Catyro leverages AI agents to automate complex data ingestion, parsing, mapping, and review workflows — turning unstructured documents into structured, actionable data.
Multi-agent pipeline with file detection, parsing, and intelligent schema mapping.
Automatically maps source columns to target schemas with confidence scoring.
Review, correct, and confirm AI-processed data before final ingestion.
Three-layer evaluation framework for model quality, retrieval accuracy, and agent trajectory analysis.
Production-grade tools for building, evaluating, and deploying AI systems with confidence.
Three-layer evaluation framework for agentic AI systems
A comprehensive evaluation platform that scores agentic AI systems across model output quality, retrieval accuracy, and multi-step trajectory correctness. Designed to run before every deploy — catching regressions in reasoning, retrieval, and routing before they reach production.
Load your agent topology — models, tools, retrieval stores, routing logic — and get a structured evaluation report with per-layer scoring, failure analysis, and regression tracking.
Evaluate the raw quality of LLM outputs independent of the surrounding system. This layer answers: "Is the model producing correct, grounded, faithful answers?"
Correctness, completeness, and relevance of generated responses against ground-truth or rubric-based evaluation.
Identifies fabricated facts, unsupported claims, and confident-sounding but incorrect statements.
Measures whether the model's output is grounded in the provided context — no extrapolation beyond source material.
Detects contradictions within outputs and across repeated runs of the same prompt.
Measured independently from model quality because the model's ceiling is the retrieval floor. If retrieval is broken, no model can compensate — this layer isolates retrieval performance.
Of the top K retrieved chunks, what fraction are actually relevant? Catches noisy retrieval that floods the context window.
Of all relevant chunks in the corpus, what fraction were retrieved? Catches retrieval that misses critical information.
Per-chunk relevance scoring — identifies which retrieved passages actually contributed useful context vs. noise.
Separates retrieval failures from model failures. When the answer is wrong, was it because of bad retrieval or bad reasoning?
For multi-step agents, the final answer is the wrong unit of analysis. This layer evaluates the entire sequence — which tools were called, whether routing decisions were correct, and whether the agent reached a valid terminal state efficiently.
Evaluates the full trajectory of agent actions — was each step logically justified? Were unnecessary steps taken?
For each decision point, did the agent invoke the right tool with correct parameters? Catches tool misuse and hallucinated tool calls.
When the agent needed to choose between sub-agents, APIs, or branches — did it route correctly? Measures decision-point accuracy.
Did the agent reach a valid terminal state? Did it terminate efficiently without loops, dead ends, or unnecessary retries?
Define your agents, models, tools, retrieval stores, and routing logic. The evaluator maps your system architecture and targets each component for layer-appropriate evaluation.
Run the full three-layer evaluation as part of your CI/CD pipeline. Block deploys that regress on any layer. Track scores across versions.
Per-layer scores, per-component breakdowns, failure case analysis, and trend tracking across evaluation runs.
Tailored automation solutions for each of our clients, built on the Catyro platform.
Insurance brokerage data automation solutions
End-to-end automation for insurance claim document ingestion. Emails with claim attachments are parsed, mapped to the target schema using AI agents, reviewed by a human, and ingested into the database — reducing manual processing time by 90%.
Financial data processing solutions
Exciting automation solutions are being developed for Credit Card Company. Stay tuned for updates.
View evaluation reports from the Catyro Agentic AI Evaluator. Paste a report JSON or load one from the API.
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