AI Evaluation & Reliability
Relytic evaluates RAG systems, AI agents, LLM applications, document pipelines, and machine-learning systems to determine what works, what fails, and what should be improved next.
The problem
AI systems are pipelines.
Without a proper evaluation process, teams change models or prompts without knowing whether they fixed the real problem. We turn those failures into measurable engineering questions.
Measure retrieval quality, reranking, groundedness, answer correctness, citation quality, multilingual behavior, and difficult document types.
Evaluate task completion, tool selection, workflow correctness, validation, escalation behavior, recovery, and cost.
Test prompts, model choices, structured outputs, hallucinations, consistency, instruction following, safety behavior, and regressions.
Compare OCR, extraction, classification, table reconstruction, and multimodal processing using task-specific benchmarks.
Benchmark models, perform sliced error analysis, investigate false positives and negatives, and measure deployment trade-offs.
Track quality alongside latency, throughput, cost, failures, user corrections, abstentions, and other operational metrics.
System anatomy
A benchmark is useful only if it helps answer a real engineering or business question.
We start from the decision and design the evaluation around it.
Measure what matters
Representative test data should reflect real documents, users, languages, edge cases, and failure modes rather than only generic benchmarks.
The metric is chosen because it reflects the failure we care about, not because it is popular.
Evidence
01
Evaluation datasets and pipelines compared retrieval approaches, rerankers, multilingual embeddings, and document-processing methods across hundreds of thousands of documents in 7 languages, used by hundreds of employees and handling thousands of queries per day.
Tools and methods
02
84.5% → 92.0%mAP@50
Architecture benchmarking, sliced failure analysis, targeted data augmentation, annotation correction, and reevaluation improved a production vision system.
Determine whether an AI feature is ready for real users and identify the failure modes that need to be addressed first.
Build an evaluation dataset, measure retrieval and answer quality, compare configurations, and identify the largest bottlenecks.
Compare proprietary and open-source models, OCR tools, rerankers, embedding models, or other components on your actual data.
Create realistic workflow scenarios and measure whether the agent completes tasks correctly, safely, and consistently.
Build repeatable tests so future prompt, model, retrieval, or workflow changes can be evaluated before release.
Define the signals, sampling, human review, monitoring, and feedback process needed to measure quality after deployment.
Honest advice
Some failures can be measured automatically and repeatedly. Others require expert judgment.
High-stakes or domain-specific outputs may need human-reviewed evaluation sets, disagreement analysis, or expert validation.
We use automation where it provides reliable signal and human review where the business meaning cannot be reduced safely to an automatic score.
How we work
Translate vague goals such as good answers or accurate extraction into measurable criteria tied to the workflow.
Create evaluation cases that reflect real documents, users, languages, edge cases, and failure modes.
Evaluate individual components where useful and the complete end-to-end system where it matters.
Slice results by meaningful categories and inspect retrieval misses, tool failures, and other error patterns.
Use discovered failures to guide changes, then rerun the benchmark to verify improvement without new regressions.
Translate vague goals such as good answers or accurate extraction into measurable criteria tied to the workflow.
Create evaluation cases that reflect real documents, users, languages, edge cases, and failure modes.
Evaluate individual components where useful and the complete end-to-end system where it matters.
Slice results by meaningful categories and inspect retrieval misses, tool failures, and other error patterns.
Use discovered failures to guide changes, then rerun the benchmark to verify improvement without new regressions.
Next step
Book a 30-minute conversation with Relytic to discuss the system, the failures you are worried about, and what evidence you would need to make the next engineering decision confidently.