Find the risks in your AI before production does.
An independent, hands-on assessment of your AI systems - from models and data pipelines to integrations - testing both security and safety so you can deploy with evidence, not assumptions.
Know exactly where your AI is exposed
AI systems fail in ways traditional software doesn't. Models can be manipulated, leak sensitive data, behave unpredictably on edge cases, or degrade silently over time - and most of these risks never surface in a standard QA cycle.
Our technical assessment puts your AI under real scrutiny. We examine the model, the data it learns from, the pipeline that serves it, and the integrations around it - combining security testing with safety and reliability analysis to give you a complete picture of your exposure.
You walk away with a clear, prioritized view of what's at risk, how severe it is, and exactly what to fix - so you can move to production with confidence instead of crossing your fingers.
A model that performs well in a demo can still fail dangerously in the real world.
Adversarial attacks, data leakage, biased outputs, and silent failures don't show up in accuracy metrics - but regulators, attackers, and your users will find them. Testing before deployment is far cheaper than discovering these issues in production.
A full technical risk review - security and safety.
We assess your AI across the dimensions that matter most, combining offensive security techniques with rigorous safety and reliability analysis.
Adversarial Robustness
How your model holds up against prompt injection, evasion, data poisoning, and other adversarial techniques designed to manipulate its behavior.
Data & Privacy Exposure
Whether your system can leak training data, expose sensitive information, or be probed to reconstruct confidential inputs.
Pipeline & Integration Security
The security of the infrastructure around your model - APIs, data flows, dependencies, and the integrations that connect it to the rest of your stack.
Reliability & Failure Modes
How your system behaves under edge cases, unexpected inputs, and load - and how gracefully it fails when it does.
Bias & Fairness
Whether your model produces systematically unfair or skewed outputs across groups, use cases, or conditions that could create harm or liability.
Transparency & Explainability
Whether the system's decisions can be understood, traced, and justified - a growing expectation from regulators, auditors, and users alike.
A structured assessment, in four steps.
A clear, time-boxed engagement that fits around your team and delivers findings you can act on immediately.
Scope
We define the systems in scope, agree on objectives and rules of engagement, and map the model, data, and integrations to be tested.
Test
We run hands-on security and safety testing - adversarial attacks, data and pipeline review, and reliability and bias evaluation.
Analyze
We triage every finding by severity and business impact, separating critical exposure from lower-priority hardening opportunities.
Report
We deliver a clear report with prioritized findings and a practical remediation roadmap - and walk your team through it.
What you walk away with.
Don't wait for production to find the risks.
In one conversation, we'll scope where your AI is most exposed and what a technical assessment would cover for your systems.