AI Engineering
We design and integrate AI capabilities into applications and platforms to enable smarter systems and improved decision-making.
Engagement
01 / 05
What We Do
Build AI-enabled applications
Design AI solution architectures
Implement AI agents and intelligent workflows
Embed AI into business processes
Apply AI across the software delivery lifecycle
How we think
02 / 05
AI Engineering Principles
AI where it earns its place
Not every process needs an agent. Automation, AI-assisted workflow, deterministic orchestration and agentic autonomy are different tools — we prefer the most predictable one that solves the problem, and select autonomy only where value, risk and complexity justify it.
Human judgement where it changes the outcome
Cloud environments and platform components are defined through repeatable, version-controlled automation wherever practical.
Independent verification
AI-generated outputs should not be accepted on generation alone. We use independent checks—automated tests, deterministic controls, evaluation models and human review where appropriate—to verify quality and correctness.
Automate what can be verified
We translate standards, acceptance criteria and quality controls into automated checks wherever practical, allowing AI-assisted delivery to move quickly without bypassing engineering discipline.
Governed boundaries, with escalation
AI acts inside defined boundaries, with oversight and a path to escalate anything that falls outside them
Measured, not assumed
AI-assisted work is evaluated against the outcome it is meant to produce, rather than accepted on impression.
How we build
03 / 05
AI Solution Architecture
A production AI capability is more than a model call. It combines models with context, tools, workflow, security, guardrails, evaluation and observability—integrated into the systems that already run the business.
Security, Governance & Guardrails
Identity & access - Data protection- Tool permissions - Content safety - Policy & compliance - Human approval & escalation
Enterprise Systems & Data
The systems of record the AI must work with
Application & Experience
Where people and systems make the request
AI Orchestration Layer
Model access
Anthropic ·
OpenAI · others
Tools & functions
MCP · APIs
Retrieval & context
RAG
· vector search
Workflow & state
Agent patterns
Evaluation & Observability
Tracing - Quality & task evaluation - Safety evaluation - Cost & latency - Failure monitoring - Feedback
The model is one component. The engineering is everything around it.
What we work in
04 / 05
Technologies & Practices
Grouped by the engineering concern each one answers, because that is how the decisions actually get made.
AI Platforms & Models
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Anthropic (Claude)
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OpenAI
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Azure OpenAI
Agents & Orchestration
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Agentic workflows
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Agent and tool-use patterns
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Deterministic orchestration
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Model Context Protocol (MCP)
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Workflow and state management
Knowledge & Context
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RAG
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Vector and semantic retrieval
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Grounding and source attribution
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Chunking and indexing
Evaluation & Observability
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Evaluation frameworks
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Tracing
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Quality and safety metrics
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Cost and token monitoring
Security & Governance
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Identity and least privilege
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Guardrails and content safety
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Prompt-injection controls
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AI governance and control
Engineering Practices
What we work in
05 / 05
Supports Our Solutions
AI Engineering is the engineering discipline, not a single service. These are the solutions that apply it — to business processes, and to how software itself is designed, built and operated.
Application Modernisation
Cloud Modernisation & Migration (Azure & AWS)
