AI consulting that leads to implementation
Find the workflows where AI and custom software can create measurable value, then turn the first use case into a scoped build plan.
What consulting covers
The consulting engagement starts with the business process, not the model. Urbano DX maps current workflows, systems, documents, handoffs, data access, and adoption constraints before recommending where AI or custom software should enter.
- Workflow interviews
- Tool and data inventory
- Manual handoff mapping
- Security and procurement constraints
- Adoption risks
Use-case prioritization
Each candidate use case is scored by business impact, data readiness, technical risk, user adoption, and implementation effort. That avoids starting with the loudest idea instead of the highest-leverage one.
- ROI hypothesis
- Data readiness
- Risk and compliance review
- User group and owner
- Sprint effort estimate
Roadmap to working software
The output is a practical implementation path: what to build first, what to leave in existing tools, what should become custom software, and what proof is needed before budget expands.
- Prioritized AI backlog
- First sprint scope
- Acceptance criteria
- SOW-ready assumptions
- Next build recommendation
How it differs from generic strategy
This is consulting that leads to delivery. The recommendations are written so they can become a paid audit, PoC, MVP sprint, API sprint, or owned software build instead of staying as abstract transformation language.
- Implementation-first recommendations
- Technical feasibility checks
- Working proof path
- Handover and ownership assumptions
Métricas clave
- 1-2 weeks: consulting audit - A short engagement can turn broad AI interest into a ranked backlog and first sprint scope.
- 10-20: candidate use cases - Typical workshops surface many opportunities, then reduce them to the few worth proving first.
- 1: first proof path - The final recommendation identifies the first workflow, app, API, or AI feature to validate.
- SOW: scope-ready output - The roadmap includes assumptions, exclusions, acceptance criteria, and handover expectations.
Preguntas frecuentes
- How much does a DX PoC cost?
- A focused paid PoC usually starts from the Quick DX PoC range. Final pricing depends on data access, integrations, security needs, deployment environment, and acceptance criteria.
- How long does an AI automation sprint take?
- Most focused PoCs fit into 2 weeks, MVP automation sprints into 4 weeks, and production-oriented integrations into about 6 weeks.
- What data is required?
- The fastest start includes sample files, API docs, screenshots, example tickets, user roles, current workflow notes, and one owner who can join weekly demos.
- Can we start without API access?
- Yes. The first sprint can use exports, sample datasets, mocked APIs, or manual upload flows, then move toward API integration once access is approved.
- Do you support Japanese documentation?
- Yes. Engagements can include bilingual summaries, demo notes, handover materials, and meeting support through the Japan Desk model.
- Who owns the source code?
- Source-code ownership, repository handover, licensing, and reusable components are defined in the SOW before the sprint begins.
- What do we receive after 2 weeks?
- For a narrow PoC, the usual output is a working prototype or API slice, demo notes, assumptions, risks, acceptance criteria, and a recommendation to harden, integrate, expand, or stop.
- Who owns technical decisions?
- Senior engineers stay close to scope, architecture, AI-use risk, technical tradeoffs, weekly demos, and handover quality instead of hiding decisions behind layers of project management.
- What does an API sprint deliver?
- A focused API sprint can include endpoint design, an OpenAPI-style contract, auth assumptions, sample requests and responses, integration tests, logging, and handover notes.
- How do you measure whether the sprint worked?
- Each sprint starts with one measurable proof point such as reduced manual steps, successful extraction rate, API handoff success, response time, reviewer acceptance, or pilot-user feedback.
- Is this strategy consulting or implementation consulting?
- It is implementation-oriented consulting. The output is a prioritized backlog, first sprint scope, risk notes, and acceptance criteria that can become a paid audit, PoC, or MVP sprint.
- Can this start before we know what to build?
- Yes. This service is designed for teams that know they need AI or workflow improvement but are not yet sure which use case should go first.
- Do you provide only recommendations?
- No. Recommendations are written so they can move directly into software delivery, API integration, LLM workflow design, or a technical second opinion.