Open-source AI infrastructure

The context layer
for your next AI application.

Connect your data. Retrieve what matters. Build RAG systems, agents, and knowledge assistants on one foundation.

Explore the platform Find your deployment
Workspace / Product knowledge
Context workspace

From your sources to useful context

A unified foundation for your AI application.

RETRIEVAL / CONTEXT
Connected sources
Product documentation
Documents · knowledge
Indexed
Application codebase
Code · engineering
Indexed
Internal service API
API · application data
Indexed
Knowledge database
Database · structured data
Indexed
Process Embed Retrieve
Context preview ···
How does the application use its knowledge sources?
RETRIEVED CONTEXT
Sources are processed and indexed so the application can find relevant information. 1

Retrieved passages provide context for the model, with references to the original sources. 2
Documentation · Codebase
One workflow. From connected knowledge to source-aware responses. ILLUSTRATIVE WORKSPACE
RAG systems AI agents Knowledge assistants Context-aware applications
01 / The foundation

Build your product. Start with the context layer. Cognetion brings the infrastructure behind AI applications together, so you don’t have to rebuild it for every project.

FIG 01 / FOUNDATION

One reusable foundation

Ingestion, processing, embeddings, search, and retrieval. A unified layer beneath the applications you build.

FIG 02 / CONNECTION

Your knowledge, connected

Bring documents, codebases, APIs, databases, and other sources into the same development workflow.

FIG 03 / CONTEXT

Context that stays connected

Retrieve relevant information for your models and keep responses connected to their original sources.

02 / From data to context

Connect the knowledge.
Give your AI context.

Turn scattered source material into information your application can use. Cognetion handles the infrastructure from ingestion to retrieval and context management.

Explore the workflow
The context pipeline SOURCE → APPLICATION
Ingest
Process
Embed & index
Retrieve
Context
SEMANTIC SEARCH QUERY + KNOWLEDGE
query RELEVANT CONTEXT
Source-aware context

Information your model can work with.

Relevant passages are retrieved from your indexed knowledge and brought into the application’s context. 1

Source references keep the response connected to the material behind it. 2

1 · Product documentation
2 · Application codebase
01 Connected sources
02 Relevant information
03 Source-aware responses
Conceptual pipeline and semantic retrieval illustration. DATA / RETRIEVAL / CONTEXT

Prepare your knowledge

Ingest source data, process documents, and create embeddings for indexing and semantic search.

Retrieve what matters

Search indexed information by meaning and retrieve relevant material for your AI models.

Build with context

Use retrieved knowledge and context management to support agents, assistants, and your own AI products.

03 / Your infrastructure, your choice

Open at the core.
Flexible by design.

Run and customize Cognetion on your infrastructure, or use the hosted version for a managed experience. Choose how you want to build.

Self-hosted DEPLOYMENT / 01
YOUR INFRASTRUCTURE

Make the foundation your own.

Run the open-source core on your own infrastructure. Customize the platform around the AI products you’re building.

  • Self-hostable open-source core
  • Your deployment and infrastructure
  • A foundation you can customize
Hosted DEPLOYMENT / 02
MANAGED FOUNDATION

Focus on the application.

Use Cognetion through a hosted, managed experience, without running the underlying platform infrastructure yourself.

  • Managed deployment and storage
  • Managed indexing infrastructure
  • The same focus on AI development
Cognetion is an early-stage project, built around an open-source core and a hosted managed experience.
Build on Cognetion

Your next AI application.
A foundation to build on.

Bring your knowledge and your ideas. Start with the infrastructure behind them.

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