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Galileo AI

by Galileo

LLM evaluation platform with automated hallucination detection and RAG quality scoring — enabling APAC ML and data science teams to monitor production LLM application quality with per-response faithfulness, context relevance, and groundedness metrics.

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AIMenta verdict
Decent fit
4/5

"LLM evaluation and hallucination detection platform — APAC ML teams use Galileo to score LLM outputs for hallucination, completeness, and relevance, providing automated quality monitoring for APAC production RAG pipelines."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Hallucination scoring: APAC faithfulness and groundedness per LLM response
  • RAG metrics: APAC chunk utilization, context relevance, and completeness scoring
  • Production monitoring: APAC real-time quality alerts on statistical threshold breaches
  • Data flywheel: APAC low-quality example surfacing for fine-tuning datasets
  • Custom metrics: APAC domain-specific quality criterion definition
  • Dashboard: APAC quality trend visualization by model, prompt, and user segment
When to reach for it

Best for

  • APAC ML and data science teams building RAG applications who need continuous production quality monitoring — particularly APAC organizations where hallucination and incomplete responses have real-world consequences and where automated quality scoring must scale to production traffic volumes.
Don't get burned

Limitations to know

  • ! APAC hallucination scoring has false positive/negative rates — not a definitive quality oracle
  • ! Evaluation quality depends on context quality — APAC poor retrieval degrades scoring accuracy
  • ! APAC per-response scoring cost accumulates at high production traffic volumes
Context

About Galileo AI

Galileo AI is an LLM evaluation platform for monitoring and improving production LLM application quality — providing APAC ML and data science teams with per-response scoring for hallucination, completeness, chunk utilization, and context adherence in RAG applications. APAC teams that need automated quality monitoring at production scale use Galileo to detect quality degradations without manual review of every LLM output.

Galileo's Evaluate module scores APAC LLM outputs on multiple quality dimensions — factual accuracy relative to context (faithfulness), whether the response addresses all parts of the query (completeness), whether the retrieved chunks were actually used in the response (chunk attribution), and whether the response stayed within the bounds of retrieved context (groundedness). APAC RAG applications combine these scores into a composite quality dashboard.

Galileo's Observe module monitors APAC production LLM applications in real time — sampling production calls, scoring quality dimensions automatically, and surfacing statistical alerts when quality metrics degrade. APAC ML teams configure quality thresholds in Galileo's dashboard and receive alerts when, for example, faithfulness scores drop below 0.80 for a specific user segment or document category.

Galileo's data flywheel uses production monitoring to identify low-quality examples for APAC fine-tuning and evaluation dataset expansion — when Galileo detects consistently low-scoring responses for specific query types, those examples are surfaced for human review and annotation. APAC teams use this pipeline to continuously improve their LLM application quality using production data rather than relying only on pre-deployment test datasets.

Beyond this tool

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