Knowledge Spaces is Sprinklenet's AI governance and knowledge management platform. It is the governed control layer for teams building AI for many users, customers, and audiences at once. It runs in production today behind decision dashboards and multi-company knowledge platforms. Bring your knowledge, your workflows, and your brand. We build your layer on top and ship it to production in six weeks.
Every Major Model, Under Your Rules
Curated commercial families, hosted open models, or your own keys.
Platform architecture and how the control layer works.
Read it →Running AI on regulated data: security, governance, and data handling.
Read it →The Problem
Most organizations run AI experiments. Few run AI in production. Real work on real data exposes three gaps at once, and that is where pilots die. Knowledge Spaces is the control layer that closes all three.
Architecture
Knowledge Spaces sits between your people, your applications, and the models. It routes, retrieves, governs, and audits every call. Every bot sees only the Spaces you grant. Every audience sees only the bot you give them. Isolation is enforced on every request.
Proof
Production and in-build systems, described without client names.
A federal-acquisition assistant grounded in the FAR. In production today and publicly callable.
A partner runs assessments and wellbeing programs for many organizations at once, on their own data and under their own brand.
A private-capital partner is consolidating scattered portfolio-company data into one governed layer that turns fragmented reporting into board-ready answers across the firm's holdings.
A city-scale operating picture that fuses service, infrastructure, and population-signal data into one governed view for senior public-sector decision-makers.
Build On Top
Bring us your methodology and your data. We build your AI product on Knowledge Spaces, ship it to production in six weeks, and stay to run it with you.
One backend, any surface: assistants like FARbot, AI inside your own applications via the API, and custom workflow front ends. The platform comes with the team that builds on it every day. That team has built AI systems for two decades across startups, enterprises, and federal programs, and it works alongside yours.
How It Works
Three steps from raw knowledge to a governed bot in production.
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, not after the fact.
Four-tier roles, sharing rules, configurable guardrails, evaluation gates, usage tracking by org and bot, and a full audit log on every Space.
Ship bots to a product, portal, or API. Switch the model behind any bot in three clicks, no rebuild.
Where We Fit
We do not replace your AI stack. Bring the systems and models you already run. We add the governed control layer and ship one high-value workflow to production, on your data, under your rules.
You keep your data, your workflows, your brand, and the keys. Production-ready in six weeks.
Security and Governance
The governance controls available today, with the federal credentials behind them.
Four-tier roles, scoped per Space and per bot.
Multi-factor authentication, with SAML 2.0 SSO and CAC and PKI support for enterprise and federal deployments.
40+ event types, per-bot conversation history, and exportable records.
Test cases and scored eval runs on every bot. A synthetic document set with known correct answers can be loaded into a separate Space, so a bot is tested before real data is shared.
Answers grounded in approved sources with citations, scoped to each bot.
Configurable input and output rules with escalation handling. Designed to extend to PII handling, prompt-injection defense, and content moderation in governed deployments.
Federal Procurement, Identity, and Deployment
GSA MAS 47QTCA25D00F0 (SINs 541611, 54151S, OLM). CAC/PKI and SAML identity available.
GovCloud-ready, with a FedRAMP authorization pathway and sovereign deployment options.
The Engagement
A senior Sprinklenet team scopes, builds, and deploys one governed workflow on your data. At the end of six weeks, real users can use it, you have measured results, and leadership has a clear decision on expansion.
Choose the workflow, success measures, systems, data, and governance requirements.
Build the workflow on your data inside a governed Knowledge Space.
Put it in front of real users with guardrails, audit, and an expansion plan.
After the Pilot
When the six-week pilot proves value, we stay on the inside as your platform team, operating and extending Knowledge Spaces through a monthly engagement with a named senior team accountable for outcomes.
We run the platform to production standard: uptime, model updates, cost, and access, all handled by us.
Every model call, retrieval, and action stays audited and policy-bound as your data and rules change.
We ship your next high-stakes workflow on the same governed layer. No rebuild.
Your platform, your data, your rules. Operated to production standard, with the people who built it still on the inside. We track the value each workflow delivers and tie our engagement to it.
Knowledge Spaces is Sprinklenet's AI governance and knowledge management platform. It sits between your people, your applications, and the models. It routes, retrieves, governs, and audits every call.
Knowledge Spaces is for teams building AI for many users, customers, and audiences at once. Every bot sees only the Spaces you grant, and every audience sees only the bot you give them.
Knowledge Spaces routes across 16+ foundation models. Teams can use curated commercial families, hosted open models, or their own keys.
Four-tier roles and sharing rules set who can reach each Space and bot, and isolation is enforced on every request. Multi-factor authentication and SAML 2.0 SSO with CAC and PKI support are available.
No. Knowledge Spaces is not currently FedRAMP authorized. A FedRAMP authorization pathway is available for programs that require it.
Knowledge Spaces is Sprinklenet's proprietary platform. It is provided as a hosted service or under a commercial license.
Sprinklenet delivers a working prototype in the first two weeks and moves from pilot to production in approximately six weeks.
One working session on your systems, your governance requirements, and a concrete plan for production AI.
Building with your own engineering team? Start at Developers.
Jamie Thompson on deploying AI you actually control.
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