Security
Security by design, not afterthought
- TLS 1.2+ enforced on all service endpoints
- AES-256 encryption for sensitive data at rest
AI systems that are transparent, auditable, and yours to control.
Enterprise AI deployments fail when nobody can explain what the model did or why. We build AI systems with oversight, auditability, and human control as non-negotiables.
How we approach it
Prompt governance and versioning
Every prompt template we deploy is version-controlled. Changes go through a review process and are logged with a timestamp, author, and reason. You can roll back to any previous prompt configuration.
Output validation layers
AI model outputs are not passed directly to downstream systems. We build validation layers that check format, plausibility, and safety constraints before any output triggers an action or reaches a user.
Full audit trail on AI decisions
Every AI-assisted decision — routing, classification, generation — is logged with the input, the model version, the output, and the timestamp. This log is available for review, export, and compliance reporting.
Human oversight and intervention points
For consequential decisions, we build human-in-the-loop checkpoints. Operators can review, override, or escalate any AI recommendation. Override events are logged so you understand where and why the system is being corrected.
Model selection and provider transparency
We document which AI providers and models are used in each system. You are not locked into a single provider. When a model is updated or deprecated, we communicate that and revalidate outputs before deploying any change.
Boundary configuration for agents
AI agents we build are explicitly scoped. We define which systems they can access, what actions they can take, and what they are not permitted to do. These boundaries are enforced in code, not just documented.
Questions
Common questions about ai governance.
We build output validation into every AI workflow. Before any AI output reaches a user or triggers a downstream action, it passes through checks for format, plausibility, and safety constraints. For higher-risk workflows, we add a human review step. Every output is logged so you can identify and investigate failures.
We work with OpenAI, Anthropic, Google, and open-weight models depending on your requirements. We design integrations with a provider abstraction layer where practical, so you are not locked in. If a provider changes its pricing, terms, or model availability, we can migrate with minimal disruption.
Yes. We build structured logging for every AI invocation as a default. The log includes the input, the model version, the output, and the timestamp. You can export this data, feed it to your analytics stack, or use it for compliance reporting.
We track model lifecycle announcements from providers. When a model version changes, we rerun our validation suite before deploying the update to production. We notify you before any model change goes live.
Other trust pillars
Data Handling
Your data stays yours
- Data minimisation reviewed at design stage
- Retention periods defined per data category
Compliance Readiness
Built to pass procurement
- Access control policy documented and implemented
- Change management process with audit trail