Beat the benchmarks with multimodal evaluation
Expert human evaluation across audio, image, video, and code, at production scale.
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's broken in AI evaluation right now
Three forces making your benchmark scores unreliable in 2026.
Eval scores may mislead
42%
of recent AI evaluation results were inflated because test questions had leaked into training data, making benchmark scores measure memorization, not ability.
Balloccu et al., 2024
Eval is English; users aren't
~55%
of web content is non-English, but most multimodal models are still being evaluated English-first, leaving quality gaps across most of their real-world user base.
Common crawl statistics
Regulators demand defensible records
Aug 2026
EU AI Act enforcement begins on training-data documentation, including the evaluation data your model was aligned on. Fines up to 3% of global revenue.
EU AI Act, Article 10
Proof in production
What moved for the model
Real numbers from four multimodal evaluation programs.
94.7%
Top-1 accuracy on a spatial-VR hand-tracking model, across 16 gesture classes in 20 markets, with the disparity gap held below 1.8%.
300K+
Preference evaluations across 9+ languages for a voice AI model, with 30,000 per language and native-speaker graders in every market.
50%
Faster model development on an 8-language virtual-assistant speech program, with 500,000+ prompts recorded, transcribed, and annotated.
0%
Error rate on 20,000+ prompts and responses across 5 content categories and 7 coding languages, delivered by 100+ SMEs in a 3-week turnaround.
Across the GenAI lifecycle
Multimodal evaluation across the training lifecycle
Each phase of training needs evaluation. The modalities, the stakes, and the evaluators change at each one.
Phase 01
Pre-Training
Evaluate corpus diversity before training begins. Catch demographic, linguistic, and modality gaps that become expensive to fix downstream.
Leading global consumer tech company
We consolidated 1.9M+ assets across 50+ countries into one program, cutting ML development time 40% and reducing the recollection rate 80%.
Phase 02
Fine-tuning
Grade demonstration data against modality-specific rubrics before it shapes the model. Domain experts calibrate until agreement is defensible.
Leading e-commerce company (AI Lab)
PhD-level music-theory experts graded 5,000 audio Q&A tasks across 8 genres, and the validated pilot scaled to 50,000 pairs.
Phase 03
Post-training
RLHF needs credential-verified evaluators who understand the domain. Generic raters cannot catch patient-safety errors in clinical outputs.
World-leading personal computing devices maker
We lifted plausibility from 61% to 83.7% across two RLHF cycles, deploying 110 NPI-verified clinicians and 46 board-certified specialists.
Phase 04
Deployment
Live model outputs drift across modalities. Continuous evaluation against production data catches degradation before users report it.
Driver safety solutions company
We annotated 10,000 dashcam video clips across 50+ object categories at >99% accuracy, accelerating go-to-market in new geographies.
Program Spotlight
The multimodal problem at production scale
Audio, image, video, text and code — graded by the evaluators each modality actually needs.
Customer story · Leading global tech & social media platform
1M+ multimodal assets across 30 cities to launch AI virtual try-on
The challenge wasn't asset volume. It was that every asset had to be usable. Human-garment interaction signals, fit dynamics, and body diversity were captured across 4 continents, structured for computer-vision model training, SFT, and production-grade validation. The output enabled launch acceleration of AI virtual try-on across digital commerce.
Modalities: images and motion video. Scope: apparel, footwear, and accessories across size ranges and body profiles. Infrastructure: production-ready training pipeline delivered with the dataset.
Modalities: images and motion video. Scope: apparel, footwear, and accessories across size ranges and body profiles. Infrastructure: production-ready training pipeline delivered with the dataset.
Where it applies
Where multimodal evaluation decides what ships
What rigorous, modality-aware evaluation changes for each sector’s models.
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AI labs and foundational models
Frontier models need Arena-style, expert-graded evaluation across audio, image, video, text, and code before a checkpoint is called better — not just a benchmark that moved.
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Technology
Consumer assistants and on-device models get judged across languages, accents, and form factors, so a launch clears quality in every market it ships to.
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Robotics
Vision-language-action models are scored on spatial grounding and multi-sensor perception, surfacing failures a standard benchmark never reaches.
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Banking and financial services
Document, voice, and chat models are evaluated against accuracy and compliance bars by credentialed reviewers before they touch a regulated workflow.
Learn more
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How WE DELIVER
How we run multimodal evaluation
The tooling, the workforce, and the QA discipline behind every evaluation run.
Platform · Agentic AI Studio
Evaluate
Multi-pass eval engine
We run multi-pass evaluation with IAA (inter-annotator agreement) scoring, Elo-style calibration for head-to-head model comparison, and outlier-judge flagging built in, and we co-design the rubric with your research team.
Workforce · Gigsourcing Platform
412K
vetted experts · 100+ countries
We field evaluators across 150+ languages: PhDs, MDs, engineers, lawyers, certified clinicians, and hundreds of domain scientists rather than anonymous click-workers with sub-48-hour program activation and credentialing and NPI verification for clinical programs.
QA · Monitor
35+
KPIs tracked per program
We provide program-grade dashboards across category, experiment, and model, surfacing real-time outlier detection, throughput tracking, and inter-rater reliability scores to the client team.
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 a multimodal lead
Tell us the modality, the domain, and the timeline. A program lead replies inside one business day.
- Talk to a real program lead, not a sales SDR
- Sample evaluation returned in 5–10 business days
- Rubric co-design included in scoping
- NDA before any model or data exchange