Arkheia

For ML Engineering & MLOps

Stop autonomous
agent cascades.

When a single hallucination poisons a 10-step agentic workflow, debugging is a nightmare. Arkheia acts as a boundary guardrail, detecting structural anomalies at the exact moment of inference.

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MTTR

Seconds

Identify exactly which inference step hallucinated.

Overhead

<10ms

Zero-latency impact on your critical path.

Uptime

99.9%

Fail-open proxy ensures your pipeline never breaks.

Empirical Validation Hub

Diagnostic Benchmarks

Our characterisation pipelines establish baseline separation before profile deployment. For example, on standard structural analysis (Truth vs Fabrication):

  • • Max Cohen's d: 2.63 (Strong Separation)
  • • Proxy Overhead: ~6-8ms
  • • AUC-ROC: >0.90 on calibrated models

*Metrics derived from Arkheia Model Lab 100-prompt baseline validations.

Public Quickstart

Evaluate the telemetry surface immediately using your existing API keys.

curl -X POST https://proxy.arkheia.ai/v1/chat/completions \
  -H "X-Arkheia-Key: demo_key_123" \
  -H "X-OpenAI-Key: $OPENAI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-4o","messages":[{"role":"user","content":"Explain quantum gravity"}]}'

View full API Specs & SDKs →

The Impact on Agentic Workflows

Without Boundary Detection

Step 1: Extract data [OK]
Step 2: Summarize [SILENT HALLUCINATION]
Step 3: Format output [OK]
Step 4: Execute API [OK]
Result: Pipeline executes corrupted data. Debugging takes hours.

With Arkheia Proxy

Step 1: Extract data [x-arkheia-risk: LOW]
Step 2: Summarize [x-arkheia-risk: HIGH]
→ System pauses. Routes to human review queue.
Result: Cascade prevented. MTTR reduced to seconds.

Prove it on your own workloads.

The best way to evaluate structural detection is to see it catch anomalies on your actual data. Start with our free API Proxy tier—no credit card, no infrastructure to deploy.

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