AI You Can Defend

Every AI system needs a safety layer — or you're one bad prompt away from an incident. We build guardrails, evaluation pipelines, and governance frameworks so your AI is safe, compliant, and defensible.

Prompt injection and misuse

Users will try to jailbreak your AI, extract system prompts, or get it to do things it shouldn't. Without guardrails, they'll succeed.

Data leakage and privacy

LLMs echo training data and conversation context. Sensitive information can appear in outputs, and user data can flow to model providers.

Hallucinations in high-stakes contexts

Confident, wrong answers are worse than silence — especially in healthcare, finance, and customer-facing claims.

Regulatory uncertainty

EU AI Act, sector-specific rules, and enterprise risk frameworks demand evidence of responsible AI — not promises.

The usual approach to AI safety is a prompt that says 'be safe and accurate' — or an enterprise policy document nobody reads. Neither stops a model from doing the wrong thing under adversarial input.

Platform-level safety filters catch the obvious cases and miss the subtle ones: indirect injection hidden in retrieved documents, subtle bias in outputs, or confident hallucination on edge cases.

Safety is a continuous engineering discipline — detection, evaluation, red-teaming, and iteration — not a one-time review or a terms-of-service clause.

We design a layered safety architecture for your AI system: input guardrails that detect injection and policy violations, output guardrails that validate and filter responses, and content filters for toxicity and PII.

We build evaluation pipelines that test your AI against a curated suite of attack and safety scenarios — and keep them running in CI so every model update is vetted before deployment.

And we document it all: red-teaming reports, evaluation results, and governance documentation that stands up to enterprise review and regulatory scrutiny.

Prompt Injection Defense

Detection and mitigation for direct and indirect injection, including via retrieved documents and tools.

Output Guardrails & Validation

Policy enforcement, PII redaction, toxicity filtering, and structured-output validation on every response.

Evaluation & Red-Teaming

Automated attack suites, adversarial testing, and manual red-teaming by security-minded engineers.

Hallucination Reduction

Grounding, retrieval-based fact-checking, and confidence calibration for high-stakes contexts.

Monitoring & Incident Response

Production logging, safety-event detection, and runbooks for when something slips through.

Governance & Compliance

Risk assessments, documentation, and policy alignment for the EU AI Act and enterprise frameworks.

Safety Assessment

We map your AI system's risks — misuse, leakage, hallucination, bias — and prioritize by real impact.

Guardrail Architecture

Layered input and output defenses designed around your specific model and use case.

Evaluation Pipeline

Automated safety test suites integrated into CI so every model version is vetted.

Red-Teaming

Adversarial testing by engineers who actually try to break your system.

Governance & Handoff

Documentation, monitoring, and runbooks your security and compliance teams can rely on.

Engineers who attack AI

We red-team your system before adversaries do — with the same mindset and tooling.

Safety that ships with the product

Guardrails and evals are built into your pipeline, not bolted on as an afterthought.

Evidence, not promises

Evaluation reports and red-team findings give your stakeholders something they can actually review.

Regulation-ready

Our governance framework maps to the EU AI Act and enterprise risk standards.