AI Development
AI Guardrails & Safety
We implement guardrails so LLM features behave predictably: prompt-injection defenses, tool allowlists, PII controls, refusal patterns, and safe fallbacks. You get tests and monitoring so safety stays intact as the system evolves.
Overview
What this service is
Guardrails are the safety layer around AI features: policies, permissions, filtering, and fallbacks that prevent unsafe or untrusted behavior.
We design guardrails aligned to your risks: data leakage, unsafe actions, policy violations, and adversarial inputs.
Delivery includes safety tests and monitoring so guardrails don’t degrade silently after changes.
Start Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Reduce prompt injection and data leakage risk
Keep tool actions safe with allowlists + approvals
Improve trust with clear refusal and fallback UX
Control sensitive data handling with PII rules
Detect safety failures with tests and monitoring
Ship updates with less risk of regressions
Features
What we deliver
Tool allowlists + RBAC
Only approved tools and actions are accessible, with role-aware permissions and approval steps for sensitive actions.
Prompt injection defenses
Input sanitization, policy enforcement, context separation, and retrieval controls to reduce injection risk.
PII and sensitive-data controls
Redaction, retention controls, and configurable logging so sensitive data is handled safely.
Refusal + fallback UX
Clear “can’t do that” behavior, safe alternatives, and escalation paths that don’t frustrate users.
Content and policy filters
Moderation, policy checks, and output constraints aligned to your product and compliance needs.
Safety testing + monitoring
Red-team tests, regression checks, and alerts for policy violations or unsafe behaviors in production.
Process
How we work
Risk mapping
Threat model and safety goals.
Design
Guardrail policies, tools, approvals, and UX.
Build
Implement controls and validations.
Red-team
Adversarial tests and fixes.
Monitor
Dashboards and alerts for safety drift.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Agent safety for tool actions
Prevent unsafe actions with allowlists, approvals, and strict parameter validation.
RAG privacy and access control
Ensure retrieval respects permissions and doesn’t leak private sources across users or tenants.
Support bot policy compliance
Ensure the assistant refuses unsafe requests and follows brand and policy rules consistently.
Enterprise audit readiness
Add audit logs, retention controls, and admin oversight for enterprise deployments.
Safe rollout of new prompts/models
Add regression tests and monitoring so changes don’t degrade safety.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Secure Mobile Solution in Australian Defence Ecosystem
Problem: Secure data workflows were required in a regulated environment with strict access controls.
Solution: Hardened architecture with strict auth, encrypted storage, and audit-friendly engineering patterns.
Outcome: Deployed securely within a regulated ecosystem with clear handoff and operational guidance.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and results were inconsistent.
Solution: RAG with hybrid retrieval and reranking, plus grounded answers and safer fallback behavior.
Outcome: Reliable answers with <10s response times and measurable improvements on real queries.
Ops Automation with AI + n8n
Problem: Manual approvals and CRM syncing created delays and data inconsistencies across tools.
Solution: Event-driven automation with validation gates and AI-assisted classification where it improved routing.
Outcome: Reduced manual workload significantly with more reliable workflows and operator visibility.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
No, but they drastically reduce common failure modes. We combine allowlists, validation, monitoring, and eval tests to keep behavior predictable.
They shouldn’t. We design refusal/fallback UX so users still get value and clear next steps instead of silent failures.
Yes. We can retrofit safety controls, add tests, and instrument monitoring without rebuilding everything.
We implement redaction where needed, configure retention, and restrict logging so PII isn’t stored or exposed unnecessarily.
Yes. We build adversarial test sets and verify that policies, context separation, and tool controls hold under attack-like inputs.
Yes. Human-in-the-loop approvals are a common pattern for production agent safety.
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