EdTech · Mobile app

A mobile client for an AI learning platform, built without waiting on the backend

Flutter client for an exam-prep platform: adaptive micro-lessons, a streaming AI assistant, a knowledge map. The UI was built alongside the backend via a mock layer.

Status
In-house product
Timeline
about 3 months MVP + 2.5 months extension
Stack
  • Flutter / Dart
  • flutter_bloc
  • go_router
  • get_it
  • dio
  • drift / SQLite

The task

The platform had a web client and an AI backend but no mobile app, and its users, school students, live on their phones. We needed a thin client on the existing API that keeps pace with the web version, while some backend routes were still in progress. Voice went to the second iteration and offline mode to the backlog.

What we built

Outcome

13 weeks
from first commit to a working app with sign-in, goals, lessons and AI chat
41 → 81 endpoints
API coverage doubled from MVP to the extended version, with 14 → 31 modules on the same architecture
+17 modules in 2.5 months
31.5k lines on top of the MVP across 10 documented sync phases with the web version, with no change to stack or module structure
20 mock repositories
every module runs without a backend, so UI was built in parallel with server routes (8 in the MVP, 20 in the extended version)

Services

More cases

IT consulting · Lead qualification

In-house product

A pilot plan for the visitor, a qualified lead for sales

We replaced the contact form with an AI consultant: it interviews the visitor, returns a pilot plan with KPIs and risks, and sales gets a lead with budget and timeline.

1 day
to build a working MVP: frontend, backend and deploy configs
16 days
from first commit to production with HTTPS and an issued certificate on its own domain

Aviation · aircraft components trading and MRO

Pilot

Aircraft parts procurement: from email to a managed pipeline

An AI agent reads incoming RFQs, compares supplier quotes and checks trace documents. A separate deal-tracking system moves each deal through mandatory stages.

Full loop
the AI loop ran end to end on a live procurement process, from an incoming RFQ to a team notification. This was a pilot and the figures are our own so far: the client has not confirmed them yet
3
types of trace documents checked by the agent: FAA 8130-3, EASA Form 1 and COC. Like the figure above, it still awaits the client’s confirmation

Distribution · Partner networks

In production

A sales and support platform for a distributed partner network

Partners ask who on their team is inactive and what blocks the next level, and get numbers for their own part of the network only. Access is enforced at the SQL level.

Their own slice
a partner gets figures for their own team only: a filter on the current user is added to every SQL query
6 layers
of data protection between the language model and the database, from blocked personal-data columns in SQL to rate limits; counted in the code

Estimate the payback of a similar process

AVA lets you work out the economics of your process on your own figures, with no form and no price from us. It shows what the manual work costs, what cash effect automation would give and what budget would pay back. If the effect does not even cover running costs, it says so.

Estimate the payback

Discuss a similar problem

Describe the process and the data you have. Before any work starts we will tell you what can be measured in money, what cannot, and whether it is worth doing.

In the reply we will say what we understood, what we need to clarify and whether it is worth doing.

or email us directly: hello@xteam.pro

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