Your engineers probably use AI coding tools every day. If delivery hasn't moved, the reason is usually that writing code was never the slowest step. We redesign the workflow around agents one stage at a time, prove it on a real project against a baseline, and leave your team running it.
11 days
to take a platform team's first epic to staging, against a pre-AI estimate of 10 to 12 sprints (early signal, small sample)
Client work: AI SDLCAn agentic SDLC, or AI SDLC, is a software development lifecycle where AI agents do defined work at every stage: drafting specs, writing code, reviewing pull requests, generating tests and checking security. Engineers set the standards, approve what ships and own the result.
When code generation speeds up, the queue moves to review, testing, security checks and sign-off. We put agents into the steps that check work as well as the steps that write it.
We start in one repository and one stage of delivery, so the change is contained and the result is clear.
Before the pilot, we take a baseline on the same team and repositories. At the end, you get a before and after readout and decide whether to roll it out further.
Your engineers are trained in the new workflow and own it. What we build gets maintained inside your company, and we plan for that from the start.
For organisations with an established engineering team, a live codebase and continuous delivery, who want AI to change how they build, ship and maintain software. It isn't a fit if you want a team to build software for you, or a one-off training day.
Start
For technical leaders whose teams say they're using AI, but who aren't seeing the gains they expected.
Outcomes
Format
€2,000
$2,250 / £1,750
Book online nowThe full fee is credited against Action Plan or Scale started within 60 days.
Action Plan
For organisations ready to move from experimenting with AI to a structured way of engineering with it.
Outcomes
Format
Scale
For organisations ready to implement and prove agentic engineering at production level.
Outcomes
Format
AI Operating Partnership
Once the new workflow is running, Whitesmith can stay on as your ongoing AI leadership, on a retained basis. Each quarter we recalibrate your AI operating model, keep governance current as tools and regulation change, and help your leadership decide where to invest next.
From €10,000 / month
$11,250 / £8,500 / month
How the AI Operating Partnership worksWe have been changing how software gets built, so AI agents handle specific jobs at each stage. This webinar walks through the QA steps: how a user story becomes test notes and an executed test, and where a QA engineer reviews the result.
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AI operating models: why AI programmes stall at month six
Most AI programmes fail at sequencing, not budget. The AI operating model framework we use to find which cell to invest in first, and what to tell the board.
The repo is the operating model
What you commit to a repository before installing AI decides whether your team builds capability or rents it. Here is the kit we ship first.
From periodic security review to every commit
How we moved security review from a few times a year to every pull request, shown live in our AI for tech teams webinar. Check the slides.
AI workflows for project managers
Three AI skills we run as project managers and that we built for our clients: a kickoff pack from a client brief, a meeting briefing that checks the board against GitHub, and a weekly tracker.
How we run security review on every pull request with agents
How we moved security review from a few times a year to every pull request, with agents that hunt for issues and agents that try to refute what they find.
Simultaneous native development
AI agents make building native iOS and Android apps in parallel as cheap as cross-platform. Why we're rethinking the mobile architecture question.
Tell us about your team, your codebase and where delivery is getting stuck, and we'll get back to you.
Story points are estimates, and their meaning shifts once AI changes how work gets done. We measure per unit of work first: lead time and review iterations per pull request. Throughput comes second, with caveats. On one small platform team, specs per week rose from 2 to 6.4 and rework fell from 8% to 0%, an early signal from a small sample.
The work comes in stages, priced in the table on this page. Start is a fixed-fee diagnostic, and its fee is credited in full against the next stage if you begin within 60 days. Action Plan turns the findings into a phased plan. Scale builds one working AI-enabled workflow in a live project and ends with a before and after readout. Work covers one repository and one delivery stage at a time.
It fits if you have an in-house engineering team, a live codebase and continuous delivery, and your engineers use AI tools without delivery moving. It fits less well if you want a team to build software for you, or a single training day. If you're unsure, the free AI Momentum Check takes about 8 minutes.
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