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 If your product breaks when context gets messy, fragmented, or outdated, Cilow is for you.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

Context rot [noun]: The gradual degradation of an AI system's useful context as stored information becomes stale, contradictory, or irrelevant — causing compounding errors, hallucination, and loss of coherence over time.

How context rot happens

Information changes but the index doesn't

Data sources update, but retrieval indexes aren't refreshed automatically. The model keeps seeing the old version.

Conflicting facts accumulate without resolution

New facts are added alongside old ones, not replacing them. Over time the index fills with contradictions the model must guess between.

Preferences and decisions get buried under noise

Older, less-used context sinks while irrelevant noise rises. The signal the model needs is still there — just unreachable.

Each new session adds data without retiring old data

Indexes grow without pruning, degrading signal quality. Volume increases while precision falls.

Signs your system has context rot

×Agents citing outdated facts

×Contradictory answers to the same question over time

×Model "forgetting" previous decisions

×Increasing hallucination rate as data volume grows

Why RAG doesn't solve it (and often makes it worse)

×More data in the index means more stale data retrieved. Volume amplifies noise, not signal.

×No native mechanism for supersession or retirement. Retrieval has no concept of “this fact replaced that one.”

How to prevent context rot

Capture provenance

Track when information was written and what source it came from. Provenance is the precondition for everything else — without it, you cannot score recency or detect conflicts.

Track supersession

Newer facts should retire older ones, not coexist with them. Every write should carry a signal about what it updates or invalidates.

Score by recency

Not just semantic similarity. A fact written yesterday about the current state of a system should outrank a more similar document from two years ago.

Write outcomes back

Close the loop on what the model acted on. When an agent acts on a fact, that action is itself a new data point — record it and let the system learn from it.

Frequently asked questions

Is context rot the same as hallucination?▾

Context rot is a cause of hallucination, not the same thing. When the context a model sees is stale, contradictory, or irrelevant, the model fills in gaps — which produces hallucinations. Fixing the context reduces the hallucination rate.

Does context rot only affect RAG systems?▾

No. Any AI system that accumulates information over time — including vector databases, chat histories, and knowledge bases — can develop context rot if it lacks mechanisms for supersession, conflict resolution, and retirement.

How does Cilow prevent context rot?▾

Cilow tracks provenance, scores by recency alongside relevance, flags superseded information, and writes outcomes back into the context layer — so the system improves with use instead of degrading.

See how Cilow tracks provenance, scores by recency, and retires stale context automatically.

See how Cilow handles it → Architecture