From concept to a tested product, in weeks.
Most builds spend their first month inside planning documents. We spend ours on the data model and the security decisions, because those two are what a rebuild is usually made of.
- A costed plan by the end of week two
- The code and the accounts are yours
We build to answer a question
The core logic and the database structure come first. Polish waits until real use has said which screens deserve it, which is usually not the ones a roadmap picked.
We decide the data rules in week one
Retention and permissions get settled before the first screen exists. Leave them until submission and you are unpicking decisions that were made and paid for months earlier.
What the AI MVP development process is
The AI MVP development process is a software prototyping process with the architecture settled before the code. We map the data pipeline and the security model first, then build the one feature the product stands or falls on. A closed group uses it on real data, and what they do with it decides the roadmap. The repository and the accounts hand over at the end.
The four phases, and what each one ends with
Every phase ends with something you hold. Two of them end in a decision that is yours, and the work stops until you have made it.
- Week 1 and 2
01Architecture and security mapping
Your data pipeline gets drawn before a screen exists. We settle the multi-tenant database structure and the endpoints the model is reached through, so proprietary data never lands in a public training set. Week two ends with a costed plan.
- From week 3
02Prototyping and AI integration
The functional core of the product gets built. The model reads and routes the unstructured work. Your own backend does the arithmetic, because a figure a model guessed is a figure nobody trusts.
- Once the core runs
03Closed testing and store readiness
A controlled group uses the product on real data. Media permissions and the data handling declaration get checked against what the build actually does, which is the part a store review examines.
- At handover
04Handover and public launch
The cloud accounts were yours from week one. You take the repository and the deployment pipelines, and we either scale the infrastructure with you or step back.
How this differs from a traditional build
Both roads end at a product. They differ in when the expensive decisions get made, and in who owns what afterwards.
| Measure | A traditional build | This process |
|---|---|---|
| Timeline | Measured in quarters | Measured in weeks |
| Architecture | One system, built whole | An API layer per part |
| Security | Added towards the end | Mapped in week one |
| Model retention | Whatever the default was | Switched off at the call |
| Store review | Met at submission | Built for a closed track first |
| What you own after | A system somebody else hosts | The repository and the accounts |
What holds the whole way through
Three things hold from week one to handover. None of them needs an auditor, because each is a decision rather than a certificate.
- 01
The model layer stays isolated
Calls run through the vendor's own developer platform, with retention switched off and training disabled on the account. Your inputs get processed and not kept.
- 02
The backend does the maths
A model never calculates a number that matters. It reads and routes what arrives, and your own code works out the figures from that.
- 03
No vendor to be locked into
The proprietary logic and the vector database hand over. Nothing here is hosted on an account we control, so there is no service to be cut off from.
- NDA
- Data processing agreement
- Named accounts
- Credentials rotated
- Your accounts, your keys
One team, and a lot of time zones.
The team sits in Madurai. The work has reached 50+ countries, and every one on this map is a place it landed. Hover a line to follow one.
Frequently asked questions
How involved do I need to be during the build?
You define the business logic and supply a sample dataset. We handle the technical execution and check in at the points where a decision is yours. That is the costed plan at the end of week two, and the day the closed beta goes live.
Will this MVP pass Google Play and App Store review?
Phase three is where that work sits. The data safety declaration and the media permissions get checked against what the build actually does, rather than being written the week of submission. Nobody controls what a reviewer decides on the day.
What happens after the MVP is launched?
The infrastructure scales with the users who arrive. Beta feedback decides the next block of features, and the roadmap gets rewritten around what people did rather than around what the original document assumed.
What does agile AI engineering mean in practice?
Agile AI engineering means the model layer stays swappable while the product is still being proved. The routing sits behind your own endpoint, so changing which model answers is a configuration decision instead of a rebuild.
What does secure AI app deployment involve?
Secure AI app deployment starts with the database structure and the retention settings, not with a review at the end. The cloud accounts are yours from day one, our engineers work from named accounts, and every credential is rotated when we hand over.
How is this different from a normal software prototyping process?
A prototype is usually thrown away. This one runs on production hosting in a smaller configuration, so the thing your testers used is the thing that grows. What makes an MVP disposable is a rushed data model, and that is the part we do not rush.
Stop planning.
Start prototyping.
The first two weeks end with a costed plan. Bring the business rules and a sample of your data, and we will map it.
