Self-hosted AI tooling

First deliverable

A scoped architecture and policy outline for the intended users, models, data, tools, and approval points.

Who this is for

Organizations that need an internal AI workflow, want greater control over its deployment, or need an existing process made easier to inspect.

A scoped architecture and policy outline for the intended users, models, data, tools, and approval points.
The agreed assistant, reviewer, agent workflow, or integration slice running in the selected environment.
Operational notes covering access, human review, observed validation, and open risks for the delivered slice.

Identify the job the tool should help with, the data it may access, and decisions that must stay with people.
Define a small workflow, capability boundaries, and an evaluation approach before broadening access.
Implement the scoped integration, review behavior with real examples, and hand over the operating constraints.

Representative, safe-to-share examples of the work the tool should support and the quality bar for its output.
Constraints on deployment, identity, data handling, model providers, integrations, and audit records.
A responsible operator who can approve the workflow, review output, and own exceptions after handoff.

Model output can be incomplete or wrong and requires review appropriate to the decision it informs.
Self-hosting can change control and deployment choices; it does not by itself make a system private, secure, compliant, or available.
Provider behavior, model availability, external tool permissions, and third-party service terms remain outside Dotcoder’s control.

Self-hosting can change control and deployment choices; it does not by itself make a system private, secure, compliant, or available.

A responsible operator you name. Model output can be incomplete or wrong and requires review appropriate to the decision it informs.