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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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.

How Cilow replaces RAG

You swap the retrieval and context assembly layer for Cilow's context API. Your model and application logic stay the same.

What you're replacing

Typical RAG stack

—Embedder
—Vector DB
—Retriever
—Reranker
—Prompt template

Cilow replaces

✓Vector DB
✓Retriever
✓Reranker
✓Prompt assembly
in one API call

What Cilow gives you instead

→

Ranked, current, conflict-resolved context

Every context response is ranked by relevance and recency, with contradictions resolved before your model ever sees the data.

→

Outcomes written back automatically

Corrections, results, and feedback are written back into the context layer. Each session makes the next one better.

→

Multi-source coherence

Documents, chats, tool calls, and structured data are unified into a single coherent context — no per-source logic required.

The migration path

Connect your data sources

Connect your existing data sources to Cilow. Documents, chats, APIs, structured records — Cilow ingests from any source.

Replace your retrieval call

Swap your retrieval + prompt assembly logic for a single Cilow context query. Your model and agent framework stay the same.

Let Cilow handle the rest

Cilow handles ranking, conflict resolution, context assembly, and outcome write-back automatically.

What you keep

Your model

OpenAI, Anthropic, open weights — all work

Your agent framework

LangChain, LlamaIndex, custom — all work

Your application logic

Business rules, prompts, orchestration stay unchanged

Your existing data sources

No re-ingestion or re-embedding required

Frequently asked questions

How long does the migration take?▾

For most teams, the integration is a single API swap. You replace the retrieval call with a Cilow context query. Everything else — your model, your agent framework, your application logic — stays the same.

Do I need to re-embed all my data?▾

No. Cilow ingests from your existing sources. You connect the source and Cilow handles the indexing, embedding, and structuring.

What if I'm using LangChain or LlamaIndex?▾

Cilow works as a drop-in replacement for the retrieval layer in any framework. You keep your orchestration layer and replace the retrieval and assembly step.

One API call replaces your entire retrieval and assembly pipeline.

Start the migration → Docs