AI application development
From AI idea to production enterprise application.
Most organisations have AI pilots. Far fewer have AI running inside the systems the business depends on.
The distance between a working prototype and a production enterprise application is where most AI programmes stop. The model is rarely the obstacle. The obstacle is integration with systems that were not designed for it, governance that has to hold up to scrutiny, and an operating model that can support the result once it is live. We build for that end state from the first design decision.
Our approach
The pipeline we work through
Seven stages, in order. Skipping the second is the most common and most expensive mistake.
- 1
Business problem
Start from an operational problem with a measurable cost, not from a technology the organisation wants to adopt.
- 2
Use-case identification and value case
Identify where AI changes the economics, and build the value case that decides whether it proceeds.
- 3
AI architecture
Design retrieval, orchestration, model selection and data flow as an architecture, with explicit failure behaviour.
- 4
Enterprise integration
Connect to the systems, data services and identity the business already runs on — this is where prototypes fail.
- 5
Security and governance
Apply access control, data handling, auditability and Responsible AI obligations as part of the build.
- 6
Production deployment
Release into live operations with monitoring, cost controls and a support model that the organisation can run.
- 7
Continuous optimization
Measure output quality and business effect after go-live, and keep tuning against what production reveals.
Capability
What we build
Enterprise applications, not demonstrations.
Enterprise GenAI applications and knowledge assistants
Retrieval-grounded assistants that answer from the organisation’s own documented knowledge, with traceable sources.
Agentic and multi-agent workflows
Orchestrated agents that execute multi-step business processes against real enterprise systems under defined control.
AI-enabled business process automation
Automation of the operational steps that currently scale with headcount rather than with volume.
AI-driven analytics and root-cause analysis
Analysis that explains why an operational outcome happened, not only that it happened.
Intelligent document and workflow processing
Ingestion, extraction and routing of the documents that operational processes currently depend on people to read.
AI integrated into existing enterprise applications
Capability delivered inside the systems people already use, rather than as another destination to visit.
Technology we work with
Named as supporting detail. The architecture decision comes first.
- Azure OpenAI
- Azure AI Foundry
- Azure AI Search
- AutoGen multi-agent
- Azure Durable Functions
- Retrieval-augmented generation (RAG)
- Responsible AI and GDPR
AI does not fail on the model. It fails on integration, governance and the operating model around it.
Bring us a difficult technology problem. We will help define the practical path to solving it.