We build Knowledge Spaces: governed multi-LLM AI for federal and regulated teams. Secure, auditable, vendor-neutral. Pilot to production in six weeks.
Miami, FL
Joined March 2014
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We build the control layer for enterprise AI. Every major model, your rules. Audit trails by default. Answers grounded in your sources, with citations. Knowledge Spaces takes governed AI from pilot to production, in your cloud or ours.
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The pattern that wins: proprietary knowledge on governed infrastructure.
Your product, your expertise, a control layer underneath handling models, guardrails, and audit.
If AI features are on your roadmap, that's the build: sprinklenet.com/knowledge-sp…
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"We'll add security before launch" is the most expensive sentence in enterprise AI.
Identity, permissions, and audit are load-bearing walls, not trim. Retrofitting means rebuilding.
Ask to see the audit log before the chatbot.
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No eval set, no feature.
A few hundred real questions with graded answers, re-run on every change, is the difference between an AI product and a slot machine.
It's also the only honest answer to "which model is best."
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One API in front of every major model.
Retrieval over your documents with citations.
Guardrails and audit on every call.
SSO wired in.
The parts of an AI feature your customers never see, already built: sprinklenet.com/developers/
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We're hiring software developers.
Around 3 years of C#/.NET and SQL experience.
Mostly remote, U.S. based.
Apply: sprinklenet.com/careers/deve…
🤖 Made with AI
ALT Hiring software developers with C#/.NET and SQL experience, three-plus years. Large white text on a navy background beside a charcoal keyboard with a blue Enter key.
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We're hiring junior developers with 3+ years of C#/.NET and SQL experience.
Mostly remote. U.S. citizens based in the U.S. Bachelor's degree required. OneStream training provided.
Apply: sprinklenet.com/careers/deve…
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Every model deprecation is a forced migration.
With evals and a routing layer it's a config change plus a test run.
Without them it's a quarter of regression hunting.
That's the whole case for treating models as swappable components.
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Production-ready AI, defined:
Known users. Permission-aware answers. Grounded sources. Platform-level guardrails. Full audit trail. Evals on every change. A named owner with a budget.
No model on the list. Models rotate; the list stays.
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Before an AI pilot, identify the source owner, update frequency, access requirements, and the user's next action. These details shape the integration scope and the operating responsibility.
sprinklenet.com/why-ai-pilot…
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Government buyers ask the best AI questions:
Where's the data? Who holds the keys? What's the audit story? How do we exit?
Every one applies to a commercial deal word for word. Borrow the checklist. Watch which vendors answer in writing.
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Choosing an AI implementation partner? Ask for a system diagram, an evaluation plan, and a named operating owner. Those answers make the delivery approach easier to assess.
sprinklenet.com/vendor-due-d…
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Our most reliable bots are the narrow ones.
Defined scope. Approved sources. Citations on every answer. Refusal outside the lane.
Try the live ones: sprinklenet.com/ai-demos/
Narrow is what trustworthy looks like.
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Test an AI knowledge assistant on routine questions, conflicting sources, and questions its documents cannot answer. Check the cited evidence and define when a person should review the result.
sprinklenet.com/rag-evaluati…
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In regulated industries the AI question isn't what the model can do.
It's where the data goes, who holds the keys, and whether you can produce the record in two years.
Answer those in architecture and the rest gets easy.
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Sprinklenet is a GSA Multiple Award Schedule contract holder providing AI advisory, engineering, and systems integration services. Bring a specific federal requirement to discuss scope and technical fit.
sprinklenet.com/government-s…
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Sprinklenet connects senior AI advisory with engineering. We work alongside leadership and technical teams on what to fund, who owns delivery, and how the result will be assessed.
sprinklenet.com/technology-a…
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You wouldn't run finance on a system that can't be audited.
Plenty of companies run customer conversations on AI that can't be inspected.
Which model answered, from which sources, what changed since last quarter. Show, don't explain.
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Small senior team, on purpose.
The person in your standup made the architecture call and answers for it. No junior pyramid, no sales-to-delivery handoff.
It caps our client count. We're fine with that.
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Build your own AI platform and you own evals, guardrails, audit, connectors, and every model migration. Forever.
That's a whole product.
If customers will never pay you for it, don't build it. Build what only you can build.
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