SEMARA: Enterprise Ontology and Knowledge Graph for AI

Power of Agentic AI Accelerator for Ontology & Knowledge Graph

SEMARA is Tech Mahindra’s accelerator that automates the creation of enterprise ontologies and knowledge graphs, the foundation every trusted agentic AI needs. Its built-in AI agents extract entities, relationships, hierarchies, axioms, and canonical models from structured and unstructured datasets, metadata, SOPs, BPMN, and business rules.

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SEMARA is Tech Mahindra’s accelerator that automates the creation of enterprise ontologies and knowledge graphs, the foundation every trusted agentic AI needs. Its built-in AI agents extract entities, relationships, hierarchies, axioms, and canonical models from structured and unstructured datasets, metadata, SOPs, BPMN, and business rules.

Built on a technology-agnostic stack, SEMARA delivers a live, governed knowledge graph use case in 4–8 weeks. SEMARA brings human-in-the-loop (HITL) governance to every agent output, supported by observability and agent evaluations with MLflow. This ensures that while AI agents accelerate ontology creation, humans retain full oversight, control, and governance.

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Solution Highlights

  • Automated Extraction and Canonical Model Generation: Parses structured and unstructured enterprise assets like tables, process documents, metadata catalogs, workflow diagrams, and reporting logic to automatically extract entities, relationships, and class hierarchies. It generates standards-compliant semantic models ready for any graph database.
  • Multi-Source Ingestion Engine: Connects to diverse enterprise data sources like databases, document repositories, ERPs, policy stores, and cloud platforms, creating a unified semantic foundation that bridges organizational silos and eliminates fragmented knowledge layers.
  • Agentic Build Workbench with Human-in-the-Loop: AI-orchestrated agents automate schema extraction, semantic mapping, and graph binding, while an integrated human-in-the-loop (HITL) workbench empowers SMEs to validate and govern outputs at every stage. This reduces ontology engineering effort by 50–70% while maintaining complete human oversight and governance.
  • Enterprise-Grade Governance and Security: Every artifact, including ontologies, knowledge graphs, agents, and prompt versions, is governed through role-based access controls, secure authentication, observability, evaluations, and full lineage tracking. Purpose-built for regulated enterprises, the platform delivers governance, traceability, and compliance from day one.
  • Open Outputs and Flexible Deployment: Produces industry-standard semantic artifacts and interactive visualizations, with seamless deployment to leading graph databases. With no vendor lock-in, semantic assets remain fully portable, interoperable, and reusable across platforms.
  • Observability and Explainable AI: Every agent action is monitored and evaluated in real time, with continuous tracking of performance, cost, and latency. Built-in reasoning trails, evidence lineage, and audit-ready insights provide transparency and trust, enabling confident decision-making and accelerating enterprise adoption.

Solution Benefits

  • First knowledge graph goes live in 4–8 weeks, not 12–18 months
  • Automates 50–70% of ontology effort; SMEs validate rather than model
  • Pay-per-use-case scaling avoids high upfront costs
  • 85% fewer hallucinations and 92%+ multi-hop query accuracy
  • 90% more policy-grounded responses with full explainability and lineage
  • 65% reduction in analyst decision time and 2–3X higher business adoption and trust

Industry Use Cases

Telecom - Fraud and Network Ontology

Built a subscriber-device-CDR-network ontology and knowledge graph that powers downstream fraud detection, SIM-cloning investigation, and network root cause analysis (RCA) agents, reducing months of SME workshops to weeks through automated extraction.

BFSI - Credit and AML Knowledge Graph

Constructed a customer-account-transaction-policy ontology that grounds credit decisioning, KYC, and AML investigation agents in governed truth, delivering the explainable foundation regulators expect.

Healthcare - FWA and Claims Ontology

Assembled a provider-claim-procedure-policy knowledge graph that powers fraud, waste, and abuse detection agents alongside clinical decision support, with provenance and HIPAA-aligned governance baked in.

Retail and CPG - Customer 360 Graph

Unified product, customer, inventory, and promotion data into a connected ontology and knowledge graph that powers personalization, assortment optimization, and service-resolution agents with full cross-channel context.

Manufacturing and Auto - Operations Ontology

Implemented a semantic layer that binds asset hierarchies, BOMs, SOPs, and sensor data into a unified foundation for predictive maintenance, quality RCA, and supply-chain orchestration agents that reason across plants and partners.

Why SEMARA?

Breaks the Ontology Paradox

73% of enterprises cite cost as the primary barrier. SMEs estimate 6–12 months to build ontologies, while business leaders expect value realization to take 12–18 months. SEMARA compresses all three challenges through agentic automation, delivering a live ontology and knowledge graph in weeks.

Ontology is the Moat

Foundation models are a commodity. Enterprise knowledge is the moat. SEMARA transforms that knowledge into a governed, machine-readable asset that your organization owns, controls, and reuses across every AI initiative.

Beyond RAG Limitations

When RAG struggles with complex relationships, deterministic rules, and semantic drift, SEMARA delivers governed knowledge graphs that enable agents to reason through explainable, traceable decision paths.

Composable Semantic Stack

Ontologies provide structure, knowledge graphs provide context, and agents provide intelligence. SEMARA assembles all three into a governed foundation, making AI enterprise-ready from day one.

Use-Case-First, Scale-Fast

Pick one domain. Pick three workflows. Deliver value in weeks. The knowledge graph compounds with every sprint, creating reusable intelligence across use cases. Modular, pay-per-use-case adoption means no major investment risks.

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