Stop AI failures before they reach production

Multi-judge evaluation and guardrails for every AI tool you ship. Plug in, get a verdict, deploy with confidence.

Platform Eng
~/observal-cli
❯
mcp register db-core

Observal Hub

CENTRAL REGISTRY

12MCPs
stripe-mcpgit-mcpdb-core ✓
Live Telemetry
Rec
14:02Health check: db-core
14:03CodeBot → git-mcp
14:03SalesBot → stripe-mcp
⚠ Rate Limit
AI Agent Fleet
CodeBot Agentv2.4
Using: git-mcp
Status: Processing...
SalesBot Agentv1.1
Using: stripe-mcp
Status: Rate limited
How It Works

Three pillars. One airtight evaluation pipeline.

Built for the environments where standard evaluators fail — massive codebases, tangled dependencies, zero room for error.

01

Universal Context Mastery

Full contextual awareness across multi-million-line repositories — where standard context windows fragment and hallucinate.

02

Council of Agents Evaluation

Multiple specialized agents deliberate and reach consensus — eliminating the single-judge failure mode.

03

Instant Guardrails via SLM

A Small Language Model orchestrates the Council and enforces guardrails in real time. Deploy in minutes, not months.

Scoring Engine

Two signals. Zero blind spots.

Pattern-match against your team's actual review history. Then verify if the AI did what it claimed.

Evolutionary Grading

EGG

Learns from your team's merged PRs and reverts. Scores AI output against your actual review patterns — not a generic rubric.

Git History
✓a3f2c1d
✗e7b91fa
✓1c4d8e2
Team Pattern Model
naming conventions
error handling
test coverage
EGG Score
0/100
MATCHES TEAM STYLE
Code that matches team style scores high. Foreign patterns get flagged.

Intent-Outcome Divergence

IOD

Diffs the AI's stated reasoning against the actual code change. If the intent doesn't match the outcome, IOD catches it.

AI Reasoning
1"update button color"
2"no side effects"
3"CSS-only change"
Actual Diff
+ color: #3b82f6
+ onClick: checkout()
+ import stripe
IOD: DIVERGENCE DETECTED⚠
"Update button color" but also added checkout logic? Divergence score spikes.
Platform Lifecycle

From submission to production. One loop.

Developers submit. Observal evaluates, documents, and publishes. Agents deploy and get scored in production — continuously.

MCP REGISTRYdiscover · install · monitorAGENT REGISTRYdeploy · evaluate · scoreOBSERVALorchestratorSubmitEvaluatePublishInstallDeployScoreMonitorIterate
MCP Registry Loop
Agent Registry Loop
MCP Registry
01Submit via /submit <GIT_URL>
02Auto-evaluate: security, schema validation, docs check
03Published with docs, config file download, and setup steps
04Track downloads & tool calls per MCP in production
Agent Registry
01Submit via /submit <GIT_URL>
02Auto-evaluate agent definition: Prompt + MCPs + Model
03Document purpose, capabilities, and dependencies
04SLM Judge scores production runs: acceptance, tool calls, CoT quality
Agent = Prompt + MCPs + Model
Promptsystem instructions
+
MCPstool servers
+
ModelLLM config
Every agent is a declarative composition. Submit the repo, Observal handles the rest — evaluation, documentation, and production monitoring via SLM-as-a-judge.
The Case for Change

Standard LLM evaluators are a liability. Here's the data.

⚠ The Problem

0% of teams hit eval failures

Industry data shows the vast majority of engineering teams experience significant consistency problems when relying on standard LLM-powered evaluations. Outputs drift, hallucinations slip through, and confidence erodes.

Elite teams spend 40%+ of dev cycles hand-tuning evaluation pipelines just to achieve baseline reliability.
✓ Our Solution

Multi-judge consensus, productized.

The "Council of Agents" approach takes the multi-judge consensus methodology pioneered by elite AI teams and packages it as a drop-in API. No bespoke infrastructure. No months of fine-tuning.

Get elite-tier evaluation coverage out of the box — reclaim that 40% of engineering time.
Developer Experience

Three lines to a Council verdict.

Route any internal AI tool's output through the Council API. Get a multi-agent consensus score, flagged issues, and full reasoning — instantly.

evaluate.py
# Route any AI tool's output through the Council
from council_eval import Council

verdict = Council(api_key="sk-...").evaluate(
    prompt=user_query,
    response=agent_output,
    context=codebase_snapshot
)

# verdict.score · verdict.flags · verdict.reasoning
PythonJavaScriptGoRubyJavaC#PHP
Enterprise Privacy & Security

Your code stays yours. Period.

We evaluate your AI tools — we never train on your data, expose your proprietary logic, or retain your codebase beyond the evaluation window.

🔒

Zero Data Retention

Evaluation inputs are processed in ephemeral, isolated environments and purged immediately after scoring.

🛡️

No Model Training

Your proprietary code, prompts, and outputs are never used to train or improve any public or third-party model.

🏢

SOC 2 Type II

Enterprise-grade compliance with full audit trails, role-based access control, and encrypted data in transit and at rest.

☁️

VPC & On-Prem Options

Deploy within your own cloud boundary or on-premises for teams with the strictest data residency requirements.

Get Started Free

Evaluate your first AI tool in under five minutes.

No credit card. No fine-tuning. Just plug in your tool's output and let the Council deliver a verdict.