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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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

A vector database is a retrieval primitive. A context engine is a complete context management system. They solve different parts of the AI stack.

What a vector database does

A vector database embeds data as high-dimensional vectors and retrieves records by cosine similarity to a query vector. It answers one question well: what items in the index are most similar to this input?

Great for

Semantic search
Document retrieval
Recommendation

Not designed for

Staleness management — no way to mark data as superseded
Conflict resolution — contradictory records surface side by side
Context assembly — returns a ranked list, not a coherent working set

What a context engine does

A context engine manages the full lifecycle of information that feeds into AI inference — not just retrieval.

Full lifecycle

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

Designed for

Agents running multi-step tasks
Long-running tasks with evolving information
Systems that need to compound improvements

Feature comparison

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

When to use a vector database

A vector database is the right tool when your requirements are well-scoped and retrieval is the entire job:

—Static document retrieval over a corpus that does not change
—Single-turn Q&A where the answer comes from one document
—Semantic search as a feature inside a larger application

When you need a context engine

When retrieval is only one part of the problem, you need the full lifecycle:

AI agents running multi-step tasks

Agents accumulate observations across many tool calls. A vector database returns matches — a context engine maintains a live working set that updates as the task progresses.

Applications where information changes over time

When facts get updated, corrected, or superseded, a vector database has no mechanism to reflect that. A context engine tracks the full history and surfaces only what is currently true.

Systems that need to compound improvements

Write-back lets the system learn from outcomes. Each session leaves the context in better shape than it found it — no cold-starting from zero.

Frequently asked questions

Can I use both a vector database and a context engine together?▾

A context engine like Cilow includes its own vector index. You do not need a separate vector database. Cilow handles the full context lifecycle, including the similarity search layer.

Is a context engine more expensive than a vector database?▾

A context engine replaces multiple tools you would otherwise pay for separately: a vector database, a retrieval pipeline, a reranker, and the engineering time to glue them together.

Do I need to migrate my existing vector data?▾

Cilow ingests data from your existing sources. You connect your data sources and Cilow builds the context layer on top — there is no manual migration of vector embeddings.

Stop stitching together retrieval primitives. Get the full context lifecycle in one system.

Build with Cilow instead → Join Beta