by oshadha89 | 2025-11-12 16:15:21+00:00
# Building Exeta: A High-Performance LLM Evaluation Platform

## Why We Need This Platform

The AI landscape has exploded. Every week, new language models emerge, each promising better performance. But *how do you actually know if your LLM is working well?*

Most teams are flying blind. They deploy models, hope for the best, and discover issues only when users complain. This isn't just inefficient—it's dangerous. A hallucination in a medical chatbot or bias in a hiring tool can have real-world consequences.

Traditional software has unit tests and CI/CD pipelines. But LLM evaluation? Most teams are still manually checking outputs or relying on ad-hoc scripts.

*We built Exeta to solve this.* It's a production-ready, multi-tenant evaluation platform that gives you the same confidence in your LLM applications that you have in traditional software.

## How Exeta Differs

### 1. Multi-Tenant SaaS Architecture Built for teams and organizations from day one. Every evaluation is scoped to an organization with proper isolation, rate limiting, and usage tracking.

### 2. Comprehensive Metrics - *Correctness*: Exact match, semantic similarity, ROUGE-L - *Quality*: LLM-as-a-judge, content quality, hybrid evaluation - *Safety*: Hallucination detection, faithfulness, compliance checks - *Custom*: Pluggable architecture for custom metrics

### 3. Performance That Scales - 10,000+ requests/second throughput - <10ms average latency - <100MB baseline memory - 1,000+ concurrent connections

### 4. Production-Ready Rate limiting, intelligent caching, monitoring, multiple auth methods (API keys, JWT, OAuth2), and auto-generated OpenAPI docs.

## Why Rust?

*Performance*: LLM evaluation involves heavy I/O. Rust's performance means we handle more load with fewer resources.

*Reliability*: Rust's type system catches bugs at compile time. In production systems handling critical evaluations, reliability isn't optional.

*Right Tool*: The dashboard uses Next.js/TypeScript, but the evaluation engine—fast, reliable, scalable—needs Rust.

## Real-World Examples

*Customer Support Chatbot*: Used semantic similarity and LLM-as-a-judge to improve knowledge base coverage by 40%, leading to 25% improvement in satisfaction.

*Content Platform*: Evaluated every article with hallucination detection and content quality metrics. Reduced manual review time by 60% while maintaining quality.

*Legal Document Analysis*: Achieved 99.5% accuracy with strict factual accuracy checks and faithfulness metrics. Reduced false positives by 45%.

## The Future

*SDKs*: Python SDK in progress, JavaScript/TypeScript planned.

*Expanded Metrics*: RAG-specific metrics, bias detection, security checks, cost optimization.

*CI/CD Integration*: Fail PRs if evaluation scores drop below thresholds.

*Advanced Features*: Agentic flow evaluation, regression detection, A/B testing, cost analytics.

## Getting Started

Exeta is open-source and available now:

1. *Deploy*: Full instructions in [deployment guide](DEPLOYMENT.md) 2. *API*: RESTful API with OpenAPI documentation 3. *Dashboard*: Modern Next.js dashboard for visual management 4. *SDK*: Python SDK available, more languages coming

## Architecture

- *Backend*: Rust + Axum + MongoDB + Redis - *Frontend*: Next.js 14 + TypeScript + Tailwind CSS - *Auth*: JWT + API keys + OAuth2 - *Performance*: Redis-backed rate limiting, connection pooling, intelligent caching

## Conclusion

LLM evaluation shouldn't be an afterthought. As AI becomes central to applications, we need the same rigor in testing that we have for traditional software.

Exeta provides that rigor—built for scale, designed for teams, engineered for performance.

*Try it today*: [Exeta](https://exeta.space)

Built with using Rust, Next.js, and a lot of coffee.