Annotate with the precision your model needs
Specialist annotation across images, video, audio, and text. Production throughput and quality gates that don't flinch.
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
Why annotation quality is now a model capability question
Annotation errors don't stay in the training set. They replicate into model behavior at every inference.
Label Quality
3.3%
average label error rate found in commonly used machine learning datasets, enough to invert benchmark rankings and corrupt fine-tuning signals when left uncorrected.
Northcutt et al., MIT CSAIL
Compute Waste
~80%
of AI project failures trace to data quality problems rather than algorithm choices, with annotation noise, coverage gaps, and inconsistent labeling schemas topping the list.
Gartner Research, 2022
Training Cost
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25%
accuracy drop observed when label noise is introduced to training data. Models trained on poorly annotated datasets don't just underperform, they confidently produce wrong outputs at scale.
noisy label research, SitePoint
Proof in Production
Numbers from programs already in production
Three programs, three modalities — every number traced to a live delivery.
35%
productivity increase for annotation specialists, with 6M+ images labeled across an automotive facial landmark detection program spanning multiple sensor types and lighting conditions.
50K
accepted annotation hours across a robotics vision-language-action program, covering motion primitives, object interaction, spatial reasoning labels, and visual grounding for embodied AI.
3x
faster annotator onboarding versus industry baseline, with 98.5% compliance rate maintained at scale across a global commerce moderation and annotation marketplace program.
Across the GenAI lifecycle
Annotation work that fits where your model actually is
Different GenAI lifecycle phases demand different annotation types, ontologies, and specialist profiles. The wrong labels at the wrong phase don't just fail; they actively mislead.
Phase 01
Pre-Training
Pre-training annotation builds the foundational data distribution: diversity, coverage, and language representation across multilingual transcription, diarization, and dialect-level labeling.
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Multi-locale voice data
We delivered 37,000+ hours of native-speaker transcription across 16 markets with dialect-level quality gates.
Pre-training speech · Voice AI platform
Phase 02
Fine-tuning
Fine-tuning annotation demands precision: consistent ontologies, tight inter-annotator agreement, and task-specific calibration for landmark placement and interaction-state classification.
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Palm gesture recognition
We annotated 50,000+ participant sessions with keypoint and gesture-state labels for a consumer device.
Fine-tuning gesture model · Consumer device maker
Phase 03
Post-Training
Post-training annotation powers content moderation, safety classification, and policy compliance through always-on programs that absorb throughput spikes without degrading quality.
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Commerce content moderation
We handle 200,000+ moderation tasks per month with real-time SLA compliance for a global marketplace.
Post-training content safety · Global marketplace
Phase 04
Deployment and red-teaming
Deployment-phase annotation covers adversarial data generation, vulnerability labeling, and red-team scenario annotation, and it requires domain specialists in attack taxonomies rather than generalist labelers.
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Penetration testing annotation
We build adversarial scenario labels and red-team prompts with certified security specialists.
Deployment red-teaming · Cybersecurity platform
Program spotlight
One program. Seven dimensions of annotation complexity.
Multimodal annotation across 8 device types and 38 languages, with no sampling and no extrapolation.
Client Program
140K+ assets. 38 languages. 8 device form factors.
Smartphone manufacturer needed multimodal annotation spanning images, audio, and interaction data across 8 client-branded device configurations, simultaneously in 38 languages across 28 countries. Standard annotation pipelines collapse under that combinatorial load.
Firstsource built a specialist workforce calibrated per modality, per language, and per device type, delivering 140K+ labeled assets in 24 weeks with client-defined quality thresholds enforced across every language-device combination. No sampling. No extrapolation.
Firstsource built a specialist workforce calibrated per modality, per language, and per device type, delivering 140K+ labeled assets in 24 weeks with client-defined quality thresholds enforced across every language-device combination. No sampling. No extrapolation.
Program dimensions
140K+
annotated assets delivered
28
countries in scope
8
client-branded device types
24 weeks
delivery timeline
Where it applies
Where annotation drives production outcomes
The same precision discipline, calibrated to what each sector’s models have to clear.
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AI labs and foundational models
Frontier models can't ship without expert-graded STEM, code, and multilingual annotation powering RLHF cycles, Arena-style evaluation, and cross-lingual consistency across 24+ languages.
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Technology
Gesture models, voice assistants, and anti-spoofing systems depend on labeled data across 20+ markets, device form factors, and skin tones to clear global launch thresholds.
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Robotics
Vision-language-action models don't reach manipulation or navigation benchmarks without spatiotemporal labels across LiDAR, point clouds, and multi-sensor fusion data.
Learn more
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Banking and financial services
FinCrime, credit, and compliance models need credentialed annotators labeling 35+ PII types, transaction patterns, and financial harm taxonomies to pass audit and clear regulatory submission.
Learn more
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How we deliver
Annotation at scale doesn't mean annotation at lower quality
Specialist teams per language and modality, calibrated to your ontology before the first batch ships.
Annotate
150+
languages supported
We assemble specialist annotation teams per language, modality, and task type rather than pooling generalist labor, and each team is calibrated to the client's ontology before first delivery.
Gigsourcing
Days
to specialist capacity
Our gigsourcing infrastructure assembles domain-matched specialists at speed, with client-branded onboarding, task-specific calibration, and continuous quality monitoring built in from day one.
Quality Assurance
Multi-layer
QA at every stage
We run expert annotation, an independent reviewer pass, automated schema validation, inter-annotator agreement scoring, and client-gate sign-off before any batch ships
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
Talk to an annotation lead
Tell us what you're building. We'll scope the program.
- Modality requirements and scale
- Language and locale coverage
- Ontology design and schema review
- Quality thresholds and QA structure
- Ramp timeline and throughput targets