Log10

Captures user feedback on LLM responses in production environments as they occur, creating a continuous stream of quality signals. Enables teams to identify hallucinations, incorrect answers, and user dissatisfaction immediately rather than through delayed batch analysis.

Automatically calculates and tracks accuracy metrics specific to customer support and chatbot use cases. Provides quantifiable measurements of model performance against business-relevant quality benchmarks without requiring manual evaluation.

Improves LLM accuracy and reduces hallucinations through optimization techniques that don't require expensive full model retraining. Uses feedback signals to adjust behavior and improve outputs at inference time or through lightweight fine-tuning.

Continuously monitors deployed LLM systems for quality degradation, accuracy drops, and emerging failure patterns. Provides alerts when performance falls below thresholds or anomalies are detected.

Records and stores complete conversation histories with LLM outputs, user feedback, and context. Enables teams to replay, analyze, and learn from specific interactions to identify improvement opportunities.

Handles production deployments of LLMs at scale without performance degradation. Manages infrastructure, load balancing, and optimization to support high-volume customer interactions.

Provides pre-built quality metrics and evaluation frameworks tailored to customer support and chatbot use cases. Measures dimensions like answer correctness, tone appropriateness, and customer satisfaction.

Identifies when LLMs generate false or unsupported information and applies techniques to reduce hallucination rates. Monitors for confidence mismatches and factual inconsistencies in responses.

Creates an automated workflow that converts user feedback into model improvements. Identifies high-impact feedback patterns and applies optimizations based on aggregate signals.