train eyes, hands, and voice for a leading AI assistant, with a <3% rejection rate.
<0.2%
We held the data rejection rate to this level on 100K STEM, engineering, creatives, and coding data points delivered in just 2 weeks.
CLIENT SPOTLIGHT
Inside the leading multimodal assistant
Expert-built multimodal data at global scale is what lets an assistant see, hear, and respond across dozens of markets.
Training eyes, hands & voice at global scale
We collected, annotated, and tested 140K+ diverse multimodal assets across 30+ countries and 100+ ethnicities. The program reached 2× annotation efficiency with 100% brand safety and <3% data rejection, reducing recollection by 80%.
We collected, annotated, and tested 140K+ diverse multimodal assets across 30+ countries and 100+ ethnicities. The program reached 2× annotation efficiency with 100% brand safety and <3% data rejection, reducing recollection by 80%.
80%
recollection reduced
OFFERINGS FOR AI LABS
Training data for every frontier model workflow
Each program covers a specific stage of frontier model development, from pre-training corpora to post-training preference data.
Pre-training corpora
We build licensed, deduplicated corpora filtered for quality, toxicity, and PII compliance.
SFT / CoT demonstrations
We author chain-of-thought reasoning traces across STEM, coding, and domain-specific tasks.
RLHF / Expert preference
Domain experts rank model outputs so your alignment reflects real-world quality, not crowd consensus.
Red teaming & safety
We run adversarial probing, jailbreak discovery, and safety evaluation across languages and modalities.
Multimodal data collection
We collect and annotate image, video, audio, and text data across 30+ countries for multimodal model training.
DEEP DOMAIN SOLUTIONS
How each solution works for AI labs
Each solution delivers outcomes for high-impact frontier-model use cases, from multimodal evaluation to red teaming.
.svg?tr=f-auto)
Multimodal evaluation
We run arena-style evaluation across audio, image, video, text, and code. Test model quality before it ships.
Learn more
.svg?tr=f-auto)
SFT / CoT demonstrations
We author reasoning chains across STEM, coding, and domain-specific tasks for fine-tuning.
Learn more
.svg?tr=f-auto)
Data annotation
Domain experts handle labeling, classification, segmentation, and entity recognition across every modality.
Learn more
Expert preference
Domain specialists rank model responses for alignment that reflects real-world quality standards.
Learn more
.svg?tr=f-auto)
Red teaming
We run adversarial probing for prompt injection, jailbreak discovery, and safety boundary testing.
Learn more
.svg?tr=f-auto)
.svg?tr=f-auto)
how we deliver
How these offerings get delivered
Three numbers explain why labs trust these programs: a credentialed expert pool, rigorous quality control, and fast activation at scale.
Quality | STEM Content Creation
100K
data units in 2 weeks
Generation, validation, transcription, and annotation run across STEM, engineering, creatives, and coding (Java and Python). Expert teams are deployed globally, including data scientists, raters, validators, domain SMEs, and industry consultants.
Multilingual STEM Annotation
280+
STEM-trained bilingual annotators
We run native-speaker evaluation pools across English, German, Spanish, French, Italian, Portuguese, Chinese, Arabic, Japanese, Hindi and more. These pools hold 98.4% cross-lingual consistency under ISO 17100 quality standards.
Depth | Domain Coverage
10+
factuality domains covered
Our factuality workflows span STEM, coding, legal, finance, health, safety, e-commerce, education, maps, and business productivity. Chain-of-thought testing, hallucination detection, and bias audits are built in.
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.
Contact US
Build training data for your frontier model
Tell us what you need. A program lead replies inside one business day.
- Talk to a domain-expert program lead
- Sample dataset returned in 5–10 business days
- Compliance docs (SOC 2, ISO, GDPR) on request
- NDA before any data exchange