Evals for AI SaaS Features

Josh Pitzalis

Starting at

$

6,000

About this service

Summary

I systematically diagnose and fix existing AI features that aren't performing as expected, delivering quantified improvements in just 3 weeks. Unlike generic monitoring tools, I create custom grading criteria specific to your domain and provide measurable before/after results that prove ROI. You get a complete quality framework, documented fixes, and the knowledge to maintain high AI performance long after the engagement ends.
What makes this unique: Most AI consulting focuses on building new features, but I specialize in rescuing underperforming AI systems with rapid, measurable improvements and custom quality standards tailored to your specific business domain.

Process

Week 1: Assessment & Instrumentation
Set up monitoring infrastructure to capture AI responses at scale (using tools like Langfuse, Braintrust, or custom dashboards)
Manual review of hundreds of AI outputs to identify all error modes (no assumptions - we discover problems through direct observation)
Create comprehensive error taxonomy based on actual failures, not predicted ones
Develop initial grading criteria document defining quality standards for your specific domain
Week 2: Analysis & Automated Detection
Build code-based evaluators for deterministic error detection (regex, length checks, tool usage patterns)
Create LLM-as-Judge evaluators for subjective quality assessment (tone, helpfulness, accuracy)
Quantify prevalence of each error type across your full dataset
Implement fixes for identified issues and optimize AI performance
Week 3: Validation & Knowledge Transfer
Validate improvements using proper train/dev/test data splits
Measure before/after performance across all error categories
Finalize grading criteria documentation with maintenance guidelines
Train your team on ongoing evaluation and quality assessment processes

What's included

  • A Grading Criteria Document

    A comprehensive, evolving document that defines what "good" AI output looks like for your specific use case. This includes scoring rubrics, quality thresholds, edge case handling rules, and examples of acceptable vs. unacceptable outputs. Unlike static documentation, this document is designed to be updated as your understanding of quality evolves, serving as the foundation for all future AI evaluation and improvement efforts.

  • Baseline Performance Report

    A quantified analysis of your AI system's current performance, documenting all identified error modes with specific metrics. This report includes failure rates, error categories, cost analysis, and impact assessment for each problem area. It serves as your "before" snapshot, establishing concrete benchmarks against which all improvements will be measured.

  • Final Improvement Report

    A comprehensive before/after comparison showing exactly what was fixed and by how much. This report quantifies the measurable improvements achieved across all error modes, including reduced failure rates, cost savings, and enhanced reliability metrics. It provides concrete evidence of ROI and serves as documentation for stakeholders on the tangible value delivered.

Duration

3 weeks

Skills and tools

Engineering Manager

AI Developer

AI Engineer

TypeScript

TypeScript

Industries

Artificial Intelligence
Computer Software
IT Infrastructure

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