What Is Context AI?
Context AI - also called contextual AI - describes AI systems designed to understand and use surrounding information rather than processing inputs in isolation. The term covers both a general capability (any AI that adapts to context) and a specific company (Contextual AI, co-founded in 2023). In enterprise settings, context AI determines whether an AI agent can answer "What is our churn rate?" with a grounded, role-appropriate response or a generic hallucination.
The difference is practical. A system without context processes each input in isolation, so the same query gets the same interpretation whoever asks it. Context AI considers who is asking, what they asked before, what domain they work in, and what the underlying data actually means. For how this plays out with specific examples, see context understanding in AI.
Context AI refers to AI systems that incorporate surrounding information - conversation history, user identity, domain knowledge, metadata - into their processing. Unlike systems that process each input in isolation, context AI adapts responses based on who is asking, when, and why. For enterprises, the practical difference is whether AI tools produce trustworthy, relevant answers or generic outputs that teams ignore.
What Makes AI Contextual
Four capabilities define context AI and separate it from static systems.
Situational awareness. Context AI considers who, when, where, and why. A sales VP asking "How are we doing?" gets a pipeline summary. A support manager asking the same question gets ticket resolution metrics. The system reads the user's role, department, and recent activity to determine what "doing" means in each case.
Adaptive responses. The same query produces different outputs based on context. This goes beyond recommendation-style personalization, because the system has to interpret the words themselves. "Revenue" means ARR to the finance team and bookings to the sales team. Context AI resolves this ambiguity using business glossary definitions and user context.
Memory. Context AI retains relevant history across interactions. When a data analyst asks "Show me customer churn," then follows up with "Break it down by plan tier," the system remembers the churn metric, the time range, and the customer segment from the first query. Without memory, each question starts from zero.
Multi-signal reasoning. Context AI combines linguistic, temporal, domain, and user context simultaneously to produce a single response. Using only one signal - like keyword matching - is not context AI. The power comes from integrating multiple context dimensions at inference time.
How Context AI Works
The architecture of context AI has three stages: context extraction, context representation, and context integration.
Context extraction identifies which signals matter for the current query. This includes parsing the user's role from their session, identifying entities in their question, and pulling conversation history. Not all available context is relevant - extraction filters for what the model actually needs.
Context representation encodes extracted context in formats the AI can consume. This might be structured metadata from a data catalog (table descriptions, column definitions, ownership), vector embeddings from a knowledge base, or nodes in a knowledge graph. The semantic layer plays a role here - it maps business concepts to their physical data representations.
Context integration delivers this context to the model at inference time. The three main mechanisms are retrieval-augmented generation (RAG), which retrieves relevant documents and metadata before the model generates a response; tool calls, which let the model query external systems mid-generation; and system prompts, which pre-load organizational rules and definitions.
In the 2020 paper that introduced retrieval-augmented generation, RAG models set the state of the art on three open-domain question-answering tasks and generated more specific, diverse and factual language than a parametric-only baseline. How much of that carries into production depends on the quality of what the retriever finds.
- Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, NeurIPS 2020
Context AI vs. Traditional AI
The table below highlights the practical differences across five dimensions:
| Dimension | Traditional AI | Context AI |
|---|---|---|
| Input handling | Each query processed in isolation | Query interpreted with surrounding context |
| Response consistency | Identical output for identical input | Output adapts based on user and situation |
| Ambiguity handling | Guesses or asks for clarification | Disambiguates using context signals |
| Personalization | None - same result for every user | Role-aware, history-aware, domain-aware |
| Enterprise readiness | Requires heavy prompt engineering | Governed context pipeline provides grounding |
Context AI in Enterprise Applications
Three patterns show where context AI delivers the most value in enterprise settings.
Contextual search in data catalogs. A data catalog powered by context AI ranks search results by the user's role, department, and recent queries - not just keyword relevance. A data engineer searching "customer" sees raw tables and ETL pipelines. A product manager sees dashboards and KPI definitions. Same query, different context, different results.
AI copilots in BI tools. A context-aware copilot maintains conversation context across multi-step analysis. When a user asks "Show me churn by segment" and follows up with "Which segment grew most?", the copilot knows "grew" refers to churn increase, not revenue growth, because it retains the analytical context from the prior query.
Automated data quality classification. Context AI classifies anomalies using historical and seasonal context rather than static thresholds. A 50% drop in weekend transactions is normal for a B2B SaaS company. A 50% drop on a Tuesday is not. Context AI learns these patterns from history and classifies alerts accordingly, reducing false positives that erode trust in monitoring systems.
Contextual AI: The Company
Contextual AI is also a specific company, co-founded in 2023 by Douwe Kiela and Amanpreet Singh; the company launched from stealth in June 2023. The founders had worked together at Facebook AI Research (Meta) and Hugging Face. Kiela is a co-author of the 2020 paper that introduced RAG, and the company's Series A announcement (August 2024) calls him one of the pioneers of RAG. Its core thesis: enterprise AI needs context grounding to be reliable, and general-purpose models trained on public text do not provide it on their own.
The company describes itself as building "the context layer for enterprise AI", a platform for building AI agents and applications that work securely on an organization's own data. Its RAG 2.0 approach (March 2024) trains the retriever and the language model together as one system instead of stitching separately built components. In May 2026, Bloomberg Law reported that Google DeepMind had licensed Contextual AI's technology and hired part of its team, including Kiela.
Context AI with Dawiso
Dawiso functions as the context layer for enterprise AI. The data catalog, business glossary, and lineage graph provide the structured context that AI agents need to produce grounded responses.
Through MCP, any MCP-compatible agent can query Dawiso programmatically. The Dawiso MCP server lets an agent search the catalog, read object details, trace upstream and downstream lineage and preview data samples; write tools are available only to user roles that permit them, and the server is currently available for cloud customers. When an AI copilot encounters the question "What is our customer lifetime value?", it can look up the indicator in the business glossary, which records a definition, a calculation and an owner and steward, and follow lineage to the data behind it. The model can then answer from the organization's agreed definition instead of a generic formula.
Maintaining one governed context source that several AI tools query is easier to keep consistent than copying definitions into each tool separately, which is how context AI scales from one use case to many.
Sources
- Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, arXiv, May 2020 (NeurIPS 2020).
- Contextual AI, Company.
- Contextual AI, Introducing RAG 2.0, March 19, 2024.
- Contextual AI, Contextual AI Raises $80M Series A to Scale Production-Grade LLMs for Enterprises, August 1, 2024.
- Business Wire, Contextual AI Emerges From Stealth to Build the Next Generation of Language Models, for the Enterprise, June 7, 2023.
- Bloomberg Law, Google Hires Staff From AI Startup Contextual In Licensing Deal, May 19, 2026.
- Dawiso Help, Dawiso Model Context Protocol (MCP).
- Dawiso Help, Business Glossary attributes and components.
See it in action
MCP (Model Context Protocol)
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