Cost-Quality Optimization via Historical Replay
Portkey Hackathon Project
Team 23
Neeraj
Rithwik
Eshwitha SAi
Problem Statement:
The Cost of Over-Engineering AI Agents with blind model choices
  • Enterprise teams waste budget on frontier models for tasks that don't need them—GPT-4 for copy, Claude Opus for FAQs.
  • The problem: Non-technical teams default to expensive models without expertise to optimize. They need a smarter solution.
80%
Potential Cost Savings
By using right-sized models
What are we building:
Intelligent Model Optimization Through Historical Analysis
Concept
01
Auto-Capture AI Traces for agents
Integrate Portkey to log all prompt-completion pairs
02
Configure LLM Judge & Guardrails
Define quality metrics and minimum thresholds
03
Benchmark LLM Alternatives
Test different models on your actual workload
04
Generate Recommendations
Receive data-driven guidance on optimal model selection
Real-World Example: Lisa's AI Agent Ecosystem
Marketing Team
Campaign Copy Generator agent creating ad variations and product descriptions
HR Operations
Employee support agent answering policy questions and benefits inquiries
Each team currently uses frontier models by default, leading to unnecessary costs. Our solution identifies optimization opportunities across all agents in the company.
Configuration
Flexible Evaluation Framework
Agent builders maintain full control over quality standards through configurable evaluation components. Define your own judges and guardrails to ensure model recommendations align with your specific requirements.
Custom Judges
Define quality metrics specific to your use case—accuracy, Factual grounding in the policy, completeness, safety, etc
Evaluation Criteria
Define a scoring scale to assess alignment, accuracy, and guardrails effectiveness.
Guardrails
Implement regulatory compliance, brand safety, and inclusive language requirements to maintain quality thresholds as per company guidelines
Attach Company Context
Connect external sources for verification of required disclosures, prohibited practices, and industry standards.
Live Demo
Actionable Reports
System Overview
Made with