AI Native Technology

AI Engineering & Agentic SDLC

Production-grade AI applications, engineered and governed by design.

From AI prototypes to production-grade systems.

Most enterprises can show a working prototype. Production asks harder questions: how the system is evaluated before release, how it is observed once live, what it costs per request, how it is updated when the model changes, and who signs off. AI engineering treats those questions as requirements from the first sprint.

Applications, models and agents are built as enterprise software: version-controlled, tested against evaluation harnesses, deployed through governed pipelines and observed in operation. The agentic software development lifecycle extends that discipline to agents themselves, so the way they are built is as accountable as the way they run.

Two developers pointing at code on a monitor

Core capabilities

Engineering capabilities delivered end to end by a single accountable team

AI application engineering

Full-stack engineering of AI applications, from interface to model integration to operations.

Generative AI engineering

Generation, summarisation and reasoning features engineered with grounding, guardrails and evaluation.

LLM application development

Applications built on large language models with retrieval, memory, tools and policy.

RAG systems

Retrieval-augmented generation grounded in governed enterprise knowledge, with lineage and access control.

Agent engineering

Agents engineered with typed tools, bounded authority and continuous evaluation.

AI evaluation

Harnesses, golden sets and scenario tests that decide fitness for release, run in every pipeline.

Model integration

Provider-agnostic integration of models with routing, fallback and cost controls.

Agentic SDLC

A development lifecycle for AI systems: evaluation gates, model change management and release governance.

MLOps and LLMOps

Pipelines, registries, serving and monitoring for models and prompts alike.

AI observability

Traces, quality signals, drift and cost visible in one operating picture.

AI testing and quality engineering

Quality engineering for probabilistic systems, from unit tests to red teaming.

iFortis Worldwide®

Where it sits in the architecture

This domain engineers AI Applications + Models + Agents 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

AI & Machine Learning

  • PyTorch
  • TensorFlow
  • Keras
  • scikit-learn
  • XGBoost
  • LightGBM
  • JAX
  • ONNX
  • MLflow
  • Kubeflow
  • Weights & Biases

Generative AI & LLM Engineering

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Meta Llama
  • Mistral
  • Cohere
  • Hugging Face
  • Azure OpenAI
  • Amazon Bedrock
  • Google Vertex AI
  • LangChain
  • LangGraph
  • LlamaIndex
  • Semantic Kernel
  • DSPy
  • Ollama
  • vLLM
  • Transformers
  • PEFT
  • LoRA

Vector & AI Databases

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

DevOps & CI/CD

  • Git
  • GitHub
  • GitLab
  • Bitbucket
  • Jenkins
  • CircleCI
  • Travis CI
  • GitHub Actions
  • GitLab CI/CD
  • Azure DevOps
  • Argo CD
  • Spinnaker
  • TeamCity
  • Bamboo

Testing & Quality Engineering

  • JUnit
  • TestNG
  • pytest
  • Jest
  • Vitest
  • Mocha
  • Cypress
  • Playwright
  • Selenium
  • Appium
  • Postman
  • Newman
  • k6
  • JMeter
  • Gatling
  • SonarQube
  • ESLint
  • Prettier

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

Evaluation before release, observability in operation, cost controls, a change process for models and prompts, and a named owner. If any of those is missing, it is a prototype with users.

The architecture is provider-agnostic. Models are integrated through a routing layer with fallback and cost controls, so the enterprise can change providers as the market moves without re-engineering the application.

Through the agentic SDLC: every model or prompt change runs the evaluation harness, is reviewed like any other release, and is observed after deployment against the same quality signals.

With evaluation sets built from real enterprise cases, scenario replay, human review where judgement matters, and live quality signals in production. Quality is a measured property, not an impression.

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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