The difference between AI risk and AI resilience is safety and alignment. We provide the expert human oversight to evaluate, validate and harden systems at scale, so models behave as intended and can withstand emerging threats.
Every update, integration and new usage pattern changes the model after launch. What’s safe on day one doesn’t stay safe on its own. We deliver ongoing performance management across foundation and frontier models, multimodalities, agentic AI, autonomous vehicles and robotics.
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Meet Our People
Rick Balzano
VP, Sales Enablement – AI Safety
Rick has 8+ years of experience in enterprise tech, specializing in scaling LLM training services and advancing AI safety frameworks for global organizations.
AI SERVICES
Model safety at every layer
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Training data quality
We curate diverse, high-quality datasets that reduce bias and improve model precision for reliable AI outputs.
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Human-in-the-loop oversight
Expert annotators guide AI behavior through reinforcement learning and continuous feedback to align with human values.
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Multimodal red teaming
Red teams stress-test AI systems with adversarial prompts and edge cases to identify vulnerabilities before launch and test how models behave under pressure and unexpected conditions.
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Real-time monitoring
We design escalation paths, monitoring workflows and human-in-the-loop (HITL) systems to catch and correct harmful or unintended actions.
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Clinical AI auditing
We identify multimodal vulnerabilities early and embed safeguards to prevent misuse, bias and downstream risks throughout the model lifecycle.
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Trustworthy AI agent deployment
We implement safeguards for autonomous AI agents, ensuring reliable decision-making with appropriate human oversight.
EXPERT TAKE
3 ways AI fails mental health
and how to make models safer
Many people turn to chatbots for emotional support, but standard LLMs can’t recognize a brewing mental health crisis. Left unchecked, they could give a false sense of comfort and even lead to self-harm.
In a Fast Company article, TaskUs Trust & Safety leaders explain that platforms are responsible for their tool’s behavior and recommend a clinically grounded strategy to engineer trustworthy AI.