Before buying more AI, identify the operating architecture.
The assessment maps workloads, models, data sources, identity, tool access, deployment constraints, governance and observability.
Model strategy
Which workloads need which capabilities and what lock-in is acceptable.
Data & retrieval
Which sources are authoritative and how existing permissions are preserved.
Agent & tool security
Which actions remain read-only, which require approval and where execution occurs.
Production model
Networking, secrets, logging, cost, resilience, support and lifecycle.
Deliverable
A current-state and target-state architecture with prioritised implementation options, risks and a practical pilot boundary.
Request an AI architecture assessment.
Use the assessment as a vendor-neutral starting point before a larger implementation.
The enterprise problem
Enterprise AI Architecture Assessment is most valuable when it is treated as part of the operating architecture rather than an isolated tool purchase. X-ITM starts by identifying authoritative systems, ownership, constraints, security boundaries and the business or engineering outcome that must improve.
Problems we address
Disconnected systems
The relevant evidence is spread across platforms, teams and ownership boundaries.
Incomplete context
Point tools can answer local questions while missing dependencies elsewhere in the estate.
Governance overhead
Manual review and evidence collection slow delivery and become stale.
Production gap
A proof of concept may not include the controls, observability and ownership required to operate.
What the capability includes
Evidence-led discovery
Start with real systems, architecture and authoritative data.
Target architecture
Define integration, identity, network, data and operating boundaries.
Implementation
Build and integrate the required capability rather than stopping at recommendations.
Operationalisation
Validate, document, observe and support the production service.
What a good outcome looks like
- A defensible technical decision based on current-state evidence.
- Reduced implementation uncertainty before larger investment.
- Clear security, operational and ownership boundaries.
- A practical route into pilot, production implementation or managed operation.
Delivery model
- Discover the environment, users, systems and constraints.
- Define target architecture, controls and measurable acceptance criteria.
- Implement the smallest useful production-capable slice.
- Validate technically and operationally before expansion.
- Operate, measure and improve using real evidence.
Enterprise controls built into delivery
How Core accelerates this
Core can accelerate the work when connected enterprise context, AI routing, cloud/engineering intelligence, governance, search or automation are relevant to the engagement.
Typical engagement entry points
Technical assessment
A bounded current-state review with target architecture, risks and prioritised next steps.
Pilot
Prove one valuable workflow or intelligence capability against real systems and explicit acceptance criteria.
Implementation
Move the approved architecture into production with integrations, controls, validation and handover.
Managed engineering
Continue operating, improving and extending the capability after initial delivery.
Turn this into an implementation plan.
Bring the current environment, constraints and desired outcome. X-ITM will help identify the smallest credible next step.
The enterprise problem
Enterprise AI Architecture Assessment is most valuable when it is treated as part of the operating architecture rather than an isolated tool purchase. X-ITM starts by identifying authoritative systems, ownership, constraints, security boundaries and the business or engineering outcome that must improve.
Problems we address
Disconnected systems
The relevant evidence is spread across platforms, teams and ownership boundaries.
Incomplete context
Point tools can answer local questions while missing dependencies elsewhere in the estate.
Governance overhead
Manual review and evidence collection slow delivery and become stale.
Production gap
A proof of concept may not include the controls, observability and ownership required to operate.
What the capability includes
Evidence-led discovery
Start with real systems, architecture and authoritative data.
Target architecture
Define integration, identity, network, data and operating boundaries.
Implementation
Build and integrate the required capability rather than stopping at recommendations.
Operationalisation
Validate, document, observe and support the production service.
What a good outcome looks like
- A defensible technical decision based on current-state evidence.
- Reduced implementation uncertainty before larger investment.
- Clear security, operational and ownership boundaries.
- A practical route into pilot, production implementation or managed operation.
Delivery model
- Discover the environment, users, systems and constraints.
- Define target architecture, controls and measurable acceptance criteria.
- Implement the smallest useful production-capable slice.
- Validate technically and operationally before expansion.
- Operate, measure and improve using real evidence.
Enterprise controls built into delivery
How Core accelerates this
Core can accelerate the work when connected enterprise context, AI routing, cloud/engineering intelligence, governance, search or automation are relevant to the engagement.
Typical engagement entry points
Technical assessment
A bounded current-state review with target architecture, risks and prioritised next steps.
Pilot
Prove one valuable workflow or intelligence capability against real systems and explicit acceptance criteria.
Implementation
Move the approved architecture into production with integrations, controls, validation and handover.
Managed engineering
Continue operating, improving and extending the capability after initial delivery.
Turn this into an implementation plan.
Bring the current environment, constraints and desired outcome. X-ITM will help identify the smallest credible next step.