A controlled model gateway
Define which models and providers may be used for which tasks. Apply authentication, usage limits and routing policies, with a way to evaluate a provider change before it affects production.
Enterprise AI architecture
Move from isolated pilots to a shared foundation.
Create the architecture, permissions and operating discipline needed to put AI to work across teams, systems and sensitive information.
Design your AI architectureWhat needs to be reusable, and what needs to remain under local control?
A collection of working pilots is not yet an enterprise platform. Model access, knowledge permissions, evaluation, integrations and incident ownership need deliberate boundaries. We separate shared foundations from use-case-specific workflows so teams can build without duplicating the most important controls.
WHAT YOU RECEIVE
Target architecture, an integration and identity model, evaluation standards, a prioritised delivery roadmap and an operating-responsibility matrix.
Permission-aware knowledge
Connect selected repositories, retain document ownership and apply the caller’s permissions to retrieval. Answers should link to their sources and handle stale or conflicting material.
Governed agents and applications
Give each agent a bounded job, limited tools and explicit escalation paths. Separate a proposed action from permission to execute it, especially where money, access or commitments are involved.
An operating model across teams
Agree who owns the platform, each workflow, its data and its evaluation. Create release gates, incident procedures and service expectations appropriate to the consequences of failure.
- ✓Identity, roles and access boundaries
- ✓Evaluation before material changes
- ✓Action traces and incident ownership
- ✓Provider and deployment review
Can existing AI pilots become part of the platform?
Often, but they should be assessed rather than absorbed automatically. We review the pilot’s data access, dependencies, evaluation, ownership and actual usage before choosing to extend, replace or retire it.
Does enterprise AI mean private deployment?
Not always. The deployment decision depends on the information, providers, threat model, latency and operating capacity. Different workloads may reasonably use different patterns.
What should AI do
for your business?
Tell us what you want to build, connect or improve.
We’ll help define the architecture, the delivery and what it takes to run it.