Managed AI Governance and Knowledge Management
Knowledge Spaces is a managed service for AI governance and knowledge management, hosted and operated by Sprinklenet. It is also the foundation for the bespoke applications and agentic workflows we build for customers, in any cloud or inside their own environments.
Three Gaps Between a Pilot and Production
Pilots stall at three points: model control, data governance, and the path to production. Real work on real data exposes all three at once.
- No Model ControlOne workflow, one model, no way to switch or compare without a rebuild.
- No Data GovernancePilots mix sources and audiences that should never meet.
- No Path to ProductionA demo that works once is not a system you can run.
A Foundation for Bespoke Applications and Agentic Workflows
Your methodology and your knowledge are the value. Sprinklenet runs the platform, and what you bring stays yours. Applications built on Knowledge Spaces inherit identity, retrieval limits, guardrails, and the audit trail, and they work alongside the models and enterprise AI products you already hold. Agentic workflows run with bounded tools and your permissions, and they stop for human approval before any consequential action. A partner already runs its own AI product on Knowledge Spaces in production, on its own data and under its own brand.
Application layers
Merlin
Falcon
Knowledge Spaces VRMS
- Identity and Role
- Policy and Guardrails
- Retrieval Boundary
- Audit Record
SharePoint
Entra ID
Microsoft 365
Salesforce
PostgreSQL
Commercial model families, hosted open models, or your own keys
Capture, Govern, Deploy
Capture
Upload documents or connect live systems through the connector library. Everything is indexed into a governed Space, and what each bot can retrieve is decided here.
Govern
Every bot sees only the Spaces you grant. Every audience sees only the bot you give them. Access runs through your identity provider and four-tier roles, with configurable guardrails and scored evaluation runs on every bot.
Deploy
Ship bots to a product, a portal, or an API. Staff ask in natural language and receive answers cited to your approved documents, retained under your records management rules. Change the model behind any bot by configuration, with no rebuild. Each change is versioned, scored against your evaluation set, and released through your approval step.
What Reaches the Model
Source material is indexed inside the governed boundary, and it is not bulk-loaded into the model. When an application asks a question, the model receives the prompt, the current exchange, and the retrieved context allowed for that bot and user. What does not reach the model is the full corpus, other spaces, or any other tenant's data.
Your Data
Records
Documents
Databases
Inside the Boundary
Tenant Separation
Minimum-Necessary Retrieval
Role-Based Access
Audit Logging
AI Model
Your Choice of Foundation Model
Ownership and Data Rights
Customers own their intelligence, their data, and their AI harness: the application code, connectors, configurations, prompts, policies, and evaluation sets built for them. That source code is yours to read, data rights are written into the engagement, and your data, application code, and evidence export in non-proprietary formats. Applications reach Knowledge Spaces through documented APIs, so your engineers or another firm can maintain them. If you leave the managed service, you take all of it with you, and the applications can keep running on Knowledge Spaces in your own environment under a commercial license. Knowledge transfer is a scheduled deliverable.
You own your data and the application layer we build for you under the engagement. Knowledge Spaces is Sprinklenet's proprietary platform and is provided as a hosted service or under a commercial license.
One Operator, Many Isolated Clients
Consultancies, software companies, and prime contractors hold data for many clients at once, and every client expects its data to stay its own. A principal organization holds each client in its own child organization, with its own Spaces, bots, roles, records, and billing. You deliver your methodology under your own brand and keep your customer relationships.
Principal Organization
Child Organization A
- Spaces
- Bots
- Roles
- Records
- Billing
Isolated From Each Other
Child Organization B
- Spaces
- Bots
- Roles
- Records
- Billing
Where Knowledge Spaces Runs
- Managed Cloud
- Hosted and run by Sprinklenet under service levels, with incident response, disaster recovery, rollback, and sustainment. Data is encrypted in transit and at rest. This is the fastest route to production and the usual starting point. Knowledge Spaces runs on AWS, and its security controls are aligned to NIST SP 800-53. Our systems and security program are ready for FedRAMP authorization, which we will pursue with an agency sponsor.
- Dedicated Tenant
- Single-tenant isolation for regulated work.
- Customer Environment
- A dedicated deployment on AWS, Azure, Google Cloud, government cloud regions, or on-premises infrastructure, including air-gapped networks, scoped for each engagement under a commercial license.
Knowledge Spaces is one subscription covering every function, metered by office and application, with spend limits and a cost report leadership can read.
Microsoft Azure
Building With Your Own Engineering Team
The platform is API-first: bots, sessions, connectors, and governance are callable. Register, add a model key, and make your first call in minutes. Two open papers cover how the platform works and how it handles regulated data.
Related Reading
See the Platform Run
We will give you a demonstration on public or synthetic data, then hold one working session on your systems, your governance requirements, and a concrete plan for production AI.