Cleanlab TLM

Cleanlab's Trustworthy Language Model (TLM) is a specialized product that wraps any LLM with a reliability layer — quantifying the confidence of each output and flagging responses that are likely to be hallucinated, incorrect, or unreliable. Rather than accepting LLM outputs at face value, TLM provides a per-response trustworthiness score that enables applications to handle uncertain AI outputs differently from reliable ones.

TLM works by running internal consistency checks and calibration processes that assess how confident the underlying model is in each specific response. High-confidence answers can be automatically accepted; low-confidence answers can be flagged for human review, triggering alternative responses, or simply refused — creating a more reliable pipeline than naively trusting all LLM outputs equally.

This capability is particularly valuable in high-stakes domains: medical AI where hallucinated clinical information is dangerous, legal AI where fabricated citations could be professionally damaging, financial AI where incorrect data could cause losses, and any application where the cost of AI errors is high. TLM transforms LLMs from unreliable oracles into systems with calibrated, actionable uncertainty quantification.