Latent

Detect and intercept language model hallucinations before generated responses reach customers

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Latent to detect and stop model hallucinations in real time before your customers read them. It inspects your language model's internal state as it generates tokens, identifying unsupported claims, incorrect dates, and invented names without needing slow judge models. When Latent flags an ungrounded answer, it can hold the response, trigger a fallback message, or route the query to a more capable model. Reviewers can verify flagged responses in a dedicated queue, which generates labeled training data to improve model performance over time.

What Latent does

Install the Python SDK to monitor language model API callsDefine fallback responses when hallucinations are detectedReview flagged answers inside a centralized human validation queueForward operational alerts to Slack or PagerDuty channelsCalibrate detection thresholds automatically using baseline traffic samplesExport validated prompt-response pairs for fine-tuning workflowsTrack hallucination metrics using Prometheus endpoints and OpenTelemetry trace IDsInspects model activations token by token as text is generatedHolds fabricated responses before users can see themRoutes flagged outputs automatically to fallback text or stronger modelsHighlights exact words and conflicting sentences causing detection flagsSends alerts to Slack, PagerDuty, email, or webhooks when failure rates spikeSupports self-hosted deployment inside private VPCs with zero data egressExports reviewer feedback and labeled corrections directly for model fine-tuning