Knowledge Spaces 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. 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
Real systems built on Knowledge Spaces, described by shape rather than by name.
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 people who build on it every day, a team that has built AI systems for two decades across startups, enterprises, and federal programs, working alongside yours rather than at arm's length.
How It Works
One backend, three moves 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. No rip and replace.
Security and Governance
The governance spine that exists today, stated plainly, plus the federal credentials behind it.
Four-tier roles, scoped per Space and per bot.
Multi-factor authentication today; SAML 2.0 SSO with CAC and PKI support on the near-term roadmap.
40+ event types, per-bot conversation history, and exportable records.
Test cases and scored eval runs on every bot.
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.
Built for Federal Reality. GSA MAS 47QTCA25D00F0 (SINs 541611, 54151S, OLM). CAC/PKI and SAML identity on the roadmap.
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.
One scoped pilot. One deployed workflow. Real users, measured results, and a clear expansion decision.
After the Pilot
The six-week pilot is the entry ramp, not the finish line. When it 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. When the platform performs, so do we.
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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