The scope and duration depend on the work. These are common starting points for data and AI architecture, implementation, training, and embedded technical support.
These are starting points, not rigid packages. Each engagement is adapted to your team, existing technology, delivery constraints, and appetite for internal ownership.
Use a review to make a high-consequence technical decision with more confidence. We examine the requirements, architecture, users, constraints, and delivery risks, then turn the findings into an actionable recommendation.
Use an implementation project to move a defined system or product capability into working software. We design and build alongside your team, with production constraints, testing, documentation, and handover considered from the start.
Use embedded support when the team needs experienced technical leadership that can still work directly in the detail. We join the delivery rhythm, help prioritise decisions, unblock implementation, and build capability as the system evolves.
Review the context, users, existing system, delivery pressure, and the decision that actually needs to be made.
Agree the target outcome, boundaries, architecture, risks, responsibilities, and evidence of completion.
Build, review, and validate the system with the people who will use, operate, and continue developing it.
Leave clear documentation, working software, decision context, and practical capability inside the team.
Application code, agent workflows, data pipelines, APIs, templates, infrastructure, tests, and reference implementations.
Architecture diagrams, technical decisions, tradeoffs, delivery sequencing, estimates, and risk assessments.
Workshops, operating guides, implementation notes, paired sessions, and code review for the internal team.
Share the problem, your current technical setup, and the help you need. We will reply with relevant questions and a proposed scope.