Cilow is a context engine for AI. It replaces fragmented retrieval stacks with a single system that ingests data, structures it, keeps it current, and serves the right context at inference time.

Why isn't RAG enough?

RAG is a patch, not a foundation. It can retrieve relevant fragments, but it does not reliably decide what is current, what conflicts, what matters most, or what should actually be in the model's working set.

Does Cilow replace vector databases, search, or RAG?

Yes. Cilow replaces vector databases, search pipelines, and traditional RAG systems with one unified context layer for AI. Instead of stitching together embeddings, retrieval, filters, rerankers, and prompt logic, you send data to Cilow and query it directly for usable context.

How is Cilow different from a vector database or GraphRAG stack?

Vector databases retrieve similarity. GraphRAG adds relationships. Cilow goes further: it handles ingestion, structuring, updating, conflict resolution, and context assembly in one system. The goal is not to return more data. It is to give models the right data.

Does Cilow support continual learning without retraining?

Yes. Cilow updates context without changing model weights. As information changes, the system updates what the model sees, so applications stay current without constant retraining or brittle prompt hacks.

What kinds of data can Cilow use?

Cilow is built for mixed, real-world data: documents, chats, code, APIs, product data, tickets, notes, internal tools, and structured records. If your AI system depends on it, Cilow can turn it into usable context.

How does Cilow handle changing or conflicting information?

Cilow tracks where information came from, when it changed, and what should supersede older context, so models are less likely to reason over stale or contradictory inputs.

Cilow is for teams building serious AI products: agents, copilots, research systems, internal AI tools, and applications that need reliable context over time. If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.

How does Cilow fit into my stack?

Cilow sits where your retrieval and context layer would normally be. Instead of maintaining a separate search stack, vector database, and RAG pipeline, you plug Cilow in as the system that prepares context for your models.

Why does this matter now?

AI products are hitting the same wall: too much data, too many tools, and too much brittle glue. The next generation of AI systems will not be built on better prompts alone. They will be built on better context infrastructure.

[TEAM]

The team building the memory OS for agents

A small, technical team focused on making stateful AI agents reliable in the real world.

Founder & CEO

Stephen Keehn

Stephen is a builder, engineer, and lifelong learner focused on giving AI agents the same advantage humans have: memory. He has spent years shipping systems where the hardest bugs came from missing context, not bad logic. Cilow is his answer to that problem at infrastructure scale.

Co-Founder & CTO

Mihir Gupta

Mihir is the co-founder and CTO of Cilow with a background in AI engineering and production software development. He designed and shipped machine-learning-powered systems and developer tools, working across the stack from model training to backend services and deployment. He cares deeply about making stateful AI agents reliable in real-world environments, not just in demos.

Want to join us? contact@cilow.ai