We're building the governance layer of enterprise AI.
Every company is racing to deploy internal AI on top of documentation it doesn't fully trust. Our mission is to make sure that what those AIs retrieve is consistent, current, and aligned with operational reality — at every step, for every team.
"AI systems fail not because of models, but because of unreliable data."
Last year, a global bank deployed an AI assistant on 40,000 internal documents. In the first month, it gave 1,200 contradictory answers. The AI wasn't broken. The knowledge was.
Every company building internal AI is making the same bet: that their documents are good enough. They're not. The average enterprise has 150+ disconnected documentation sources, updated by different teams, on different schedules, with no cross-source consistency checks.
Enterprises have invested in AI. Now they need to control it.
When you build AI on top of unvalidated knowledge, you don't get intelligence — you get confident misinformation. The question isn't whether your knowledge base has conflicts. It does. The question is: do you find them before your AI does?
Three things converging
The infrastructure layer is settled. The governance layer is wide open.
AI deployment is accelerating
Every company is building internal AI in 2025–2026. Quality is the next bottleneck.
Regulatory pressure is real
The EU AI Act and emerging US frameworks require documented quality controls for AI systems.
Infrastructure is commoditizing
ChromaDB, Pinecone, OpenAI are commodity. The value is moving up the stack to governance.
Backed by enterprise software & AI veterans
Industry advisors from enterprise software, AI governance, LLM/RAG infrastructure, and B2B SaaS GTM — supporting Alignode's path from seed to category leader.
Want to talk about knowledge governance?
We'd love to hear about your AI deployment, the conflicts you're seeing, and where Alignode might help.