AI Development
AI Guardrails & Safety Services
We implement safety layers for AI systems: prompt injection defenses, tool allowlists, PII controls, policy checks, and safe fallbacks—so assistants and agents behave predictably.
Overview
What this service is
We start with an AI-specific threat model for your product: what users can input, what tools the system can call, and what data it can access.
Guardrails are applied across the stack—retrieval filters, constrained schemas, moderation policies, and approval steps for sensitive actions.
We add monitoring and test cases so safety improves over time and risky behaviour is visible before it becomes an incident.
Benefits
What you get
Reduce unsafe actions and outputs
Guardrails constrain what the system can do and how it responds under uncertainty.
Lower data leakage risk
Permission-aware retrieval and PII controls reduce accidental exposure of sensitive content.
Better user trust and adoption
Clear fallbacks, citations, and escalation paths make the experience feel reliable.
Safer tool access for agents
Allowlists and schemas keep actions bounded and auditable as workflows expand.
Operational visibility
Safety events are logged and measured so teams can keep improving with confidence.
Features
What we deliver
Threat modeling
Identify injection, data leakage, and misuse risks across prompts, retrieval, tools, and UX.
Tool allowlists + schemas
Constrain actions with typed inputs, validation, and approvals for sensitive operations.
PII and sensitive data controls
Redaction, data minimization, and policy enforcement aligned to your privacy requirements.
Prompt injection defense
Input filtering, system prompt hardening, and retrieval safeguards to reduce jailbreak attempts.
Safety fallbacks
Escalation workflows, “I don’t know” handling, and user guidance when confidence is low.
Safety monitoring + tests
Red-team scenarios and ongoing monitoring to detect and prevent recurring issues.
Process
How we work
Threat model + scope
We map risks, tool surface area, and data access boundaries for your AI features.
Guardrails implementation
We implement allowlists, validation, policies, and fallbacks across the workflow.
Safety testing
We add adversarial cases and regression checks to catch unsafe behaviour early.
Monitoring + rollout
We deploy with logging, dashboards, and staged rollout controls to reduce risk.
Tech Stack
Technologies we use
Core
Tools
Use Cases
Who this is for
Public-facing chatbots
Reduce off-policy answers, prompt injection attempts, and unsafe outputs with clear fallbacks.
Tool-calling agents
Protect actions behind allowlists, schemas, and approvals so automation stays bounded.
Document-grounded assistants
Apply permission-aware retrieval and sensitive data policies for internal knowledge access.
Voice agents
Add explicit escalation rules and constrained extraction for critical fields collected by phone.
Enterprise copilots
Align behaviour with governance requirements and audit trails across departments.
FAQ
Frequently asked questions
Good guardrails improve usefulness by preventing confusion and unsafe behaviour. We tune guardrails to protect critical risks while preserving helpful responses.
No defense is perfect, but layered controls (retrieval safeguards, schemas, approvals, monitoring) significantly reduce risk and improve resilience.
Yes. We implement data minimization, redaction, and logging controls aligned to your policies and risk profile.
We design explicit fallbacks: clarification questions, refusal policies, and escalation to a human with summaries.
Yes. We can layer guardrails onto existing assistants and agents and then progressively improve safety coverage with tests and monitoring.
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Make your AI features safer before scale
Share your AI flows and risk profile—we’ll propose guardrails, tests, and rollout controls to reduce unsafe outputs and actions.
Security-first implementation.