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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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

GraphRAG adds graph structure to retrieval to help with multi-hop reasoning. A context engine handles the full lifecycle: ingestion, updating, conflict resolution, and context assembly. GraphRAG solves a structural problem. A context engine solves the whole problem.

What GraphRAG does

GraphRAG builds a knowledge graph from documents and uses graph traversal to answer questions that require connecting multiple facts. It is a meaningful improvement over vanilla RAG for multi-hop reasoning.

Great for

Multi-hop Q&A
Relationship-rich corpora
One-time document sets

Still fundamentally retrieval

Graph traversal returns retrieved fragments, not an assembled working set
Contradictory nodes in the graph are retrieved together without resolution
No mechanism to record what the model acted on
The graph does not update when information changes

What GraphRAG still doesn't solve

Graph structure improves what gets retrieved. It does not change the fundamental limitations of a retrieval-only approach.

Staleness

The graph is built from a snapshot of your documents. When information changes, the graph does not update automatically. Stale nodes remain in the index alongside current ones.

Conflict resolution

When two nodes in the graph contradict each other, GraphRAG retrieves both. There is no built-in mechanism to decide which one is current or to surface the conflict to the model cleanly.

Outcome write-back

GraphRAG has no mechanism to record what the model acted on or what was confirmed correct. Each session starts from the same static graph.

Agent-oriented assembly

Agents need a compiled working set scoped to a task — not a ranked list of graph fragments. GraphRAG returns retrieved subgraphs; assembling them into coherent context is left to the application.

Feature comparison

Capability	GraphRAG	Cilow Context Engine
Semantic similarity search	Yes	Yes
Multi-hop relationship reasoning	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
Designed for agents	Partial	Yes

When GraphRAG is a good choice

GraphRAG is a good fit when your requirements stay within retrieval over a fixed corpus:

—Static knowledge bases where relationships matter
—One-time document corpora that do not change
—Multi-hop question answering over fixed data where structure improves recall

When you need a context engine

When the data changes, the agent needs more than retrieved fragments, or you need sessions to compound:

Data changes over time

When facts are updated, corrected, or superseded, a static graph is immediately out of date. A context engine tracks change and surfaces only what is currently true.

Agents need a compiled working set

A retrieved subgraph is not the same as an assembled working set. Agents performing multi-step tasks need context that is scoped, coherent, and conflict-free — not a list of fragments to reassemble.

Improvements need to compound across sessions

Write-back lets the system record what the model acted on and what was confirmed. Each session improves the context for the next — no cold-starting from the same static graph every time.

Frequently asked questions

Is Cilow built on top of GraphRAG?▾

No. Cilow includes its own temporal knowledge graph with entity extraction, relationship tracking, and supersession handling. It is not built on top of GraphRAG and does not use Microsoft's GraphRAG library.

Can GraphRAG and a context engine coexist?▾

If you are using GraphRAG for static document retrieval, a context engine adds the lifecycle management layer on top — handling updates, conflicts, and assembly that GraphRAG does not address.

Does Cilow use graph structure?▾

Yes. Cilow includes a temporal knowledge graph for tracking entities, relationships, and how they change over time. This is part of the context assembly pipeline, not a standalone retrieval technique.

Graph structure is one layer. A context engine handles the whole stack.

Learn what a context engine does instead → Architecture