AI Agent Evaluation Platform

Pinpoint where your

Your customer says "the AI isn't working." We show you exactly which layer is causing it — retrieval, intent routing, generation, or tools.

Model-agnostic
Framework-agnostic
Setup in 30 minutes
10x Cheaper
// AGENT QUALITY MONITOR — PROD
LIVE
INTENT ROUTING
45%
DOC HIT RATE
65%
RETRIEVAL PRECISION
70%
CHUNKING QUALITY
91%
FINAL ANSWER
76%

ROOT CAUSE IDENTIFIED

It's not hallucination. Your intent layer needs improvement. Retrieval & model layers are healthy.

Sound familiar?

"Response quality is bad."

Your team keeps hearing it. Customer success escalates. The CTO asks questions. But nobody can explain WHY the quality is bad — just that it is.

"The model is hallucinating."

The default blame for every AI failure. But when you actually dig in, the model is often fine — it's retrieval, intent routing, or tool usage that's broken. You just can't see it.

"We can't scale this to more customers."

You're fixing issues case by case. Every new customer, every model upgrade — you re-test everything manually and pray nothing breaks.

AI Agent Flow
Intent
Retrieval
✕
SupaEval finds this
Reasoning
Tool
Response

Your final answer scores 76%.
Your intent routing is at 45%.

Final-answer tools say "looks fine." SupaEval pinpoints the issue.

What other tools see

76%Final Answer
All looks good!

What SupaEval sees

INTENT ROUTING
45%
DOC HIT RATE
65%
RETRIEVAL PRECISION
70%
CHUNKING QUALITY
91%
FINAL ANSWER
76%
It's not hallucination. Your intent layer needs improvement.

From "quality is bad" to "intent routing is fixed." In one week.

Start evaluating your agents in minutes, not weeks.

1

Connect your agent

One SDK call. Works with any framework — LangChain, LlamaIndex, custom builds. No code changes to your agent.

supaeval.trace(your_agent)
2

Evaluate every layer

SupaEval automatically evaluates retrieval, intent routing, chunking, generation, and tool use — with thousands of synthetic test cases generated for your domain.

supaeval.evaluate(layers='all')
3

Pinpoint the root cause

One dashboard shows exactly which layer is failing and why. No trace-by-trace debugging. No war rooms. Just data.

supaeval.get_root_cause()
4

Fix with suggestions

SupaEval suggests fixes — prompt improvements, few-shot examples, instruction changes. Implement, re-run, improve.

supaeval.suggest_fixes()

Teams that stopped guessing.

Enterprise SaaS Company, 500+ employees

"Every team was blaming the AI/ML team — 'the model is hallucinating.' But the layer-wise data showed intent detection and document retrieval were the real issues. The blame game ended. Our CPO mandated an eval-first strategy across the company."

— Head of AI Engineering
Fortune 500 Tech Company

"We had war rooms with 300 prompts divided among engineers and stopwatches for latency. Half the prompts never got executed. Automated layer-wise evaluation changed the entire release process overnight."

— Principal Engineer

Everything you need to evaluate AI agents.

Purpose-built for quality. Not observability with eval bolted on.

Layer-By-Layer Evaluation

Evaluate every layer independently — retrieval precision, intent routing accuracy, chunking quality, generation faithfulness, tool use correctness. Know exactly WHY the final answer is wrong.

Synthetic Data Pipeline

Generate thousands of diverse test cases from 200 seed prompts. PDFs, HTML, tables, images — all document types. First run takes hours, not the months it takes to build manually.

Fix Suggestions

When a layer fails, SupaEval generates actionable fixes — prompt improvements, few-shot examples, system instruction changes. Don't just find the problem — start solving it.

Real-Time Production Monitoring

Evaluate quality metrics on live traces. Failed metrics automatically expand your test dataset. Catch regressions before customers do.

What you're doing now
vs. SupaEval

Test coverage

The Status Quo
Manual Testing

100–300 prompts

Building Internally

Custom, limited

General Tools (Ragas/Langsmith)

Bring your own

With SupaEval

Thousands, auto-generated

Root cause identification

The Status Quo
Manual Testing

No

Building Internally

Maybe, after months

General Tools (Ragas/Langsmith)

No (final answer only)

With SupaEval

Automated, per layer

Setup time

The Status Quo
Manual Testing

Ongoing manual

Building Internally

2–3 months

General Tools (Ragas/Langsmith)

Weeks

With SupaEval

1 week

Annual cost

The Status Quo
Manual Testing

High (Time)

Building Internally

$300–500K/year

General Tools (Ragas/Langsmith)

Tool + Time

With SupaEval

$50–100 per run

Fix suggestions

The Status Quo
Manual Testing

No

Building Internally

No

General Tools (Ragas/Langsmith)

No

With SupaEval

Yes, AI-powered

Framework lock-in

The Status Quo
General Tools (Ragas/Langsmith)

Some

With SupaEval

None — any framework

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Enterprise-Grade Trust

Built for security-conscious teams. We take data privacy and security seriously.

Secure Agent Invocation

We invoke your agents securely via encrypted channels. Your API keys and secrets are stored in a vault.

Tenant Isolation

Strict logical separation of data. Your datasets and evaluation results are never accessible by other tenants.

No Training on Data

We guarantee that your data is never used to train our models or any third-party models.

Audit-Friendly

Comprehensive logs of every evaluation run, including who ran it, what config was used, and the results.

No eval engineer required.

  • ✓Pinpoint where your agent is failing for $50–100 per run.
  • ✓No $300K evaluation team. No 3 months building internal tools.
  • ✓No code changes to your agent.
  • ✓Connect, evaluate, fix — your first root cause analysis in one week.

Stop guessing.
Start evaluating.

Join engineering teams that debug with data, not blame. Layer by layer.