AI is the default
Every task starts with how AI should help. People still own judgement, quality and the call to ship.
DatabyPassion is a founder-led boutique technology consultancy. One accountable core holds the outcome and delivery standard; trusted specialists join when the problem needs their depth.
DatabyPassion exists to close the gap between senior advice and the product that actually reaches users. The engagement lead stays close to the business problem, architecture and build instead of passing the work down an account ladder.
The founder's career includes lead data-engineering work in wealth at Mercer and AI-engineering experience at EY-Parthenon. Those organisations are career context only; they are not presented as DatabyPassion clients.
The result is deliberately small at the centre: direct communication, visible decisions and a team that changes with the work.
Founder film — English, with captions — will sit here when it is ready.
Six senior capabilities show how an engagement can form. One accountable lead holds the outcome; specialists join when the work earns them. Named people and time commitment are agreed around the scope.
These are the rules we hold ourselves to on every engagement — so a small, senior team can move at startup speed inside an enterprise without becoming theatre.
Every task starts with how AI should help. People still own judgement, quality and the call to ship.
A working note, a workshop and an executive product are not the same deliverable. Polish follows audience, not habit.
Stack and tooling follow the problem and the people who must live with it. We do not install a product looking for a use.
Prefer a working increment over a perfect plan. Add contracts, tests and controls as the product earns them.
When a decision is blocked, document the assumption and continue. Challenge the assumption — do not wait in silence.
Every increment leaves a shareable artefact the client can see, challenge and take into the business.
Move fast to learn. Bring users into the work when they must live with the result — adoption is not a handover slide.
Interfaces, APIs and context should be usable by humans today and by agents tomorrow. Document in the repo, not in someone’s head.
The collective uses the same AI-first, visible, accountable model it asks clients to trust. We do not sell a way of working we will not run ourselves.
Code, contracts, runbooks and product context stay useful after we leave. The next release is earned with evidence, not momentum.
The person shaping the engagement stays close to the decisions, the build and the standard.
Roles join because the work needs them—not to fill a pre-sold organisation chart.
Decisions, artefacts and handover stay visible so progress does not depend on account layers or hidden context.
The method was not licensed from a playbook. It is how senior product, engineering and consulting work already holds together when the outcome is software a business can run: Agile and Scrum for inspectable cadence, consultancy delivery for framing, decision rights and sponsor artefacts, and the operating habits of technology teams that ship inside real enterprises.
It matured in our own delivery — tightened each time a workshop, a sprint or a handover failed to earn the next commitment. Understand, Shape, Build and Evolve is the sequence a sponsor can inspect. Each stage ends with a useful artefact and an explicit decision to go, reshape or stop. Scrum stays in service of that sequence; it does not become the work.
Map the business decision, real workflow, users, data and delivery constraints.
Choose what ships first, what waits, how risk is handled and what evidence matters.
Deliver in short, visible loops inside the agreed stack and control model.
Observe use, improve the product and decide whether to scale, transfer or stop.
Before anything is designed or built, we sit with the people who own the outcome. We map the decision they need to make, the workflow that exists today, who actually uses it, which data can be trusted, and the constraints that will kill a clever idea in production.
The artefact is a problem frame the sponsor can recognise — not a discovery programme that invents a new transformation. If the problem is not valuable, owned or solvable in a first product, we say so here.
We do not sell a transformation. We choose the smallest product that can create evidence: what ships first, what waits, how risk is handled, and what “good” looks like in the business.
Scope, team shape and controls are agreed here so Build is execution, not a second negotiation. The collective changes with that shape — specialists join for the decisions and delivery the work actually needs.
Senior people stay in the work. Delivery happens in short, visible loops inside the stack and control model you already run — or the one we agree for the engagement. Those loops follow Agile and Scrum discipline — planning, review and backlog — without turning the engagement into process theatre.
You see working software, not status theatre. Decisions, designs and code live in the engagement record so context does not sit in an account layer or in someone’s head.
After release we watch real use. Improve what is working, transfer ownership when the client team can run it, or stop cleanly when the evidence does not support more investment.
Expansion is a decision, not an assumption. A useful first product can earn the next — scale the product, add the next use case, or end the work. The next commitment is earned with evidence, not momentum.
Build a concise brief before choosing whether to start a conversation.