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SOLUTION

Enterprise AI

Governed AI platform foundations: access control, evaluation, cost management, and deployment standards.
THE POSITION

Scaling AI is
a governance
problem long
before it is a
model problem.

Pilots multiply, each with its own keys, prompts, and evaluation approach. Nobody can answer what is deployed, what it costs, or how well it performs.

We establish a platform layer: a single gateway, shared retrieval, standard evaluation, and policy enforced centrally.

CAPABILITIES

What the engagement includes

01
Model gateway

One controlled entry point with routing, quotas, and logging.

02
Shared retrieval

A governed knowledge layer with document-level permissions.

03
Evaluation standard

Every use case ships with a test suite and a quality bar.

04
Cost visibility

Spend attributed per team and per use case.

SYSTEM ARCHITECTURE

How the system runs end to end

REFERENCE FLOW
01
Standardise

One gateway, one logging format, one policy set.

02
Govern

Access, retention, and residency rules enforced centrally.

03
Evaluate

Quality measured continuously, not at launch only.

04
Enable

Teams build on paved paths instead of from scratch.

05
Report

Performance and cost reported to leadership.

TECHNOLOGY

Typical stack

Kubernetes Terraform PostgreSQL pgvector OpenTelemetry Azure AWS
OUTCOMES
Visibility

A single answer to what is running and how it performs.

Reusability

Second and third use cases cost far less than the first.

Defensible controls

Policy demonstrable to auditors and regulators.