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By the third pilot, most companies have three sets of connectors, three permission models and no shared way to test or cost them.
Model access and connectors are recoded for each project.
Budgets and tests arrive after the pilot is live.
Agents reach the same system with different rules.
Only the original team can maintain custom parts.
The Foundry connects approved systems and models, applies controls and shows quality, cost and ownership from the first build.
Expose business systems as governed tools through MCP servers, built once and reused.
Separate clients, units or brands with namespaces, signed tokens, a tenant registry, suspension and audit logs.
Assign approved models to tasks and track usage, speed and cost per workload.
Give each agent a defined job and tool scope, with people approving major actions.
Keep test cases for each capability and record evidence when models or prompts change.
We find what your first two or three use cases have in common.
We set up the shared base and build the first custom workflow on top.
We measure quality and cost, improve shared parts and ship the next use case faster.
Connectors and controls carry to the next project.
Your workflow stays separate from the plumbing.
Quality, speed and cost are tracked before rollout.
Access is enforced at each connector.
Clients and units cannot see each other's data.
Approval is reserved for major decisions.
New models are tested before they go live.
Contracts, versions and owners replace custom wiring.
Client engagement, anonymized
Each private equity firm has its own templates, criteria and confidentiality rules, and building a custom system for each would be slow and hard to maintain. We built our PE platform on the Foundry: four MCP servers, 47 tools, AWS hosting and full tenant isolation, with each firm's rules in its own files. Onboarding a new firm means configuring its templates and presentation layer, nothing more.
A shared, governed environment for building AI applications. It provides reusable model access, connectors, permissions, testing and cost controls, so teams focus on business-specific work.
MCP is an open standard for connecting AI models to tools and data through defined servers. You build a connector once and reuse it across agents and models, with permissions enforced at the server.
With separate namespaces, signed tokens per tenant, a tenant registry, instant suspension and audit logs. The code is shared; the data never is.
Buy commodity pieces such as model access and hosting. Build what reflects your business: workflows, templates, domain knowledge and presentation.
Usually two or three. Once a second project needs the same connectors and controls, a shared base saves time on every project after it.
We will separate the reusable base from your custom workflow and pick the first use case.
Discuss your AI foundationOr email us at admin@pointonezero.com.