Input and output filtering
Injection defense, jailbreak detection, PII redaction and content policy enforcement on every interaction.
AI Infrastructure
AI Guardrails & Safety
Keep AI helpful and compliant with layered guardrails, content policies, and human approval flows.
- 100%of interactions policy-checked in-line
- <50msguardrail latency overhead
- 0PII leaks with layered redaction
Capabilities
Safety as infrastructure, not prompts
Policy enforcement, PII protection, approvals and safe AI interactions for enterprises.
Action guardrails
Runtime policy checks on tool calls — scope, spend, data access — enforced outside the model.
Red-teaming and hardening
Systematic adversarial testing before launch and after every significant change.
Audit and accountability
Immutable logs of every decision, block and override — ready for security review and regulators.
How it works
Defense in depth for AI systems
A system prompt is not a security boundary. We enforce policy in the runtime — before the model sees input and before its output reaches users or systems.
Policy enforcement
Rules the model can't talk its way around
Guardrails run outside the model: input screening for injection and abuse, output checks for leakage and policy violations, action gates on every tool call. The model proposes; the policy engine disposes.
- Prompt-injection and jailbreak detection on inputs
- PII and secrets redaction on inputs and outputs
- Allow-listed tools with argument-level validation
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Adversarial testing
We attack it before anyone else can
Structured red-teaming against your actual system — injection chains, data exfiltration attempts, scope escalation — with findings turned into regression tests that run forever after.
- Attack libraries tailored to your use case and data
- Findings become permanent regression tests
- Re-testing after every model or prompt change
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Accountability
Every decision explainable, every action traceable
Complete audit trails of what was asked, what was retrieved, what was blocked and why — mapped to your compliance frameworks and ready when auditors ask.
- Immutable logs of prompts, outputs and policy decisions
- Human override paths with recorded justification
- Reporting mapped to SOC 2, GDPR and internal risk controls
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
Where guardrails & safety delivers value
Customer-facing AI
Chatbots and agents that can't be jailbroken into refunds, leaks or brand damage.
Agent action control
Autonomous agents with hard limits on scope, spend and data access.
Regulated industries
Financial, healthcare and public-sector AI with evidence trails regulators accept.
PII-heavy workloads
Support, HR and health data flows with layered detection and redaction.
Third-party AI adoption
Guardrail gateways that make vendor AI tools safe for enterprise data.
Pre-launch hardening
Red-team engagements that find the failure modes before launch day.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Our red team broke the first version in an afternoon — that was the point. The rebuilt guardrail layer has blocked every attempt since, and the audit log made our compliance review painless.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Anthropic Claude
- OpenAI Moderation
- Guardrails AI
- NeMo Guardrails
- Presidio
- AWS Bedrock Guardrails
- Azure AI Content Safety
- OPA
- Vault
- Datadog
- Splunk
- OpenTelemetry
FAQ
Common questions
Isn't a good system prompt enough?
No. Prompts are suggestions to a probabilistic system; determined users defeat them. Real safety needs enforcement outside the model — input screening, output filtering and action gates the model cannot override.
How much latency do guardrails add?
Well-engineered checks run in parallel where possible and typically add under 50ms. We measure the overhead explicitly and tune the pipeline to your latency budget.
Can you harden a system we've already built?
Yes — that's a common engagement. We assess the current system, red-team it, then retrofit layered guardrails and audit logging without rebuilding the core.
How do you keep up with new attack techniques?
Attack libraries are updated continuously, and every new technique becomes a regression test. Production monitoring also flags anomalous interaction patterns for review.
What about compliance evidence?
Every policy decision is logged immutably and mapped to your control framework — so SOC 2, GDPR or internal audits get evidence, not assurances.
Next step
Make your AI safe to scale
Get a guardrail assessment and red-team of your current system — before someone else runs one for you.