AI/ML Benchmarking LLMs for customer support in 3 days
How Future AGI's observability platform helped benchmark Mistral, Claude, and GPT-4o across 12+ metrics in just 3 days.
Key Results
What would have taken weeks of manual evaluation was compressed into three days with objective, data-driven results we could trust.
Use Cases
The Challenge
Organizations deploying AI for customer support face a complex model selection problem. Multiple performance dimensions matter simultaneously-response accuracy, tone, cost-effectiveness, and latency all influence customer experience outcomes.
Manual evaluation of LLMs is slow, subjective, and impossible to scale across the growing number of available models.
The Solution
Future AGI’s observability platform streamlined the entire evaluation process:
Automated Instrumentation
Integration with OpenAI, Anthropic, and Mistral APIs using minimal code. Each model was tested against the same customer support dataset spanning billing issues, account access, product complaints, and technical support.
Comprehensive Metrics
The platform automatically collected 12+ metrics per model including:
- Response Accuracy & Completeness
- Politeness & Tone Analysis
- Content Moderation & Bias Detection
- Data Privacy Compliance
- Cultural Sensitivity
- Latency & Cost
Unified Dashboard
A centralized visualization compared system and evaluation metrics across all models, with a “Choose Winner” feature for objective ranking using weighted priorities.
The Experiment
Three models tested:
- Mistral Large
- Claude Sonnet 3.5
- GPT-4o
Test dataset: Queries representing a fictitious technology company with scenarios spanning billing, account access, product complaints, technical support, and shipping inquiries across various emotional tones.
The Results
Claude Sonnet won based on weighted scoring across all dimensions:
| Metric | Claude Sonnet | GPT-4o | Mistral Large |
|---|---|---|---|
| Avg. Latency | 4.8s | ~5.2s | 5.7s |
| Content Moderation | >95% | >95% | >95% |
| Cultural Sensitivity | Highest | Comparable | Comparable |
| Cost | Moderate | Lowest | Highest |
The entire benchmarking process-from setup to final decision-took just 3 days instead of weeks. Dashboard analysis condensed decision-making from hours to minutes.
More from AI/ML
Autonomous agents in production: 95% task completion rate
An AI automation company used Future AGI to test multi-step workflows, detect loops, and achieve 95% task completion in production.
Building a coding agent that ships safe code to production
A startup building an AI coding agent used Future AGI to evaluate generated code across languages, block destructive ops, and ship reliably.
Want similar results?
Start building reliable AI systems with Future AGI today.