Evaluate Your LLMs with Confidence
Test, measure, and improve your LLMs — before your users find the bugs.
Eval Score
94.2%
Hallucination Rate
2.1%
Tests Passed
847/892
Score Trend (7d)
+5.2%Model Comparison
The Problem with Evaluating LLMs
Large language models fail in ways that are hard to predict and even harder to measure. Traditional software testing doesn't work — outputs are non-deterministic, failure modes are subtle, and quality degrades silently over time.
No Standard Metrics
Every team invents their own evaluation criteria. There's no unified way to measure accuracy, relevance, or safety across prompts and models.
Silent Failures in Production
LLMs don't throw errors — they confidently produce wrong answers. Without continuous evaluation, regressions go unnoticed until users complain.
Manual Testing Doesn't Scale
Reviewing outputs by hand works for 10 prompts, not 10,000. Teams need automated, repeatable evaluation pipelines to move fast.
Everything You Need to Evaluate LLMs
A complete evaluation platform — from prompt testing to production monitoring.
Prompt Regression Testing
Detect when model updates or prompt changes break existing behavior. Run your test suites on every change and get instant pass/fail reports.
Hallucination Scoring
Measure factual accuracy with source-grounded evaluation. Score every response for faithfulness, relevance, and fabrication risk.
Dataset Evaluation
Benchmark models against curated or custom datasets. Upload your golden datasets and evaluate across hundreds of test cases in minutes.
Automated Adversarial Testing
Stress-test your models with adversarial prompts, jailbreak attempts, and edge cases. Find vulnerabilities before bad actors do.
Model Comparison Dashboards
Compare GPT-4, Claude, Llama, Mistral, and your fine-tuned models side by side. See which model wins on cost, quality, latency, and safety.
Custom Evaluation Metrics
Define your own scoring rubrics — tone, format compliance, domain accuracy, brand voice. Build evaluation criteria that match your product.
CI/CD Integration
Plug evaluations into your deployment pipeline. Run eval suites on every PR, block deploys that fail quality thresholds, and track scores over time.
Evaluation Reports & Analytics
Get detailed reports with pass rates, score distributions, failure analysis, and trend charts. Share results with your team or export to your tools.
How It Works
Go from zero to production-grade evaluation in minutes.
Connect Your Model
Point YetixAI at any LLM — OpenAI, Anthropic, open-source, or your own fine-tuned model. Just provide an API endpoint.
Configure Eval Suites
Choose from built-in evaluation templates or define custom test suites with your own datasets, metrics, and scoring rubrics.
Run Evaluations
Execute evaluation runs on demand or on a schedule. Test across thousands of prompts in parallel with detailed per-case results.
Analyze & Improve
Review dashboards, drill into failures, compare model versions, and track quality trends over time. Ship better models, faster.
Developer-First Integration
Get started with a few lines of code. Our SDK plugs into your existing workflow — no complex setup required.
- Install the SDK via pip or npm
- Point it at any model endpoint
- Run evaluations from your terminal or CI pipeline
- View results in the dashboard or as JSON
from yetixai import YetixClient
# Connect to your model
client = YetixClient(api_key="your-key")
# Run an evaluation suite
results = client.evaluate(
model="gpt-4o",
suite="hallucination-v2",
dataset="./test_cases.json"
)
# Check results
print(f"Score: {results.score}%")
print(f"Passed: {results.passed}/{results.total}")
# Fail CI if score drops
assert results.score > 90, "Eval score regression!"Built for Every AI Team
Whether you're shipping a chatbot or fine-tuning a foundation model, YetixAI fits your workflow.
LLM Product Testing
QA your AI features before every release. Ensure your chatbot, copilot, or AI assistant meets quality bars across every user scenario.
AI Safety & Compliance
Evaluate models for harmful outputs, bias, toxicity, and policy violations. Generate compliance reports for internal review or regulatory needs.
Model Benchmarking
Compare foundation models head-to-head on your own data. Make informed model selection decisions based on quality, cost, and latency.
CI/CD for Prompts
Treat prompts like code. Version them, test them on every change, and block deployments that introduce regressions.
Fine-Tune Validation
Evaluate fine-tuned models against base models to verify that training improved performance without introducing new failure modes.
Red Teaming & Security
Run automated red-team exercises to probe for prompt injection, data leakage, and jailbreak vulnerabilities across your LLM stack.
Built for AI Engineers
By engineers who've shipped LLM systems at scale.
Scalable Evaluations
Run thousands of test cases in parallel. No limits.
Automated Pipelines
Schedule evaluations, integrate CI/CD, get alerts.
Enterprise Ready
SOC 2, SSO, role-based access, and audit logs.
Ready to Evaluate Your LLMs?
Stop guessing about model quality. Start testing with automated, rigorous evaluation pipelines — and ship AI products you can trust.