Enterprise AI connected to real systems.
X-ITM helps organisations move from isolated model experiments to governed AI capabilities connected to enterprise context, identity, tools and workflows.
Architecture
Model, data, retrieval, security and runtime patterns.
RAG & Search
Approved enterprise knowledge and operational data.
Agents
Bounded agents with explicit tools and controls.
Integration
AI connected to cloud, engineering and internal systems.
Discuss an enterprise AI programme.
Start with the business workflow, data boundary and operating controls.
The enterprise problem
Enterprise AI Consulting 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 Consulting 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.