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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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

Benchmarks

Independent benchmark results for Cilow's context engine, measured on LongMemEval with real embeddings and a GPT-4o-mini judge.

94.17%

LongMemEval accuracy
(113/120)

Up to 70%

token reduction vs.
naive context passing

226ms

P50 latency
end-to-end

LongMemEval results

LongMemEval is a benchmark for long-term memory evaluation. It tests a system's ability to recall, reason over, and update information across long conversation histories.

Category	Score
Single-session attribution (SSA)	100% (20/20)
Single-session preference (SSP)	100% (20/20)
Single-session update (SSU)	100% (20/20)
Knowledge update (KU)	90% (18/20)
Multi-session (MS)	90% (18/20)
Temporal reasoning (TR)	85% (17/20)
Overall	94.17% (113/120)

Information extraction: 100% (60/60) — perfect recall across all attribution, preference, and update categories.

Token efficiency

Fewer tokens in the context window = lower cost, lower latency, and less noise for the model to reason over. Flooding a model with loosely relevant text degrades reasoning.

Cilow assembles a minimal working set for each inference call rather than retrieving and dumping all available context. The result is a smaller, higher-signal context window.

Up to 70%

reduction in tokens vs. naive context passing — measured across representative workloads.

→The query planner builds the minimal working set needed for the current call — it does not retrieve all related context and dump it into the prompt.

→Smaller context windows reduce cost per call and lower the cognitive load on the model — both of which improve accuracy.

Latency

Measured end-to-end with OpenAI GPT-4o-mini and real embeddings — not stubs. Includes retrieval, ranking, and context assembly.

226ms

P50 latency

269ms

P95 latency

These numbers cover the full retrieval-to-assembly pipeline. Retrieval, reranking, and working set assembly are all included — not just vector search alone.

Methodology

Numbers are only useful if the methodology is transparent. Here is exactly how this benchmark was run.

Benchmark

LongMemEval stratified 120-question sample (seed=42).

Benchmarks

LongMemEval results

Token efficiency

Latency

Methodology

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