Latent

Teams running LLM products need to catch made-up or unsupported answers before they reach customers, but checking every answer with another model (LLM-as-judge) is slow and expensive and requires sending output outside the environment to be reviewed. Guardrails, judges and dashboards only look at the finished output, not at what the model was doing while it generated the answer, so they miss signals available earlier in the process. The page quantifies this cost directly: a judge model costs $10.61 per 1,000 answers versus $0.02 with Latent on API models, and checking is claimed to be 103 times faster when Latent reads the model's own activations instead of using a separate judge call.

Latent instruments a model at runtime and reads its internal state token by token as it writes an answer, rather than only judging the finished text. A Python SDK (runlatent) auto-instruments calls to OpenAI, Anthropic, Gemini or Ollama, sending each finished answer to Latent's reader for scoring off the request path; for open-weight models served on vLLM in a customer's VPC, Latent reads the model's own activations and can act mid-stream, cutting a streamed answer at the first unsupported sentence. Flagged answers can be held and replaced with fallback text or a stronger model's answer, or delivered and logged to a review queue, based on a policy set in the console. Each flag points to the specific sentence and words that drove it and the source passage it most relied on, and reviewer decisions (confirm or clear) are kept as labeled examples for recalibration and for exporting fine-tuning or preference data. Latent also runs a calibration step against a sample of a customer's own traffic (judge included, under an hour, no restart) and offers alerting via Slack, PagerDuty, email or webhook when flag rates, scoring speed or traffic drift shift.