AI Native Technology

Enterprise Intelligence Fabric, Data & Ontology

One governed layer of enterprise truth across data and knowledge.

A governed intelligence layer for the enterprise.

Summarising a document needs only a model. Enterprise intelligence begins when the system can establish that an invoice belongs to a specific contract, which is an ontology problem before it is a model problem. The fabric connects data, knowledge, processes, relationships and decisions into one governed layer that every model and agent reasons over.

That layer is engineered, not assembled. The enterprise ontology defines what things mean; knowledge graphs hold how they relate; semantic and context layers present them to models in governed form; decision intelligence closes the loop from insight to action. It is the most differentiated part of the architecture, and every other domain depends on it.

A cable of blue fibres studded with points of light

Core capabilities

Engineering capabilities delivered end to end by a single accountable team

Enterprise ontology

One definition of what a customer, a contract or an invoice means, shared by people, systems and models.

Knowledge graphs

Entities and relationships held as a graph the enterprise and its agents can traverse.

Enterprise knowledge architecture

How documents, policies, processes and expertise are captured, governed and served.

Data intelligence

Metadata, lineage, quality and classification that make data trustworthy at the point of use.

Data fabric

Governed access to data wherever it lives, without moving everything first.

Knowledge fabric

Unstructured knowledge connected to structured data under one governance model.

Semantic layers

Business meaning defined once and presented consistently to analytics, applications and models.

Master data intelligence

Master data reconciled and governed as the reference for the ontology.

Context engineering

The retrieval, ranking and assembly of enterprise context that a model receives for a task.

Decision intelligence

From evidence to decision to action, with the reasoning and the outcome recorded.

AI-ready data architecture

Data engineered for models to reason over, not only for reports to read.

iFortis Worldwide®

Where it sits in the architecture

This domain engineers Enterprise Intelligence Fabric + Ontology and Data + Integration + APIs within the enterprise architecture.

16:19  Mon 31 Aug
DoneWhere it sits in the architecture
CONTROL PLANESECURITY + GOVERNANCE + OBSERVABILITYIDENTITY · POLICY · MODEL RISK · AUDIT EVIDENCECONTROL PLANECLOUD + GPU + INFRASTRUCTURECOMPUTE · GPU · STORAGE · HYBRID AND MULTI-CLOUDINFRASTRUCTUREDATAPLATFORMS · LAKEHOUSEINTEGRATION + APISEVENT STREAMS · SYSTEMS OF RECORDDATA & INTEGRATIONAPPLICATIONSINTELLIGENT APPSMODELSEVALUATED · SERVEDAGENTSAGENTIC SDLCAPPLICATIONSRECORDSAGENTSPEOPLEPROCESSESPOLICYTIMEENTERPRISE INTELLIGENCE FABRIC + ONTOLOGYENTERPRISE GRAPH · ONTOLOGY · CONTEXT ENGINEINTELLIGENCE FABRICAI DIGITAL WORKFORCE + AGENTIC OPERATIONSDIGITAL WORKERS · AGENTS · AUTHORITY LIMITSDIGITAL WORKFORCEBUSINESS & OPERATING MODELDECISION RIGHTS · FUNDING · OUTCOMESBUSINESSAGENTRole definedAuthority limit setNamed human ownerGOVERNEDDECISIONPolicy checkedEvidence loggedReversibleTRACEABLEAI NATIVE TECHNOLOGY · ENTERPRISE ARCHITECTURE · SCALE 1:1
Business & Operating ModelAI Digital Workforce + Agentic OperationsEnterprise Intelligence Fabric + OntologyAI Applications + Models + AgentsData + Integration + APIsCloud + GPU + InfrastructureSecurity + Governance + Observability

How we engineer

A connected engineering model that takes growth from discovery to continuous adaptation.

  1. 01Discover
    • Enterprise landscape
    • Technology estate
    • Data and intelligence gaps
    • Opportunity map
  2. 02Architect
    • Target architecture
    • AI, data and cloud design
    • Integration model
    • Control plane
  3. 03Engineer
    • Products and platforms
    • Agents and models
    • APIs and pipelines
    • Infrastructure
  4. 04Industrialise
    • Evaluation harnesses
    • Agentic SDLC
    • MLOps and LLMOps
    • Repeatable patterns
  5. 05Deploy
    • Workflow activation
    • Digital workforce
    • Change and adoption
    • Release governance
  6. 06Operate
    • Service levels
    • Observability
    • Cost and FinOps
    • Incident and oversight
  7. 07Continuously adapt
    • Model and agent updates
    • Architecture evolution
    • Outcome measurement
    • Compounding value

Engineering stack

The disciplines this domain draws on most, from the complete engineering stack

Databases: Relational

  • PostgreSQL
  • MySQL
  • MariaDB
  • Oracle Database
  • Microsoft SQL Server
  • IBM Db2
  • SQLite
  • CockroachDB
  • Aurora

Databases: NoSQL

  • MongoDB
  • DynamoDB
  • Cassandra
  • Couchbase
  • Firebase
  • Cosmos DB
  • Neo4j
  • ArangoDB

Data Engineering

  • Apache Spark
  • Apache Flink
  • Apache Beam
  • Hadoop
  • Kafka
  • Kafka Connect
  • Debezium
  • Airflow
  • Dagster
  • Prefect
  • dbt
  • Trino
  • Presto
  • Databricks

Data Warehousing & Lakehouse

  • Snowflake
  • BigQuery
  • Amazon Redshift
  • Azure Synapse
  • Databricks Lakehouse
  • Delta Lake
  • Apache Iceberg
  • Apache Hudi

Vector & AI Databases

  • Pinecone
  • Weaviate
  • Milvus
  • Qdrant
  • Chroma
  • pgvector
  • Redis Vector Search
  • Elasticsearch Vector Search
  • OpenSearch Vector Search

Explore the full stack

Enterprise outcomes

Measured in the operating business, not in the programme report

Faster decision cycles

Reduce the distance between enterprise data and action.

Lower cost of operations

Automate the work itself, so cost per outcome falls as quality rises.

Higher engineering velocity

Accelerate product, platform and application development.

Greater automation

Automate work across processes, functions and workflows.

Reduced technology complexity

Modernise fragmented estates and simplify the enterprise architecture.

Improved enterprise visibility

See operations, risk and performance in one governed picture.

Scalable AI adoption

Move successful AI from individual use cases to enterprise-wide capability.

Continuous operating intelligence

An enterprise that improves without waiting for the next programme.

Frequently asked questions

Both. It is an architectural layer engineered above the existing estate, and iFortis Worldwide brings a reference implementation to every programme so it is built consistently.

No. The fabric governs access to data where it lives and connects it through the ontology and semantic layer. Consolidation happens where it earns its place, not as a precondition.

Because a model can only reason about the enterprise in the terms it is given. A governed ontology and context layer are what let an agent understand that this invoice, this contract and this customer are related, and act correctly.

It is a living layer: knowledge updates as work happens, lineage and quality are monitored, and the ontology is governed by named owners rather than left to drift.

Build the intelligent enterprise. Explore an AI transformation opportunity with iFortis Worldwide®.

iFortis Worldwide®

Independently audited. Continuously governed.

ISO/IEC 27001:2022 certification mark

ISO/IEC 27001

Information security management

CERTIFIEDQUALITY MANAGEMENTISO9001

ISO 9001

Quality management

AICPA SOC 2 service organization control report

SOC 2 Type II

Security, availability and confidentiality

EU GDPR

GDPR

Data protection and cross border transfer

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