# Agentic AI-Guided Evaluation Platform

## Executive Summary

Most AI systems fail not because of insufficient data or compute, but because companies cannot reliably determine if their systems work for customers. This platform makes rigorous evaluation accessible through conversational AI, enabling companies to build, optimize, and trust their AI systems from day one. The platform is deployed and operational, with open-source release and one-click customer deployment planned.

## Why Evaluation is Foundational

Evaluation is not observability or monitoring. Evaluation is the basis upon which all optimization occurs—it determines what companies build toward. The most successful AI achievements, from AlphaGo to ChatGPT, share one common element: robust evaluation systems that enable reinforcement learning and continuous improvement. Without trusted evaluation, all downstream optimization amplifies flawed measurements rather than genuine progress.

## Problem: Current Tool Limitations

Existing evaluation frameworks (Promptfoo, fmeval, DeepEval) provide powerful capabilities but require significant expertise. They follow traditional software patterns: configuration files, CLI commands, manual dataset creation. This creates four problems:

- **Accessibility barrier**: Teams without ML expertise cannot evaluate systems effectively
- **No guardrails**: Users can misconfigure evaluations, leading to false confidence in flawed systems
- **Isolated workflows**: Evaluation happens separately from analysis, iteration, and optimization
- **Single-judge reliability**: Evaluations depend on one LLM judge, inheriting that model's biases and blind spots

The result: companies either skip rigorous evaluation entirely or invest months building custom tooling—and even then, they cannot trust that their measurements reflect real-world performance.

## Solution: Agentic AI-Guided Evaluation

An expert LLM agent with deep knowledge of evaluation best practices serves as the primary interface, while enterprise-grade tools provide the execution layer. Users describe what they want to evaluate through natural conversation and upload their own documents—PDFs, knowledge bases, product documentation. The platform automatically generates synthetic question-answer pairs grounded in the user's actual content, creating evaluation datasets that reflect real customer scenarios rather than generic benchmarks. The agent recommends and validates evaluation criteria, and catches misconfigurations before execution.

To address single-judge reliability, the platform implements a jury system inspired by recent research on [multi-agent verification](https://arxiv.org/abs/2502.20379). Multiple judges from different model families each analyze distinct aspects of every response—whether the answer is correct, whether the reasoning is sound, whether the response is complete. The combination of diverse judges evaluating multiple dimensions produces a final score that is both fairer across model families and more comprehensive than any single-judge approach.

The complexity is fully managed—users request an evaluation and the platform handles dataset generation, judge configuration, execution, and aggregation. This makes scientific evaluation rigor accessible through conversation, providing the trusted foundation companies need to continuously improve their AI systems.

## Status

- Platform deployed and operational
- Website live
- Open-source release planned
- One-click customer deployment planned
