Give AI the context to understand before it acts

Yarnit Contextual Intelligence is the context engineering layer behind our agentic AI systems. It connects enterprise knowledge, data, relationships, memory and real-time signals, and assembles the right context for every question, decision and action.

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Context is more than what retrieval finds

Find what matters

Retrieve the right evidence across enterprise documents, structured data, knowledge systems and external sources—not just the nearest vector match.

Content gets stitched together by hand

Connect entities, relationships and business semantics so AI can understand how products, customers, policies, events and other domain concepts relate.

Assemble what the task needs

Combine knowledge, relationships, memory, task state and live signals into a bounded context package—ranked for relevance, authority, freshness and policy.

CONTEXT ENGINEERING

The right context is engineered, not retrieved.

Turn enterprise information into usable knowledge.

Documents, presentations, spreadsheets, databases, images and enterprise systems carry different kinds of information. Contextual Intelligence preserves their structure, metadata and provenance so they can be represented and retrieved appropriately—not flattened into one generic knowledge base.

Documents & presentations
Structured and semi-structured data
Multimodal content
Enterprise and external sources

Diagram showing data flow from Agents, Developers, and Users into a container labeled with Developers Data, Agents Data, Users Data, and Resource Data.

Choose what you need and when it should go out

Not every question should be answered with the same RAG pipeline. Contextual Intelligence can plan and combine retrieval across semantic search, lexical search, structured data and graph relationships—then fuse and rerank the evidence before it reaches the agent.

Enterprise RAG: Ground agents in proprietary enterprise knowledge.
Agentic RAG: Plan what to retrieve, transform queries, inspect evidence and retrieve again when the task demands it.
Graph RAG: Use entities and relationships for connected and multi-hop questions.
Hybrid Retrieval: Combine dense, sparse, metadata, structured and federated retrieval.
Reranking: Separate candidate retrieval from semantic and business-aware ranking.

Illustration of a document icon labeled 'Doc.x' surrounded by four circular icons with abstract symbols, all on a light background with browser window controls visible at the top left.

Understand relationships, not just documents.

Context Graph connects the entities, relationships, business semantics and changing state that define a domain. It gives agents a structured understanding of how the world they are operating in fits together—and gives retrieval a richer path to context.

Commerce Products · Categories · Attributes · Customers · Inventory · Offers · Campaigns
Life Sciences HCPs · Institutions · Evidence · Therapies · Congresses · Interactions
Manufacturing Products · Components · Failures · Diagnostics · Tests · Requirements

Browser window mockup showing a process with 'Searched' for 'Stripe founders' and 'Read' items including Reteropiea, Jhumpy, and Default media, with options for Competitors and Q1 Web Traffic.

Carry useful context across time.

Some context belongs to this task. Some should persist. Memora gives Yarnit agents governed memory across interactions and workflows—so relevant decisions, knowledge, preferences and prior context can carry forward without treating every conversation as a blank slate.

Working | Episodic | Semantic | Procedural | Preference

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Give the agent what it needs. Not everything you know.

Contextual Intelligence selects and assembles the final context around the task—combining instructions, workflow state, retrieved evidence, graph relationships, memory, tool results and business rules. Context is ranked, deduplicated, reconciled and compacted before it reaches the model.

Relevance | Authority | Freshness | Provenance | Policy | Context budget

INSIDE CONTEXTUAL INTELLIGENCE

Built for context that goes beyond RAG

Agentic RAG

Agents can plan retrieval, transform queries, inspect evidence and retrieve again as the task evolves.

Graph RAG

Combine semantic retrieval with entities, relationships and graph traversal to answer questions that depend on connected, multi-hop context.

Hybrid Retrieval & Reranking

Combine vector, lexical, metadata, structured and graph retrieval—then fuse and rerank candidates before context assembly.

Context Graph

A domain context model connecting enterprise entities, relationships, semantics and changing state.

Memora

Governed memory for context that needs to persist across interactions, users and workflows.

Context Engineering Tools

Reusable tools for source ingestion, query transformation, entity resolution, retrieval, graph traversal, fusion, reranking, provenance and context assembly.

BUILT FOR REAL WORK

Built for context that goes beyond RAG

Bring the right data, knowledge, relationships and signals into context.So AI can understand what matters and act with greater relevance.

AI for Commerce

See how Yarnit connects product data, attributes, customer intent, inventory, business signals and memory to power product intelligence, discovery, recommendations and conversational selling.

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Life Sciences

See how scientific evidence, HCP relationships, enterprise data and domain context come together to support governed scientific and commercial intelligence.

Source

Forward Deployed AI

Contextual Intelligence gives Forward Deployed AI systems a grounded understanding of proprietary data, knowledge, relationships and workflows.

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Give Your Enterprise AI the Context to Make Better Decisions

Start free, or let us walk you through it with your own brand loaded in.

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