AI needs product surfaces
Agents should create forms, tables, progress, choices, and actions, not only text responses in a chat transcript.
Why Democr.ai
AI features fail when the application layer is missing.
Models can reason, call tools, and generate useful output. Production software still needs UI, identity, documents, storage, permissions, quotas, observability, deployment, and clear extension boundaries. Democr.ai packages those runtime layers into one open framework.
The missing layer
Most teams do not need another demo. They need AI behavior to become application behavior.
A prototype can send prompts to a model. An application needs repeatable runtime contracts for every step around that model.
Prototypeprompt, tool call, answer
Applicationusers, state, permissions
RuntimeUI, documents, engines, policy
Operationaudit, quotas, deployment
AI needs governed context
Documents, embeddings, graph context, tools, and model calls must carry user, organization, module, and request scope.
AI needs operational evidence
Teams need to know what ran, which model was used, what data changed, which quota applied, and why a request failed.
Who it is for
Built for teams turning AI into software, not isolated experiments.
Democr.ai is most useful when developers need to ship AI features inside real products, and platform teams need those features to remain governable.
Application developers
Build AI modules with Python actions and YAML UI while the runtime owns transport, effects, providers, storage, and tasks.
AI platform teams
Standardize model providers, engine orchestration, extractors, quotas, audit, and deployment patterns across teams.
Controlled organizations
Self-host AI workflows with scoped data, sandbox policy, visible approvals, model usage limits, and inspectable execution flows.
What makes it different
Democr.ai is an application runtime, not a single-purpose AI component.
Use model providers, vector stores, graph stores, object storage, and observability systems where they fit. Democr.ai gives them a coherent application boundary.
UI is part of the runtime
A2UI lets backend modules define structured UI once and render it through multiple clients.
Model execution is governable
Engines can run local or remote providers, enforce token/request quotas, and scale from a single orchestrator to cluster placement.
Extensions are bounded
Modules, engines, extractors, tools, agents, and skills use SDK contracts and run behind sandbox, permission, and audit boundaries.
Why open and self-hostable
Some AI systems cannot be a black box around your application.
Teams building regulated, private, or operationally critical AI workflows need control over data, providers, runtime policy, and deployment shape.
Choose infrastructure
Run local-first, use cloud providers, or move storage, media, queues, graph, vector, and observability services outward by configuration.
Keep execution inspectable
Correlate actions, model calls, tasks, tool output, ingestion state, data mutations, and runtime policy decisions.
Avoid provider lock-in
Use local engines, OpenAI-compatible APIs, cloud providers, and configured objectives behind the same SDK boundary.
Build real application modules
Keep product behavior in modules instead of scattering it across frontend state, scripts, model wrappers, and ingestion jobs.
Next
Related pages
Use these pages to move from the concept to adjacent parts of the runtime.