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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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

How Cilow works

Cilow is built around a tri-store architecture: a vector index for semantic similarity, a temporal knowledge graph for relationships and timeline, and a structured key-value store for fast lookup. A unified query planner across all three assembles the minimal context a model needs for any inference call.

Write path: Data sources → Ingest → Extract → Structure → Index ↓ Query path: Intent → Multi-lane retrieval → Fusion → Assembly → Model ↑ Memory: Reinforcement / Decay / Supersession

Three layers of context

Write path

Ingest

Accepts documents, chats, code, APIs, and structured data — any source, any format.

Extract

Entity recognition, relationship extraction, and timeline anchoring from raw input.

Structure

Builds the knowledge graph, indexes vectors, and updates the key-value store.

Index

All three stores updated atomically — vector, graph, and structured in one pass.

Query path

Intent classification

Routes the query to the right retrieval lanes based on what kind of answer is needed.

Multi-lane retrieval

Vector, graph, and structured retrieval run in parallel — each contributing different signal.

Fusion

N-way result fusion surfaces the best candidates from across all three lanes.

Assembly

Builds the minimal working set for the current inference call — nothing more.

Memory lifecycle

Reinforcement

Context that proves useful gets stronger signal — the system learns what helps.

Decay

Context that is never retrieved or acted on fades over time rather than accumulating as noise.

Supersession

Newer, more specific facts retire older ones — no contradictions accumulate silently.

The storage layer

Vector index

Semantic similarity search using high-dimensional embeddings. Retrieves what is conceptually close to the query.

Temporal knowledge graph

Entities, relationships, and how they change over time — the structural layer that RAG does not have. Tracks when facts held true and when they were superseded.

Structured key-value store

Fast exact lookup, scalar filters, and secondary indexes. The right tool when you need precision over similarity.

Why three stores and not one: each store handles a different kind of retrieval need. Combining them with a query planner gives better results than any single store alone — because semantic similarity, structural relationships, and exact lookup are three distinct problems.

How Cilow scores context

Cilow ranks candidates across multiple signals before assembly. No single signal dominates — the system combines them to surface what is genuinely useful for the current call.

Recency

More recent information scores higher by default. Information that was true last week ranks above information that was true two years ago.

Relevance

Semantic match to the current query — the signal vector search specializes in.

Frequency

Context used often by the model gets reinforced. Repeated utility is a strong signal of lasting value.

Causal role

Information that led to a correct outcome is weighted up. The system tracks which facts contributed to good answers.

Past utility

The system learns over time what kinds of context actually helped, and adjusts scoring accordingly.

Context assembly

→The query planner builds the working set from candidates across all three stores — vector, graph, and structured retrieval contribute in parallel.

→Assembly is constrained: the goal is the minimal set that answers the question, not the maximal set. Everything that does not contribute is excluded.

→Smaller working sets mean lower latency, lower cost, and less noise for the model to reason through.

Benchmarks

In LongMemEval testing, Cilow achieved 94.17% accuracy (113/120) — see the full results.

→ Benchmarks

Ready to build on top of Cilow's tri-store architecture?

Start building → Docs