Your Production AI Is Failing in Ways You Can't See
Axiontest is an AI Reliability Engineering company. We run proprietary tooling — 374 attack vectors across your web layer and AI layer — to evaluate, red-team, and harden LLMs, RAG systems, and AI agents before and after they ship.
SaaS Chatbot API · GPT-4o · 374 vectors run
Full HTML report · Payload + response evidence · Remediation guide
Delivered in 48hrThe Problem
AI Systems Fail in Production. Most Teams Never See It Coming.
Every production AI system has failure modes baked in. Without continuous evaluation and adversarial testing, you only discover them after users do.
Hallucinations
Confident, wrong answers delivered as fact to end users.
Prompt Injection
Adversarial inputs hijacking model behavior and overriding system instructions.
Model Drift
Silent accuracy degradation over time as model versions or data distributions shift.
Unsafe Outputs
Harmful, biased, or non-compliant responses reaching production users.
Retrieval Failures
RAG pipelines returning irrelevant, stale, or hallucinated context from vector stores.
Agent Breakdowns
Multi-step agentic workflows failing mid-orchestration with no recoverable state.
Tool Misuse
Agents calling the wrong tools, with wrong parameters, or at the wrong time.
Context Degradation
Long-context coherence loss and instruction forgetting in extended conversations.
Core Solutions
The Full Stack of AI Reliability
Six interconnected disciplines that together close every reliability gap in your production AI system.
Catch regressions before your users do.
We run our proprietary LLM evaluator across 6 quality dimensions — Factual Accuracy, Instruction Following, Refusal Precision, Consistency, Hallucination Detection, and Scope Adherence — with 57+ pre-built test cases tuned to your domain.
- 6-dimension quality scoring
- 57+ domain-specific test cases
- Deterministic PASS/PARTIAL/FAIL
- Markdown client reports
// eval run: checkout-agent-v2.4.1
PASS hallucination_rate → 2.1% (threshold: 5%)
PASS faithfulness → 0.94
WARN answer_relevance → 0.81 (↓ from 0.89)
PASS toxicity → 0.00
3 passed · 1 warning · 0 failed
→ Report sent to Slack #ai-quality
Systematically attack your AI before adversaries do.
We run 374 attack vectors across two layers: 90 AI-specific prompt injection vectors (9 domain packs) against your model, and 284 web-layer payloads against your endpoints. Most red teams only see the AI layer. We test both.
- 90 prompt injection vectors
- 284 web-layer attack payloads
- OWASP LLM Top 10 coverage
- VULNERABLE/SUSPICIOUS/CLEAN verdicts
Red Team Run — GPT-4o RAG Agent
4 criticalFull visibility into every LLM call, trace, and output.
Real-time tracing of LLM calls, latency, token usage, output quality, retrieval relevance, and tool invocations across your entire AI stack. Know exactly what your AI is doing, why, and where it failed.
- Distributed LLM tracing
- Latency & token dashboards
- Output quality scoring
- Retrieval relevance metrics
Trace: customer-support-agent · run_a91f
End-to-end testing for multi-agent orchestration.
Validate the full agentic loop — tool use accuracy, handoff integrity between agents, memory consistency, goal completion rates, and graceful failure recovery. Built for LangGraph, CrewAI, AutoGen, and custom orchestration layers.
- Multi-agent workflow testing
- Tool use accuracy scoring
- Handoff integrity checks
- Failure recovery validation
Agent Orchestration Map
Compliance, audit trails, and policy enforcement for AI.
Full audit trails for every AI decision, automated bias detection, regulatory alignment (GDPR, HIPAA, SOC 2, EU AI Act), and policy enforcement gates. For AI teams with real compliance obligations.
- Full decision audit trails
- Bias & fairness detection
- HIPAA / GDPR alignment
- Policy enforcement gates
Governance Scorecard
SOC 2 AlignedContinuous quality scoring on your live AI traffic.
We instrument your production AI stack to score every output, detect anomalies, and alert on drift in real-time. Weekly reliability scorecards give your team and stakeholders a clear picture of AI quality over time.
- Live output quality scoring
- Drift anomaly detection
- Weekly reliability reports
- Stakeholder scorecards
Weekly Reliability Report
May 12–18, 202594.2%
Avg Quality
2
Drift Events
99.97%
Uptime
Proprietary Tooling
Built for This. Not Retrofitted.
We built our own testing tools because existing frameworks only evaluate the AI layer. Every engagement is backed by three purpose-built instruments that together cover the full attack surface of an AI application.
374
Total Attack Vectors
9
Domain-Specific Attack Packs
6
LLM Quality Dimensions
2
Attack Layers Covered
6
Quality Dimensions
LLM Quality Evaluator
Pattern-based scoring across Factual Accuracy, Instruction Following, Refusal Precision, Consistency, Hallucination Detection, and Scope Adherence. Deterministic PASS/PARTIAL/FAIL — not AI-judges-AI.
- 57+ pre-built test cases
- Healthcare · Fintech · Legal · Coding · Education · Support
- Compare-runs (before/after prompt changes)
- Markdown reports ready for client delivery
# Healthcare AI · Eval Run 23
90+
Attack Vectors
Prompt Injection Tester
Systematic prompt injection across 9 domain-specific packs. Auto-detects system prompt leakage by extracting key phrases from the target system prompt and checking every response.
- Financial AI · Healthcare AI · Support Chatbot
- RAG/Document AI · Multi-Agent Systems · E-commerce
- System prompt leakage detection
- Works with OpenAI, Anthropic, and any OpenAI-compatible API
# Financial AI Pack · 12 of 90 vectors
284
Attack Payloads
Web Security Scanner
Full web-layer security scanner covering the HTTP surface that underlies every AI application. Tests the endpoints that AI reliability tools completely ignore.
- SQL Injection (60) · XSS (34) · Command/SSTI (40)
- Path Traversal + SSRF (32, incl. AWS metadata)
- NoSQL + GraphQL (18) · Deserialization (13) · JWT attacks
- cURL paste-and-parse workflow · HTML findings report
# /api/chat endpoint · 3 of 9 packs
Tools run internally as part of every engagement — not sold as standalone licenses.
Industry Solutions
Built for Teams Where AI Reliability Is Non-Negotiable
Sector-specific evaluation frameworks tuned to the compliance, accuracy, and safety requirements of your industry.
Healthcare AI
HIPAA compliance, clinical accuracy, hallucination risk in diagnostic or advisory AI.
- Clinical accuracy evals
- PHI leakage detection
- HIPAA audit trails
- Hallucination risk scoring
Fintech AI
Regulatory compliance, decision auditability, and model risk in financial AI systems.
- Decision audit trails
- Bias & fairness testing
- Regulatory alignment
- Model risk reporting
Legal AI
Contract accuracy, citation validation, and confidentiality in legal document AI.
- Citation accuracy evals
- Confidentiality testing
- Hallucination scoring
- Privilege boundary checks
SaaS AI
Feature reliability, multi-tenant isolation, and output consistency at scale.
- Regression eval pipelines
- Tenant isolation testing
- Output consistency scoring
- Release gate automation
Customer Support AI
Tone accuracy, escalation path integrity, and brand-safe outputs in support AI.
- Tone & sentiment evals
- Escalation path testing
- Brand safety scoring
- Resolution accuracy
How It Works
A Continuous Reliability Loop, Not a One-Time Audit
AI reliability isn't a single event. It's an ongoing engineering discipline that runs in parallel with your product development cycle.
Evaluate
Define your eval suite. Run automated assessments against ground truth across hallucination, faithfulness, relevance, toxicity, and task-completion metrics.
- Metric definition
- Ground truth benchmarking
- Regression diffs
- CI/CD gates
Observe
Instrument your production AI stack. Trace every LLM call, score live outputs in real-time, and surface quality anomalies before they compound.
- Distributed tracing
- Live quality scoring
- Anomaly detection
- Latency monitoring
Secure
Red-team adversarial scenarios, enforce output policies, audit every AI decision, and validate compliance with industry-specific regulatory requirements.
- Adversarial red-teaming
- Policy enforcement
- Decision auditing
- Compliance alignment
Improve
Prioritise regressions by impact, guide fine-tuning and prompt iteration, validate improvements, and re-deploy with confidence.
- Regression prioritisation
- Fine-tune guidance
- Improvement validation
- Safe re-deployment
Enterprise Trust
Reliability You Can Measure and Report On
Axiontest doesn't just find problems. We give you the dashboards, reports, and scorecards to demonstrate AI reliability to your team, board, and regulators.
500+
Eval scenarios per project
48hr
Time to first assessment
SOC 2
Aligned reporting
10+
Model families supported
Reliability Scorecards
Executive-ready summaries of your AI quality posture — hallucination rates, drift trends, red-team findings, and compliance status in a single document.
Weekly Drift Reports
Automated weekly reports comparing current production quality against baselines, highlighting regressions and emerging failure patterns before they escalate.
Live Client Dashboard
Real-time visibility into your eval results, defect board, security findings, and reliability metrics — available to your entire team, 24/7.
Tooling Ecosystem
Works With Your Existing AI Stack
We instrument, extend, and integrate with the tools your team already uses. No rip-and-replace. No new platform to learn.
And any OpenAI-compatible or Hugging Face model endpoint. If your team uses it, we can instrument it.
Free Assessment
Is Your Production AI System Reliable?
Book a free AI Reliability Assessment. We'll review your stack, identify your highest-risk failure modes, and show you exactly what to fix first.