The interesting engineering bit: retrieval uses a distance threshold — if nothing relevant is found, the system responds "not found" instead of forcing the LLM to guess. Every response carries the exact chunk metadata for source citations.
Runs on a normal laptop. No GPU server needed.
👥 Enterprise-Grade Team Features
This isn't a weekend demo — it's built for real organizations:
- Multi-tenant: isolated organization workspaces
- Access control: admin whitelist, role-based permissions
- Security: salted password hashes, session tokens, brute-force rate limiting
- Recovery: one-time recovery codes for password resets
- Collaboration: team chat, knowledge sharing with admin approvals
- Licensing: trial → license system built in
66+ test scenarios passed before launch. Security-hardened. Windows-verified with automatic desktop-icon installer.
💰 The Model (Simple)
- 7-day free trial — no credit card
- $699/year per organization — unlimited employees
- Per-person cost: effectively zero
- India: ₹24,999/year
Solo-built. Bootstrapped. Zero burn. Launched in 2 weeks.
🎯 Why This Is a Moat, Not a Feature
Every AI company is fighting for the cloud market.
But the privacy-mandated segment — GDPR companies, law, healthcare, defense, finance — cannot use cloud AI no matter how good it gets. Their legal teams will never approve it.
Nobody built for them.
Until now.
🚀 Try It (Free, 7 Days, No Card)
https://knowledges-landing-8d70qc8y6-knowledge-os1.vercel.app
Upload your own company handbook. Ask it real questions. Try the WiFi-off trick.
💬 Questions for This Community
I'd genuinely value feedback from developers and founders here:
- Would your company's legal team approve an on-premise AI like this?
- What's missing for enterprise adoption in your view?
- Anyone optimized local RAG retrieval further? (tips welcome!)
Happy to answer anything about the architecture, the security design, or the solo-build journey.
Building in public. Privacy-first. No cloud was harmed in the making of this product. 🔒
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