Service detail
Last updated: May 2026
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.
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.
Buyer Guide
How AI consulting that leads to implementation usually starts
AI consulting that leads to implementation starts by narrowing a broad request into the first business result worth proving. The smaller the first scope, the faster the team can expose real risks around data, APIs, user experience, AI behavior, security, and handover.
The first conversation covers the goal, users, current workflow, available data, existing systems, timeline, and internal decision process. From there, Urbano DX recommends whether the first step should be an audit, PoC, MVP sprint, or ongoing delivery track.
The output should not be a demo that disappears after the call. It should leave source, runbook notes, acceptance criteria, open risks, and a next-step recommendation that the buyer can use internally.
First decision
What must be proven before the buyer funds the next step.
Managed risks
Data, APIs, AI behavior, security, adoption, and handover.
After delivery
Internal explanation, next sprint, vendor comparison, or budget approval.
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: what should buyers know?
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: what should buyers know?
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
What myths slow down AI workflow builds?
Myth
If the demo works, the PoC is done.
Fact
A useful PoC also leaves acceptance criteria, logs, risks, handover notes, and a clear next decision.
Myth
AI workflows should be fully automated from day one.
Fact
Early sprints are usually safer with human review, evidence display, and an override path.
Myth
Low-code means there is no engineering design risk.
Fact
Business-critical workflows still need API contracts, permissions, audit logs, and failure behavior.
AI consulting flow
Step 1
Understand the business workflow
Review teams, systems, documents, APIs, approval paths, current AI tools, and manual bottlenecks.
Step 2
Map AI and software opportunities
List candidate workflows, internal tools, dashboards, document automations, LLM features, and API integrations.
Step 3
Prioritize by impact and readiness
Score each candidate by impact, data readiness, risk, stakeholder ownership, and first-sprint effort.
Step 4
Define the first implementation
Produce a roadmap, first sprint scope, acceptance criteria, risk notes, and next-step recommendation.
Buyer FAQs
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.
Scope the first sprint
Bring the app, API, LLM feature, or AI workflow you want to test. We will turn it into a clear first-sprint scope.
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