Build with Sprinklenet.
A technical entry point for partners evaluating Knowledge Spaces, cited AI assistants, public reference data, and reviewed integration access.
reference-dataplanned
Public source packs and cited threshold datasets, starting with federal acquisition references.
examplesplanned
Sanitized embed patterns, bot configuration samples, and partner workflow notes.
integration-notesqueued
Short technical guides for access models, source discipline, citations, and review.
partner-accessreview
Private workspace, API, and deployment access after use case and security review.
Spin Up Knowledge Spaces in Minutes
Self-serve developer access is live. Register, bring your own model key, and provision your own organization, knowledge spaces, and bots through the Knowledge Spaces API. You build the front end. Sprinklenet runs the governed backend: access control, tenant isolation, and audit.
Create your account
Register and spin up your own organization. The free tier includes 3 knowledge spaces, 3 bots, and 3 members.
Get your API key
Request a Knowledge Spaces API key scoped to your organization.
Add your model key
Bring your own foundation-model key from OpenAI, Anthropic, Google Gemini, and more. You stay in control of model spend.
Build
Create spaces, upload documents, configure bots, and run governed AI programmatically through the API.
Evaluate First
Start with the public surfaces. Private access begins when the workflow and data boundary are clear.
Partner Access Map
Reviewed access can include the platform surfaces below. Scope depends on the workflow, data boundary, and deployment path.
Hosted pages and widgets for approved assistants.
Tenant, user, space, bot, and workflow provisioning.
Source ingestion, citation rules, refresh plans, and access control.
Model routing, guardrails, review states, activity logs, and usage rollups.
Signals and workflow handoffs to approved partner systems.
Build Matrix
Most partner work starts as one of three motions. The right scope is narrow, measurable, and tied to a real workflow.
Cited Intelligence Surface
Assistant or decision support inside a partner experience.
Workflow, audience, and approved sources.
Knowledge Spaces runtime, citations, guardrails, and review path.
Embedded assistant or private page.
Reference Data Layer
Maintained source layer for high-change domains.
Authoritative sources and update rules.
Ingestion pattern, versioning discipline, and source-linked answers.
Reusable data product or answer layer.
Workflow Extension
Knowledge Spaces inside a product or operating process.
Surface, roles, and integration constraints.
Tenant model, API plan, events, and usage telemetry.
Controlled integration path.
Release Queue
First public artifacts planned for GitHub.
FAR Reference
Current acquisition reference data and threshold datasets.
planned
Knowledge Spaces Examples
Sanitized embeds, sample configs, and integration notes.
planned
Partner Build Notes
Short guides for access, citations, and deployment review.
queued
Release Standard
- No client data, private endpoints, credentials, environment configuration, platform internals, or customer-specific workflows.
- Every public artifact gets source-quality review before publication.
- Private workspaces and deployment paths require approval.
