AI treated as
production infrastructure.
Our systems are built on a deny-by-default posture. We prioritize controls, auditability, and isolated execution over unconstrained capability expansion.
Data Handling
We do not use customer data to train generalized models. Engagement data is handled according to the deployment model and data-processing terms agreed with the client. We support retention controls, PII redaction heuristics, and tokenization patterns before model invocation.
Identity & Access
We can integrate with existing identity providers such as SSO, SAML, or OIDC. Retrieval and execution workflows are designed around role-based access controls so users only reach the systems and source material they are permitted to use.
Auditability
Prompts, retrieved context, generated outputs, and tool invocations can be recorded to reviewable trace streams. Teams can inspect what evidence was used, what actions were attempted, and where human approval entered the workflow.
Execution Controls
Our agentic systems run behind policy gates. Budgets and rate limits reduce runaway execution risk, while human-in-the-loop approval workflows restrict high-risk operations.
Sandboxing & Egress
Runtime environments can be sandboxed with restricted egress and allowlisted endpoints. The goal is to reduce blast radius and make external interactions explicit.
Deployment Models
We deploy to the model that fits the work: managed cloud, client VPC, on-prem, or offline/local-first environments where the use case and model constraints support it.
Security & Governance Baseline
Standard controls baked into every deployment by default.
Security review should shape the build.
We design for InfoSec review early, so architecture, controls, and operating assumptions are documented before production rollout.
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