The Context Layer:
Where Your Data Gains Meaning
Your AI agents don't fail because of bad models. They fail because they don't understand your business. The App Orchid Context Layer is the missing infrastructure layer that fixes that for every AI, every team, every query.
Why AI agents fail at enterprise questions
A simple question — "What was revenue growth last quarter?" — breaks because your AI has no idea what "revenue," "last quarter," or "growth" means inside your specific business.
Enterprise data is complex. Every organization has its own definition of ARR, its own fiscal calendar, its own mix of raw tables and materialized views across warehouses. AI models trained on generic data can't know any of this.
A traditional semantic layer helps — it standardizes metric definitions in YAML. But it's static, manually maintained, and limited to BI tools. It can't encode tribal knowledge, doesn't update itself, and was never built for autonomous agents.
The App Orchid Context Layer is a superset of the semantic layer. It encodes not just metric definitions, but governance rules, decision precedents, data lineage, temporal validity, and the institutional knowledge your organization has built over years — all expressed as a living knowledge graph that AI agents can reason over.
One graph to rule your entire data universe
The App Orchid Knowledge Graph ingests every data source and encodes the relationships, definitions, and business logic that give raw data its true enterprise meaning.
Every layer of the enterprise context stack
App Orchid combines six distinct capabilities into one unified context layer — replacing the fragmented tooling most enterprises depend on today.
Patented Knowledge Graph
Map relationships between entities, metrics, tables, and concepts across your entire data estate. The graph encodes not just data, but the meaning behind it.
Semantic SQL Engine
Translate any natural language question into governed, accurate SQL — grounded in your specific metric definitions, not generic web knowledge.
Context Enrichment
Automatically surface tribal knowledge from query history, Slack conversations, and documentation. Turn implicit institutional knowledge into explicit, queryable context.
Governance & Sovereignty
Role-based access controls, full audit trails, and policy enforcement ensure your enterprise data — and its meaning — stays under your control at all times.
Agent-First APIs
Expose your context layer via MCP, REST, or GraphQL. Any AI agent, LLM tool, or analytics platform can consume governed context in real time.
Self-Updating Context
Built-in feedback loops capture agent query failures and surface context gaps. The knowledge graph evolves continuously — without manual re-documentation.
A universal foundation for
AI outcomes
Trustworthy, Consistent and Universal
A unified semantic layer captures relationships and data entities across your datascape in an enriched knowledge graph. Define semantic models once and consume across many use cases.
Designed for LLMs
Store detailed metadata, roles and relationships to get the highest quality answers for all your business questions. App Orchid can understand the user’s role, intent and more to give you personalized and accurate answers.
Capture Your Unique Business Language
Define acronyms and map internal terminology—so your AI understands how your people talk, think, and work.
One Data Model, Many Use Cases
Connect to the universal semantic layer to enable trustworthy AI Agents, Business Intelligence, and Applications. Activate AI data agents that can securely and accurately source data from your enterprise stack.
Data integration
150+ Enterprise
Data Stores
Out-of-the-box connectivity to commonly used enterprise data stores. Automated discovery makes onboarding and context enrichment a cinch.
External Data
and Content
Quicky onboard external data sources, including weather, traffic, crime and more. Utilize LLM knowledge and internet search.
Insights from Unstructured Data,
in your Analytics
Easily onboard and extract data from documents and text, with prebuilt entity extraction and easy to use LLM processing pipelines.
Connect to almost any data source
150+ Enterprise Data stores
Out of the box connectivity to commonly used enterprise data stores. Automated discovery makes onboarding and context enrichment a cinch.