Find the bias before your user
Across 6M+ evaluated data points in healthcare and automotive AI, bias was identified and removed before a single model reached production.
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
Why AI safety can't be a post-launch problem
Three documented forces turning AI safety from a nice-to-have into a board-level requirement.
Hallucinations remain unsolved
94%
of frontier models tested still hallucinate on complex real-world tasks.
Stanford AI Index 2026
Governance demand is surging
17%
year-on-year growth in AI governance roles—the fastest-growing AI job category.
Stanford AI Index 2026
Transparency gap is glaring
40/100
average score on the Foundation Model Transparency Index across frontier models.
Stanford AI Index 2026
PROOF IN PRODUCTION
How safety evaluation actually moved the numbers
Four outcomes from real Data Services AI safety programs. No padding.
83.7%
clinical knowledge plausibility—up from 61%
We deployed 110 NPI-verified clinicians and 46 board-certified specialists to grade clinical outputs across 2 RLHF cycles.
6M+
images evaluated for bias across demographic range
We annotated 68-point facial landmarks across a full range of skin tones and tagged eye-blink status to strip demographic bias from the driver-monitoring dataset.
38,000+
notification assets collected under strict data-handling controls across 19 locales
We ran collection through access-controlled hubs with 700 vetted native participants and in-flow PII redaction across all 19 regulated locales.
10,000+
GenAI and non-GenAI assets generated for scalable, reproducible penetration-test cases
We built a library of GenAI and non-GenAI image and video assets, mapped a platform-specific taxonomy of attack vectors, and ran penetration tests that simulated identity and listing fraud.
What the evaluation surface looks like
Standard safety benchmarks measure known prompts in controlled conditions. AI safety evaluation measures what the model does when it encounters inputs it was not designed for.
Stage 01
Clinical and domain knowledge grounding
We build domain-specific knowledge frameworks that establish what "correct" looks like in your field, then evaluate model outputs against that standard at scale.
Domain benchmark construction
We validate clinical, legal, and financial knowledge frameworks against expert gold standards.
Stage 02
Bias audit and fairness evaluation
We run structured bias audits across demographic, geographic, and linguistic dimensions, and every audit produces an impact-weighted bias inventory rather than a simple pass or fail.
Demographic bias detection
We test gender, age, ethnicity, facial-landmark, and linguistic fairness across user groups.
Stage 03
Policy alignment and harm classification
We design and validate harm taxonomies against your content policy, classifying outputs across rephrasings, languages, and edge cases.
Harm taxonomy design
We classify violence, toxicity, misinformation, and brand-safety risks using curated RLHF training data.
Stage 04
Hallucination detection and factual grounding
We run systematic factual probing across knowledge-intensive domains, identifying failure patterns and measuring the hallucination rate by topic and task type.
Factual probing at scale
We evaluate source attribution and produce correction datasets for targeted fine-tuning.
Stage 05
Safety calibration and refusal tuning
We tune refusal behaviour for accuracy, so the model refuses the right requests, at the right threshold, with the right explanation, across languages.
Refusal quality grading
We run cross-turn safety evaluation with gold-standard refusal datasets for RLHF fine-tuning.
WHY FIRSTSOURCE
61% to 83.7% plausibility. Before a single patient interaction.
Client Program
Clinical AI plausibility lifted from 61% to 83.7%
AI readiness evaluation: clinical knowledge framework
World-leading personal computing devices manufacturer
· Healthcare · 20k+ tasks across 3 knowledge layers · 16-week program
World-leading personal computing devices manufacturer
· Healthcare · 20k+ tasks across 3 knowledge layers · 16-week program
A world-leading personal computing devices manufacturer needed multi-layered AI evaluation before deploying across 14 hospital networks. The program required certified model trustworthiness across 3 cognitive levels, from patient reasoning to specialist-grade accuracy.
Firstsource deployed a blended workforce across 3 credential tiers with NPI-verified clinicians and board-certified specialists, building domain-specific calibration sets with blind dual-review adjudication.
Firstsource deployed a blended workforce across 3 credential tiers with NPI-verified clinicians and board-certified specialists, building domain-specific calibration sets with blind dual-review adjudication.
Program Dimensions
280
general-population annotators
110
licensed clinicians (NPI verified)
46
board-certified specialists
2
integrated RLHF cycles
DEEP DOMAIN SOLUTIONS
Where AI safety connects
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Red teaming
We probe your model's attack surface through adversarial testing and jailbreak discovery.
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Expert preference ranking (RLHF)
We generate human preference data for alignment, fed by your safety findings.
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How We Deliver
AI safety evaluation is only as good as the evaluators running it
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Studio
35+
KPIs tracked
Agentic AI Studio maps domain gaps, bias dimensions, and hallucination risk with IAA-scored grader calibration.
EXPERTS
350K+
expert network
We match clinicians, lawyers, and financial experts to your model's risk surface and activate them in under 48 hours.
Scale
50+
languages covered
We run native-speaker safety evaluation across 100+ countries, testing bias and policy alignment across language variants.
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 AI safety lead
Tell us what you're evaluating. We'll scope the safety program.
- Model type and deployment context
- Domain-specialist requirements
- Bias dimensions and fairness criteria
- Languages and locale coverage
- Compliance and reporting needs