Pre-training data that lifts the ceiling
Text, audio, image, video, and code corpora across 150 languages — every modality, every market.
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
Market forces
What breaks at the corpus
Model quality is decided before training starts. These three corpus failures compound upstream, and nowhere downstream in the lifecycle can fix them.
Training AI on AI hurts quality
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2X
Models trained on AI-generated content degrade twice as fast, and the damage compounds with every successive generation.
Shumailov et al., Nature, July 2024
Contaminated data inflates benchmarks
42%
of recent AI evaluation results were inflated because test questions leaked into training data, making models look better than they are.
Balloccu et al., arXiv, Feb 2024
Most models can't speak regional languages
<50%
Frontier models lose close to half their accuracy when tested in regional dialects, a gap that originates in how the pre-training corpus was built.
Stanford AI Index 2026, Responsible AI
Proven outcomes
What pre-training programs deliver
3.9M+
Assets delivered across 150 languages and 100+ countries
50K
Hours of accepted egocentric VLA training data for a single robotics pre-training corpus
160K
Raw images delivered while maintaining flawless background status for next-generation CV models
<3%
Data rejection rate on a 140K+ asset device pre-training program
How the corpus gets built
Two stages stand between raw data and a training-ready corpus
From the first seed prompt to a validated corpus — the work that decides what a model can learn.
Inception
- Prompt engineering & seed data design
- Real-world data collection & sourcing
- Instruction dataset authoring
- Multilingual corpus construction
- Synthetic data generation (text, code, multimodal)
- Domain expert content creation
- Counterfactual & adversarial data generation
- Crowd-sourced data collection at scale (150+ countries)
Stage 2
Data curation
- Web & document data quality filtering
- Toxicity & PII scrubbing
- Data mix strategy & proportion tuning
- Multimodal alignment (image-text, audio-text, video-text)
- Deduplication & near-duplicate removal
- Domain classification & routing
- Metadata tagging & provenance tracking
- Pre-training benchmark validation sets
Ask yourself: are your AI initiatives building your institutional memory — or someone else's?
Kairos is what closes the gap.
Customer Proof
Structured data from documents that fit no schema
How a healthcare claims program turned 50K+ unstructured records — PDFs, scanned images, handwritten notes — into clean, coded data.
Live | Healthcare Claims Processing
98.2% accuracy. 97.8% precision. On medical documents that resisted both.
Automated extraction of structured data from unstructured medical documents, across 50K+ claims records.
- 98.2% extraction accuracy, 97.8% precision
- 47 fields extracted per claim, 2 auto-flagged for human review
- Custom NER models tuned for medical terminology
- Triple-pass validation by domain experts, with a detailed error taxonomy
- HIPAA-compliant across PDFs, scanned images, and handwritten notes
WHO WE SERVE
Every industry’s pre-training problem looks different
The corpus requirements for a frontier lab, a robotics studio, and a hospital have almost nothing in common. The operating model and quality controls do.
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AI Labs & Foundational Models
Build corpora that set the knowledge ceiling, not the bias floor
- Source and validate content across text, audio, image, video, and code modalities
- Control for contamination, duplication, and synthetic data degradation
- Cover underrepresented languages and domains before gaps become model blind spots
- Grade corpus quality with domain experts, not crowd consensus
Technology & Hyperscalers
Collect device and interaction data at global scale without quality loss
- Capture speech, video, and gesture data across locales and device types
- Maintain brand-safety and privacy compliance across all collection environments
- Scale data collection without trading off rejection rates or delivery timelines
- Cover demographic and environmental variation that lab-only collection misses
robotics
Collect embodied data from the real world, not just simulation
- Source egocentric manipulation data across diverse household environments
- Manage device logistics, participant coordination, and yield at collection scale
- Cover environment variation that controlled-lab collection cannot replicate
- Build reusable data pipelines extensible to new tasks and device form factors
Healthcare
Source clinical-grade text that licensed practitioners actually authored
- Recruit and manage clinician contributors across medical specialties
- Cover clinical knowledge domains that general-purpose corpora miss entirely
- Maintain regulatory compliance from collection through delivery
- Validate content against clinical best practices, not generic quality rubrics
Banking and Financial Services
Build financial corpora that cover regulations across jurisdictions
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- Source regulatory, earnings, and compliance text across US, EU, UK, and APAC
- Span the terminology and document types that financial models must understand
- Cover structured and unstructured financial data with schema-compliant annotation
- Ensure copyright-clear sourcing for commercial training use
Retail
Capture product and customer interaction data at catalog scale
- Source multilingual product data across markets without quality degradation
- Collect visual and language signals that product AI models need to rank and recommend
- Cover seasonal, trend, and category variation before it becomes model blind spots
- Scale image, video, and text collection without bottlenecking on contributor supply
The full GenAI lifecycle
Pre-training is stage one of four
The corpus sets the ceiling. Fine-tuning, post-training, and deployment decide how close a model gets to it — and Firstsource delivers the data for every stage, under one operating model, without switching vendors.
Stage 1 · You are here
Pre-Training
Corpus building across text, audio, image, video, and code — the foundational world knowledge a model learns from.
Stage 2
Fine-Tuning
Expert domain demonstrations — SFT, CoT, and zero-error prompting that teach the model a specific domain.
Stage 3
Post-Training
RLHF preference data, safety alignment corpora, and regression sets for models accountable outside the benchmark.
Stage 4
Deployment
Continuous data engine, live edge-case pipeline, and retraining triggers — from launch to the next version.
COMMON QUESTIONS
What pre-training buyers ask
What types of pre-training data does Firstsource collect and deliver?
Text, audio, image, video, code, and sensor data, collected, annotated, and quality-validated across 150+ languages and 100+ countries. Programs span web-scraped corpus curation, in-facility moderated collection, field and household capture, and expert-authored STEM and domain content. Every dataset is contamination-tested and deduplication-checked before delivery.
How does Firstsource handle multilingual pre-training coverage?
a large pool of vetted contributors across 100+ countries, with native-speaker collection in 150+ languages. ISO 17100-aligned multilingual quality control, demographic quota enforcement to prevent language skew, and regional dialect coverage, so the corpus does not replicate the English-first bias that frontier models lose accuracy from at regional dialect testing.
What is the difference between pre-training data and fine-tuning data?
Pre-training data builds the model’s foundational world knowledge: it learns language, structure, and basic reasoning from the corpus. Fine-tuning data teaches the model to apply that knowledge in a specific domain, following specific instructions, in expert-grade demonstrations. Pre-training sets the ceiling; fine-tuning determines how close the model gets to it. Firstsource delivers both under the same operating model, without switching vendors.
How does Firstsource ensure pre-training data is copyright-clean?
Copyright-clear sourcing is a program-design requirement, not an afterthought. Firstsource uses first-party capture, licensed datasets via its OTS Data capability, and contributor-generated content with explicit rights transfer. Given the US Copyright Office’s May 2025 finding that commercial AI training on copyrighted works is presumptively infringing without a valid fair use defense, sourcing clarity is not optional at pre-training scale.
How quickly can Firstsource activate a pre-training program?
Sub-48-hour program activation via the Gigsourcing Platform, with workforce deployed, tooling provisioned, and first data flowing within two business days of sign-off. For in-facility or moderated field collection, activation timelines depend on geographic scope and device requirements; a program lead will confirm during scoping.
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
Collect it. Manage it. Train your models on it.
Tell us what you’re building. A program lead replies inside one business day.
- Talk to a real program lead
- Sample dataset returned in 5–10 business days
- Compliance docs (SOC 2, ISO, HIPAA-aligned) on request
- NDA before any data exchange