Fragmented AI stack
- Every product connects to company systems separately
- Access rules are rebuilt in each tool
- Subscriptions and APIs are paid in different places
- Agents and apps cannot reuse shared components
- Support gets harder with every new tool
Why Wide Moat
Enterprise AI eventually becomes a platform.
Teams start with separate tools and models. Then the company inherits repeated integrations, different access rules, multiple bills and no shared operating view.
Not another AI tool
Move from disconnected solutions to shared infrastructure.
One Wide Moat platform
- Connect systems once through shared connectors
- One identity and access layer
- One catalogue of approved models
- Central budgets and token-spend control
- Shared skills, data and policies for every agent and app
Connect once. Govern centrally. Reuse across every AI use case.
Wide Moat does not force useful tools out. It gives them one enterprise foundation.
What changes
One foundation replaces many separate control planes.
Connections
Shared connectors for assistants, agents and applications.
Access
One identity and permission model across AI use cases.
Models
Route work across approved internal and external models.
Spend
Central budgets, limits and visibility into token costs.
Building blocks
Shared skills, data and policies across the platform.
Operations
One managed platform instead of a fragile tool stack.
What stays
Your systems of record and useful specialist tools.
ERP, CRM, BI, repositories, workflow automation and approved AI tools remain in place. Wide Moat gives them shared infrastructure.
When a platform matters
When different use cases begin to share the same foundation.
Several functions, sensitive data, repeated integrations, multiple models and the need to manage spend centrally are signs that point tools are no longer enough.
Start with one valuable function
Identify what your next AI use cases should share.
Choose one function, connect the minimum systems and agree the baseline before expanding.