How we work
Five phases, one accountable team, and a decision point at the end of each phase so you never fund the next step blind. Here is what happens, how long it usually takes, what you get and what we need from you.
The process
Phase 1
1 to 3 weeks
We turn business objectives into requirements: stakeholder interviews, customer research, process mapping and gap analysis. For AI work we also check data readiness and agree how accuracy will be measured before anything is built.
What we need from you: A decision owner, access to stakeholders and existing data or documentation.
Phase 2
2 to 6 weeks
UX/UI design and prototyping of the riskiest parts first, tested with real users or real data. AI features get a working prototype and an evaluation set, so you decide with evidence rather than slides.
What we need from you: Feedback within agreed review windows and access to a few real users.
Phase 3
6 weeks to 6 months, in 2-week sprints
Development in short iterations with QA from the first sprint. Goals are broken into granular, trackable tasks; every dependency is mapped and every sprint ends with a review you can attend.
What we need from you: Environments and credentials, and a product owner available for sprint reviews.
Phase 4
1 to 2 weeks
Migration, performance, accessibility and security checks, then a controlled release. We train the people who will run and use what we built.
What we need from you: Sign-off on acceptance criteria and a launch window.
Phase 5
Ongoing, monthly
We monitor, support and improve what we launch against the metrics agreed in discovery. For AI systems that includes re-running evaluations when models change.
What we need from you: A quarterly review slot with the business owner.
For AI projects the same phases apply, with evaluation built in: we agree how accuracy is measured before we build, and you can stop after the prototype. See the AI Prototype Sprint.
The low-risk first step
Align business, IT and legal on what to build first, in one focused session.
Who it is for: Cross-functional teams that need one shared plan before a project, an RFP or an AI initiative.
Engagement models
Best for: Audits, workshops, prototypes and projects with clear requirements.
Scope, deliverables and timeline agreed up front. Changes go through a written change request with impact on time and budget.
What drives cost: Number of features and integrations, content and data migration, compliance requirements.
Best for: Evolving products where priorities change as you learn.
You pay for the time the team spends, with a monthly budget cap, sprint-level estimates and transparent timesheets.
What drives cost: Team seniority and mix, sprint length, number of parallel workstreams.
Best for: Long-running platforms and roadmaps that need a stable, embedded team.
A cross-functional team (for example PM, designer, developers, QA) works only on your product, with your rituals and tools.
What drives cost: Team size and composition, required availability, onboarding into your stack.
Best for: Support, optimisation, marketing and AI operations after launch.
A monthly capacity with a service level, a rolling backlog and a monthly report on what was done and what moved.
What drives cost: Response times in the SLA, monthly capacity, number of systems covered.
Project & account management
Digital initiatives succeed only when business needs are clearly defined and translated into actionable requirements. We bridge business objectives, customer needs and technical implementation, adapted to your sector and digital maturity.
Planning frameworks anchored in outcomes and ready to flex: agile sprints, hybrid workflows or milestone-driven roadmaps, structured around how your team works best.
Agile workflows with continuous communication. Transparent by default: daily updates, live dashboards, asynchronous reports and sprint reviews that keep stakeholders informed without interrupting flow.
Who you work with
Transparent by default, without meetings for the sake of meetings.
We use modern collaboration platforms and adapt to the ones your team already uses.
Quality
A dedicated QA team led by a QA team leader tests from the first sprint: test plans, manual and automated tests, regression before every release.
We design and test against WCAG 2.2 AA, with keyboard, screen reader and contrast checks before launch.
Least-privilege access, secure development practices and GDPR-aligned data handling. For AI work, your data stays in your tenant or an agreed EU region.
We combine agile execution with strategic alignment, so projects adapt quickly without losing sight of business goals. One accountable team covers analysis, design, development, QA and delivery, and you see progress every sprint rather than at the end.
Yes. We adapt the same framework to single campaigns, fixed-scope audits and prototypes, and enterprise-wide digital transformations. The phases stay the same; the depth of each phase and the size of the team change with the scope.
We provide real-time visibility through dashboards, daily async updates, weekly status meetings, sprint reviews and a monthly report. Stakeholders see decisions, risks and progress without having to chase the team for information.
Yes. We work with tools like ClickUp, Notion, Jira, Miro and Slack, and adapt to your preferred systems, rituals and approval flows rather than asking your team to switch.
Our delivery model is built to adapt. In time and materials or dedicated team models we re-prioritise the backlog each sprint. In fixed-scope projects, changes go through a short written change request that shows the impact on time and budget before you approve it.
The handover pack covers code, documentation, design files and, for AI work, prompts and evaluation sets, so your team or another partner can run it. Ownership terms are set in the contract for each engagement.
Send us a short brief and we will propose the first phase, the team and the engagement model that fits.
We reply within 1 business day, usually the same day. Not ready to talk? Take the free AI assessment.
