AI architecture & engineering
For enterprise workflows where AI decisions and actions need clear boundaries.
Defined permissions. Risk-based controls. Traceable outcomes.
Or start from one of these.
Example workflow for:
Illustrative workflow selected from our service catalogue. Your requirements still need review.
Tell us which systems are involved and what you want to achieve, or explore the examples.
Discuss your project Explore the examplesWays to work together
Clarify the unresolved decisions in an AI initiative: system access, data, controls, failure handling and ownership.
Discuss a reviewWork through the architecture and implementation of an AI capability connected to your existing systems.
Discuss an integrationInvolve David in the architecture and technical decisions of your AI project, alongside your internal team and delivery partners.
Discuss project involvementFrom AI ambition to work that gets done
Explore the operational applications, then discuss which scope and controls your situation needs.
Planning adoption across management and teams? See AI Adoption.
Designing how AI reaches enterprise systems? Explore MCP architecture.
Remove repetitive work, shorten cycle time and reduce errors, with a result you can measure.
Prepare the data, integrations, permissions and approval controls one AI use case needs.
Start from defined AI workflow patterns for sales, finance, marketing and IT operations.
See the AI rolesDesign an agent around your process, with agreed acceptance tests, handover and operating responsibilities.
Explore custom agentsAI Workforce + AI Agents
Explore defined workflows across four operational areas. Select one to see an illustrative workflow and discuss the scope.
Research, outreach and reporting with a rep or marketer approving what goes out.
Documents in, approved data out. Twenty document types, five languages and four ways in.
Monitoring, reviews and change planning through defined operations, with agreed access and action controls.
An agent around your process, with agreed acceptance tests, handover and operating responsibilities.
Define what AI can access, which actions it may propose or execute, and where approval is required. Higher-impact actions need stronger checks, explicit responsibility and a recovery or escalation path.
Direct answers
An AI workforce is a set of defined AI roles for recurring operational work. On this site those roles cover sales, marketing, finance, documents and IT operations. The scope and controls are agreed for the workflow.
AI workforce roles start from a defined workflow pattern. A custom AI agent is designed around a process, system and acceptance criteria unique to your company. Both need defined permissions, appropriate controls and action and outcome records.
AI enablement maps the process, connects the required systems, limits the operations available to AI, defines approval points and sets acceptance tests before deployment.
Permitted actions, validation and approval rules are defined for each workflow. The required controls depend on impact, reversibility and your organisation's policies. Where human approval is required, it covers the exact action and target.
The scope needs to define which data and actions are available, who can authorise access and which controls apply.
Describe your situation and the decision or work you need to support. The objective, access, responsibilities, scope and expected output are agreed before work starts. A pilot is one possible starting point, not a requirement for every project.
Technical perspective
Founder & Technology Consultant
Putting AI into production raises decisions about system access, ownership, verification and failure handling.
My background combines enterprise application development, system integration and production operations. I use that experience to work through the decisions that arise when AI connects to business systems.
Why AI projects stall before the model matters
The process comes from the people in it. I walk through the work with the people who do it today, before anyone touches a model. What they approve is what the AI learns to prepare.
The engagement defines the objective, responsibilities, scope and acceptance criteria around the systems and people involved. Technical decisions, handover and ongoing support are agreed for the specific project.
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