Engineer / Semantic Core
Impetus
Context Fabric™
AI agents are only as smart as the context they’re given. Context Fabric closes the Context Gap between your data and your AI.
Most enterprises sit on enormous amounts of data — but without semantic structure, business context, and real-time enrichment, that data remains invisible to AI. Context Fabric is Impetus’ Context Engineering platform. It automatically extracts meaning from your raw data assets, builds enterprise knowledge graphs and semantic layers, and serves structured context directly to AI agents and LLMs at inference time — so every AI interaction is grounded, accurate, and auditable.
Context Fabric is the enterprise’s semantic nervous system — it doesn’t just store what you know, it understands how everything connects.”
The Problem
AI models hallucinate because they lack context. Your data has the answers — but without semantics, your AI can’t use them.
You’ve invested in an LLM. You’ve built a vector database. But your AI still gets things wrong — because it doesn’t understand the meaning behind your data.
The root cause isn’t the model. It’s the missing context layer. Legacy data sits in silos. Business definitions live in spreadsheets. There is no shared semantic fabric connecting what data means to how AI can use it. This is the Context Gap — and it makes every AI initiative more expensive, slower, and less reliable than it should be.
Context Fabric solves this at the infrastructure level — automatically extracting, structuring, and serving the contextual intelligence your AI agents need to operate reliably. This is Context Engineering: the discipline Impetus pioneered.
What Context Fabric Does
Key capabilities
Missing semantic context means your AI can’t reason with your data effectively. Context Fabric eliminates the gap, ensuring your agents know how your business actually operates in the real world.
MCP-First Context Serving
Orchestrates the full context lifecycle — securing, governing, and routing it to agents with sub-250 millisecond MCP latency. Monitors agentic outcomes and improves context quality.
Hybrid Knowledge Graph & Vector RAG
Enables teams to define what data means for AI through business terms, domain taxonomies, entity relationships, etc. Prevents confusion and eliminates hallucination at the source.
Semantic Core & Industry Ontologies
Provides curated, validated domain knowledge that helps AI agents understand contracts, industry ontologies, regulatory frameworks, etc. Transforms generic AI into domain-expert AI.
Agentic Context Engineering (ACE)
Converts raw enterprise data into a governed semantic layer and delivers structured context to agents and LLMs in real time—so AI outputs stay accurate, relevant, and auditable.
Impetus
Context Fabric™
Product Family
Engineer
The low-code/no-code workspace where Data Architects ‘brief’ the AI — designing its information environment to prevent context confusion and eliminate hallucinations at the source.
Context Studio gives data engineers and business users a shared visual workbench to define what data means — business terms, metric definitions, domain taxonomies, entity relationships, and data product ontologies — and map those definitions onto your actual data assets. It configures how AI agents write, select, and compress memory, designs layered retrieval logic, and enforces Responsible AI policies through compartmentalized isolation. Changes automatically propagate to all downstream AI consumers, LLMs, and agent frameworks.
Core Capabilities
Visual Ontology Builder
A drag-and-drop interface for designing knowledge graphs and semantic models that prevent context confusion
Business Glossary Manager
Collaborative definition workflows for business terms, KPI definitions, and data domain standards with approval workflows and version history
Automated Semantic Mapping
AI-assisted mapping of business definitions to physical data assets across all platforms
Context Propagation
Semantic changes automatically propagate to Knowledge Context and Context Fiber
Memory Management (Write / Select / Compress)
Configures agents to write into long-term scratchpads, persisting knowledge across multi-day tasks
Layered Retrieval Design
Designs select logic combining Embeddings + Keyword + Re-ranking for precision retrieval
Context Editing & Context Budget
Applies automated rules to compress or prune stale information, maintaining a strict context budget per agent
Isolation Studio
Compartmentalized workflows that prevent context clash between sub-agents; enforces PII tagging, toxicity filters, and access boundaries
User Personas
Business Analysts
Ontology curation & semantic enrichment
Compliance Officers
Policy authoring & audit trails
Data Engineer
Ingestion configuration & lineage tracking
AI Agent Developers
MCP integration & context tuning
Know
The domain-specific intelligence layer — encompassing contracts, industry-standard ontologies, regulatory frameworks, and business reference models that give AI agents the specialized understanding required for vertical use cases.
Knowledge Context is the content layer of Context Fabric. Where Context Fiber handles infrastructure and Context Studio handles design. Knowledge Context provides the curated, validated, enterprise-grade domain knowledge that separates general-purpose AI from domain-expert AI. It sets up the enterprise catalog as a knowledge base for Context Engineering — enabling Agentic AI, AI Analytics, and GenBI across your data ecosystem. Pre-built ontology packages for Finance and Healthcare are live; Cross-industry and Manufacturing will be available soon.
Core Capabilities
Pre-built Financial Services Ontology Packages
FIBO, FIB-DM, ACORD, BAIN, FSDM, FINOS [LIVE]
Healthcare Ontologies
HIPAA, SNOMED, OCRe for clinical and payer use cases [LIVE]
Cross-Industry Standards
Microsoft CDM, ISO 20022, OSI semantic interchange [UPCOMING]
Manufacturing
Custom domain product templates and ontology models for industrial verticals [UPCOMING]
Contract Intelligence
Extraction, indexing, and semantic search over enterprise contracts
Business Rules Library
Codified ways of working, compliance constraints, and SOPs
Semantic model publishing via OSI for GenBI (Kyvos, Power BI, Looker)
Ontology comparison and benchmarking against industry standards
GraphRAG with Ontology
90% hallucination reduction via knowledge graph reasoning
Domain Data Product Templates
Pre-configured for Finance, Healthcare, and Manufacturing
Responsible AI Governance Store
Entitlements, compliance constraints, and audit provenance
User Personas
Finance
FIBO · FIB-DM · ACORD · BAIN · FSDM · FINOS
Healthcare
HIPAA · SNOMED · OCRe · HEDIS
Cross-Industry
Microsoft CDM · ISO 20022 · OSI Semantic Interchange for GenBI use cases
Manufacturing
Custom domain product templates and ontology models for industrial verticals
Serve
The MCP-first serving layer that orchestrates the full context lifecycle — from multimodal ingestion and semantic transformation to real-time knowledge base exposure via MCP servers for CE-driven Agentic AI, at ≤250ms p95 latency.
Context Fiber is the enterprise’s semantic nervous system — ingesting structured and unstructured data, enriching it with ontologies and business meaning from Knowledge Context, governing it with privacy and compliance policies, and routing it dynamically to any agent that needs it through the open MCP (Model Context Protocol) standard. The ACE (Agentic Context Engine) monitors agent outcomes and feeds accuracy signals back to continuously improve context quality. Federated Memory with MVCC prevents context conflicts across distributed agent deployments.
Core Capabilities
Legacy Context Liberation via LeapLogic
Metadata, business rules, lineage, entity relationships
Multimodal Ingestion
PDFs, DOCX, HTML, codebases, wikis, JIRA, APIs, databases, and events (5M+ pages/day)
Document Intelligence Extractor
OCR, layout-aware extraction, table parsing, image context
Semantic Core
Auto-generate OWL/RDF ontologies from schemas, map to FIBO, HIPAA, and CDM standards
Knowledge Stores
hybrid GraphRAG + Vector RAG (Milvus HNSW + IVF-PQ dual indexing, Neo4j graph)
Dynamic MCP Context Routing
Just-in-time token loading to prevent context poisoning, ≤250ms p95
Agentic Context Engine (ACE)
Feedback loops with outcome tracking for +10.6% accuracy gains
Federated Memory
Multi-agent shared memory with MVCC for coordinated agent workflows
Autonomous Ontology Management
Self-evolving ontologies with contradiction detection
DP-RAG for Sensitive Data
Differential privacy with epsilon-delta budgets
Universal Context Adapter (UCA) supporting 10 platforms
AWS Bedrock, Databricks AgentBricks, GCP Vertex AI, Azure AI Foundry, Snowflake Cortex, LangGraph/CrewAI
Customer-Owned Context Graph
Open, federated context storage to prevent vendor lock-in
Key Metrics
Client Results That Count
Is your AI grounded in the right context?
In a 2-hour Context Discovery Session, our engineers will map your data assets, identify your semantic gaps, and show you exactly what your AI is missing. No commitment required.