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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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

Context engine vs. RAG

RAG retrieves relevant text. A context engine builds the right working set. RAG is a retrieval step. A context engine is a complete pipeline replacement.

What RAG does

RAG embeds text as vectors and retrieves chunks by cosine similarity to a query. It answers one question well: what text in the index is most similar to this input?

Great for

Document Q&A
Knowledge base search
Single-turn queries

Not designed for

Staleness — no mechanism to mark information as superseded
Conflicts — contradictory chunks surface side by side
Write-back — outcomes are not fed back into the context store
Compound improvements — each session starts from zero

What a context engine does

A context engine replaces the entire retrieval and assembly layer — not just the similarity search step.

Full lifecycle

Ingest → structure → rank → assemble → write back. Every stage feeds the next so the working set is always current and coherent.

Also handles

Staleness and supersession
Conflict resolution across sources
Multi-source coherence
Outcome write-back to improve future sessions

Feature comparison

Capability	RAG	Cilow Context Engine
Semantic similarity search	Yes	Yes
Staleness / supersession handling	No	Yes
Conflict resolution	No	Yes
Context assembly (not just retrieval)	Partial	Yes
Temporal reasoning	No	Yes
Outcome write-back	No	Yes
Multi-source coherence	Partial	Yes
Designed for agents	Partial	Yes

The migration path

Cilow replaces the retrieval and prompt assembly layer. Your model and app logic stay the same.

Connect data sources to Cilow

Point Cilow at your existing sources. Cilow ingests and structures the data — no manual reindexing required.

Replace your retrieval call with Cilow's context query

Swap the retrieval + reranking + assembly logic for a single API call. The response is a coherent working set, not a ranked list of chunks.

Let Cilow handle ranking, assembly, and write-back

Cilow tracks staleness, resolves conflicts, and writes outcomes back automatically. Each session improves the next.

Frequently asked questions

Can RAG and a context engine coexist?▾

No migration is needed. Cilow replaces the retrieval + assembly layer entirely. You keep your model, your agent framework, and your application logic unchanged.

Does switching from RAG require reindexing?▾

Cilow ingests from your existing sources. You connect your data and Cilow builds the context layer — no manual reindexing required.

Is a context engine harder to set up than RAG?▾

Cilow provides a single API that replaces the retrieval, reranking, and assembly steps you would otherwise build and maintain separately.

Stop patching retrieval with more retrieval. Replace the whole layer in one step.

Replace your RAG pipeline → Join Beta