A context engine for AI agents. It replaces vector DBs, search pipelines, and RAG glue with one system that ingests data, structures it, keeps it current, and serves the right context at inference time.

RAG retrieves similar fragments. It can't decide what's current, what conflicts, or what actually belongs in the working set. Cilow handles all three.

How is Cilow different from a vector DB or GraphRAG?

Vector DBs return similarity. GraphRAG adds relationships. Cilow goes further: ingestion, structuring, updating, conflict resolution, and context assembly in one layer. The goal isn't more data. It's the right data.

What data can Cilow use?

Documents, chats, code, APIs, product data, tickets, notes, structured records. If your agent depends on it, Cilow turns it into context.

Does it support continual learning?

Yes. Cilow updates context without retraining. As facts change, what the model sees changes - no weight updates, no brittle prompt hacks.

How does it fit into my stack?

Cilow replaces your retrieval and context layer. One API in front of your models - no separate vector DB, search service, or RAG pipeline to maintain.

Pricing | Cilow

Start free

Trialfor 14 days

Try every feature, no card required.

5,000 searches
1,000 memory writes
50 fast answers
25,000 active memories

PAYG/month

Pay only for what you use.

Standard usage rates
All features unlocked
Scale up or down freely
Upgrade anytime

Monthly plans

Pro/month

$29

Solo builders and dev projects.

20,000 searches
10,000 memory writes
500 fast answers
100,000 active memories

Recommended

Team/month

$99

Small teams shipping real agents.

100,000 searches
50,000 memory writes
2,500 fast answers
500,000 active memories

Scale/month

$399

Production agents at volume.

1M searches
500,000 memory writes
10,000 fast answers
2M active memories

Need volume, on-prem, or a custom contract?

Talk to the founders. We’ll respond in under 24 hours.