SERVICE · MVP DEVELOPMENT
A first product version your team can test
Turn one business hypothesis into working software. Product, design and engineering share the scope, with real users involved before launch.
- Strategy through deployment
- One team
- Working progress you can test
- Weekly
- Built inside your constraints
- In your cloud
Brands the team has shipped for over 15 years
Digital products and AI integration cases- Shipping digital products
- 15+ yrs
- Brands served
- 300+
- Projects delivered
- 600+
- Senior team, close to the work
- Taipei
Build the smallest useful version
A minimum viable product (MVP) is the smallest working version that lets a team test an important product hypothesis with users. The delivery scope should state the learning goal, acceptance criteria and operating requirements. AI is included when it is useful to that workflow.
The first version needs a clear learning goal and a controlled scope. We identify the users, the decision the product must help them make, and the acceptance criteria for that version.
One team designs, builds and deploys the software. Working iterations give your team and users something to inspect, while architecture, permissions and operations are addressed alongside the product.
Capabilities
- One delivery team
- Product, design, engineering and deployment work from the same scope and decision record.
- Working iterations
- Review software with actual users and use the findings to decide the next scope.
- Controlled scope
- Tie each included feature to the business hypothesis and acceptance criteria.
- An operating foundation
- Include data models, access controls, deployment and a runbook appropriate to the first release.
Use cases
- First customer-facing product
- Test the core workflow with early users before committing to a larger product scope.
- Internal workflow application
- Build a usable version connected to the agreed data and systems, then evaluate it with the team.
- A new module for an existing product
- Test a bounded module with a clear integration plan and acceptance criteria.
Delivery cadence
01
01 · SCOPE
Agree on the hypothesis
Define users, the first workflow, constraints and acceptance criteria.
02
02 · BUILD
Review working iterations
Design and engineering produce software users can try, with decisions recorded as the scope develops.
03
03 · VALIDATE
Test the release conditions
Check the main user path, integrations, access controls and operating limits.
04
04 · LAUNCH
Establish the owner
Deploy the agreed release with documentation, support responsibility and a plan for the next decision.
FAQ
How is the first scope controlled?
Each included feature must answer the agreed business question or satisfy a release requirement. New ideas are assessed separately before changing the scope.
How long does a first version take?
A focused version can move in weeks when users, data and decisions are available. We confirm dependencies and release conditions before agreeing on a timeline.
Can our internal team continue the work?
Code access, documentation, licenses, knowledge transfer and operating responsibility are specified in the engagement so your team can review the continuation plan.
Does every product need AI?
No. We use AI where it serves the workflow. If a simpler application or fixed rule meets the need, that is the better starting point.
SCOPE · CHOOSING THE APPROACH
What do you need from a prototype or an MVP?
A clickable prototype can test a design. A working MVP can test a live workflow. Choose the artifact that answers the current question, then agree on what is required to operate it.
Design prototype
Working MVP
Purpose
Test a concept, flow or interface
Test the core workflow with working software
Data and integration
Often simulated for the design test
Connect the data and systems included in scope
Release conditions
Review usability and design assumptions
Also validate access, failures and operating limits
Next step
Use the findings to choose a build scope
Use the findings to continue, change or stop the product
KNOWLEDGE BASE · FURTHER READING
The AI-native MVP development knowledge base
Selected guides and external perspectives for planning this workflow.
Tenten AI whitepapers & perspectives
- Claude Code at Enterprise ScaleDeployment Patterns, Cost Models, and What AI-Augmented Engineering Looks Like at 100+ Engineers
- The FDE Operating ModelHow Forward Deployed Engineering Teams Build AI Systems That Last and Transfer Capability Alongside Code
- From AI Pilot to ProductionA Planning Guide to Data Readiness, Governance, Evaluation, and Adoption
- All whitepaper resourcesGuides for models, architecture and operations.
START HERE
Leave your contact details
Enter your name and work email. The problem type is optional. We will learn about the workflow by email; no specification or confidential data is needed here.
- Start with one real workflow
- A senior delivery team reviews the enquiry
- We will say if AI is not useful
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