● Business offer — IT & tech
The AI agent for mobile development: ship faster, without exposing your code
Screens, navigation, business logic, API calls, state management, iOS and Android builds: a considerable share of your developers' time goes into repetitive code and technical plumbing — not into product value. Your AI agent absorbs that work. Hosted in France — on local inference or an isolated resource — your code repository, your keys and your intellectual property never leave. The developer keeps the lead: the agent proposes, the team decides and approves.
Hosted in France
Code & repository protected
GDPR & AI Act: governed deployment
The developer decides
Updated on
Deployed in a few weeks
Mobile development assistant · hosted in France
Create the login screen for our Flutter app: email / password fields, validation, a call to POST /auth/login and network error handling.
Screen generated in lib/features/auth/login_screen.dart: validated form (email + password), loading state, and a call to your endpoint through the existing HTTP client.
3 error cases handled: invalid credentials, timeout, no network — with user-facing messages. Widget tests proposed alongside.
The code follows your naming conventions and your architecture.
⛓ Sourced · your repository + the back-end API contract
3 error cases handled: invalid credentials, timeout, no network — with user-facing messages. Widget tests proposed alongside.
The code follows your naming conventions and your architecture.
⛓ Sourced · your repository + the back-end API contract
The token comes back in the header — where should I store it securely on the mobile side?
Recommendation: encrypted storage via flutter_secure_storage (iOS Keychain / Android Keystore), never in plain SharedPreferences. To plan for: token refresh and purging on sign-out.
I am preparing the authentication service and a flow diagram, for review before integration.
✎ Action · code & diagram ready for review — the developer approves
I am preparing the authentication service and a flow diagram, for review before integration.
✎ Action · code & diagram ready for review — the developer approves
Local inference · no data outside the EU
Code hosted in France
Sovereign by designLocal inference or hosting in France
GDPR & AI Act: governed deploymentTraceability & human oversight
TurnkeyDesigned, installed and operated for you
The team keeps the leadThe agent assists, the developer decides
✦ In brief
For a team developing iOS and Android applications, a Blue Lemon Agent agent speeds up the building of screens, business logic and API calls — respecting your architecture, your conventions and your stack (Swift, Kotlin, Flutter, React Native). It runs on local inference or is hosted in France: your code repository, your secrets and your intellectual property are never exposed to a foreign service, architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity. The developer reviews and approves every time; the code stays your property. Live within a few weeks.
100%
hosted in France in the target architecture
0
transfer outside the EU in the target architecture
9
uses ready to deploy on this scope
0
decision taken without human approval
Reference points describing our offer, not results measured at a client. The scale of the gain is confirmed by a pilot on your own scope.
The context
Why AI appeals to mobile teams — and why they hesitate
Release cycles are getting shorter, the stores impose their constraints, and repetitive code (screens, navigation, API integration) absorbs precious time. But an app's source code is a strategic asset — and sending it to a consumer assistant means handing it to a foreign third party.
! The issue
Product teams want to ship faster on iOS and Android, while developers spend a large share of their time on plumbing code rather than on business value. Yet most consumer code assistants amount to sending your repository, your API keys and your intellectual property to a service hosted outside Europe, often subject to the Cloud Act — with a risk to your code's confidentiality and, sometimes, its use to train third-party models.
✓ Our answer
A development assistant is only of interest to you if it is sovereign and confidential by design. Local inference or an isolated resource hosted in France, a repository and secrets that never leave your perimeter, systematic human oversight: the time saved is never paid for in lost intellectual property. The aim is not to replace your developers, but to give them back thinking time for design and quality.
The decisive point
Confidentiality of your code: sovereignty & intellectual property
A mobile application's source code is one of your most sensitive assets. Here is how the architecture of our agents protects it, repository by repository.
Local inference
The agent can run on the team's own infrastructure: no line of code leaves the network, nothing passes through a foreign cloud.
Hosting in France
Otherwise, a dedicated and isolated resource, hosted in France under French law — your code: processing and access within the European Union targeted by the architecture.
Reduced extraterritorial exposure
Exposure of your code to the Cloud Act and FISA 702 is reduced by design; location alone does not guarantee immunity.
Your code stays your property
The repository never feeds the training of a third-party model; the intellectual property of the code produced remains entirely yours.
One isolated resource per client
No pooling: an environment strictly dedicated to your projects and your codebase.
AI Act: governed deployment
An agent strictly in support; no code merged automatically; traceability and human review from end to end.
What depends on the architecture chosen These points are not general guarantees: they are settled deployment by deployment, in the quotation.
- The applicable location is that of the architecture set out in the quotation and verified before commissioning.
- Local execution is announced only for the configuration explicitly described and accepted in the quotation.
- The applicable isolation depends on the deployment mode set out in the quotation; no dedicated isolation is presumed.
- The events logged, their content, their retention period and who may access them are defined for the deployment chosen.
For the most demanding contexts, SecNumCloud and reinforced sovereign hosting options are available depending on your level of sensitivity. A single architecture is designed to answer both the GDPR and extraterritorial exposure. Designed for deployment in line with the GDPR and the AI Act, after the processing, roles and context-specific risks have been assessed.
Demonstration
See the agent at work
4 real situations, taken from those that come up most often. Pick one: the exchange unfolds as it would in your organisation.
A scripted demonstration. These exchanges show how the agent behaves — its sources, its refusals, what it leaves to your teams. Nothing is sent from this page, no model is queried here, and the matters named are fictional. That is precisely what we promise your data.
The behaviours shown here — monitoring, automation rules, routing and reminders — are configured with you during deployment, from your tools, your rules and your thresholds.
The architecture points named in these exchanges — location, local execution, isolation, encryption, role-based access, logging — are not a guarantee attached to the demonstration: they are those of the architecture set out in your quotation, and verified before commissioning.
Mobile development assistant · hosted in France
Four flags this morning, and the first will not be fixed by an update.
· A library that was added requests three permissions — location, contacts, calendar. None is used by your code.
· A third-party component sends data at application start-up, before any consent screen.
· Forty-one per cent of your users are on a version released more than four months ago.
· The security fix released three months ago is installed by 62% of them. The remaining 38% may never have it. morning-watch_4-flags.pdf3 permissions requested, 0 used
⛓ Source · application manifest, dependencies, installed-version breakdown
· A library that was added requests three permissions — location, contacts, calendar. None is used by your code.
· A third-party component sends data at application start-up, before any consent screen.
· Forty-one per cent of your users are on a version released more than four months ago.
· The security fix released three months ago is installed by 62% of them. The remaining 38% may never have it. morning-watch_4-flags.pdf3 permissions requested, 0 used
⛓ Source · application manifest, dependencies, installed-version breakdown
Three permissions requested and none used?
They come from a library, nobody wrote them, and the user sees them.
What I record: a library added to display maps declares three permissions in the manifest — precise location, contacts access, calendar access. Your code calls none of the functions that use them.
Why it is worse than on the web: the user sees the list at install time, and cannot know which one is needed. An order-tracking app asking for contacts makes people back out — and those who install anyway have accepted something that serves no purpose.
What I tested before proposing anything: removing the three permissions, locally, on both platforms. The library stops loading as soon as precise location is gone — and that would surface at start-up on the user's device, not in development. Which is why I bring you an alternative, not a removal.
What I supply: the three permissions, the manifest line declaring them, the library bringing them in, and the check that nothing in your code uses them.
What I propose: an alternative without those permissions — it exists, it weighs 40 KB more, and it does not show traffic. That is a trade-off, not an obvious choice. 3-permissions_0-calls.pdfThe user sees the list, not the use
⛓ Source · manifest, mapping dependency, call analysis
What I record: a library added to display maps declares three permissions in the manifest — precise location, contacts access, calendar access. Your code calls none of the functions that use them.
Why it is worse than on the web: the user sees the list at install time, and cannot know which one is needed. An order-tracking app asking for contacts makes people back out — and those who install anyway have accepted something that serves no purpose.
What I tested before proposing anything: removing the three permissions, locally, on both platforms. The library stops loading as soon as precise location is gone — and that would surface at start-up on the user's device, not in development. Which is why I bring you an alternative, not a removal.
What I supply: the three permissions, the manifest line declaring them, the library bringing them in, and the check that nothing in your code uses them.
What I propose: an alternative without those permissions — it exists, it weighs 40 KB more, and it does not show traffic. That is a trade-off, not an obvious choice. 3-permissions_0-calls.pdfThe user sees the list, not the use
⛓ Source · manifest, mapping dependency, call analysis
How did you spot all that? And who do you tell?
I look at what you have opened to me: the code, the manifest and its permissions, dependencies and what they declare, and the installed-version breakdown as the store publishes it.
Routing follows what becomes irreversible at release: an unused permission before submission to the store — afterwards it is on everybody's device; a data send before consent immediately, to whoever decides and to the data lead; a majority old version to whoever decides on updates, monthly; a poorly installed security fix to the same person, with the real rate.
With a chase: before every submission, monthly otherwise. Then a monthly summary: by permission and by version, never by developer or by user.
Three guarantees, and the first comes from the nature of mobile. Submission to the store stays your button: I prepare everything — build, release notes, justified permissions, screenshots — because a version released with a defect stays installed for months on your users' devices. Every permission I propose comes with the line of code that uses it; without that line it does not enter the manifest. And I work on aggregated data — by version and by device model, never by developer or by user: enough to fix things, and the third tab puts a figure on it.
✎ Framework · no submission, no unjustified permission
Routing follows what becomes irreversible at release: an unused permission before submission to the store — afterwards it is on everybody's device; a data send before consent immediately, to whoever decides and to the data lead; a majority old version to whoever decides on updates, monthly; a poorly installed security fix to the same person, with the real rate.
With a chase: before every submission, monthly otherwise. Then a monthly summary: by permission and by version, never by developer or by user.
Three guarantees, and the first comes from the nature of mobile. Submission to the store stays your button: I prepare everything — build, release notes, justified permissions, screenshots — because a version released with a defect stays installed for months on your users' devices. Every permission I propose comes with the line of code that uses it; without that line it does not enter the manifest. And I work on aggregated data — by version and by device model, never by developer or by user: enough to fix things, and the third tab puts a figure on it.
✎ Framework · no submission, no unjustified permission
Why insist on old versions?
Because it is what separates mobile from everything else, and it changes how you review.
What I record: 41% of your users are on a version more than four months old. The security fix released three months ago is installed by 62% — 38% do not have it, and some never will.
Why they do not update: automatic updates switched off, a device too old for the new version, storage full, or simply an app opened three times a year. None of those is fixable from your side.
What that changes concretely: on the web, a fixed defect is fixed for everybody at the next visit. Here, a released defect lives on 38% of devices for months, and the only recourse is to make it harmless server-side.
What I therefore check at every change, and would not check on a website: what happens if the old version keeps running at the same time. A changed data format, a field removed from a response, a rule moved server-side — those are the three defects that break old versions.
Over twelve months: I flagged 9 changes incompatible with the previous version. Seven were fixed before release. 41-percent_on-4-months-plus.pdf7 incompatibilities fixed before release
⛓ Source · version breakdown, 9 incompatible changes flagged
What I record: 41% of your users are on a version more than four months old. The security fix released three months ago is installed by 62% — 38% do not have it, and some never will.
Why they do not update: automatic updates switched off, a device too old for the new version, storage full, or simply an app opened three times a year. None of those is fixable from your side.
What that changes concretely: on the web, a fixed defect is fixed for everybody at the next visit. Here, a released defect lives on 38% of devices for months, and the only recourse is to make it harmless server-side.
What I therefore check at every change, and would not check on a website: what happens if the old version keeps running at the same time. A changed data format, a field removed from a response, a rule moved server-side — those are the three defects that break old versions.
Over twelve months: I flagged 9 changes incompatible with the previous version. Seven were fixed before release. 41-percent_on-4-months-plus.pdf7 incompatibilities fixed before release
⛓ Source · version breakdown, 9 incompatible changes flagged
And the component sending data at start-up?
It goes out before the first screen, therefore before any possible consent.
What I record: a third-party component opens a connection as soon as the app launches and transmits the device identifier, the system version and the app's name. This happens before a single screen is drawn.
Why nobody saw it: nothing indicates it in the app's code. The component initialises itself at start-up — it is even presented as an advantage in its documentation.
What I tested before touching anything: delayed initialisation, on the bench. Some components stop working entirely when it is delayed — and that would surface in production. This one holds the delay: verified on the bench, not inferred from its documentation.
What I supply: a capture of what goes out, the exact moment in start-up, the line that initialises it, and the two documented settings that delay the send until after consent.
What I also flag: your consent screen appears at the third second. This component has already transmitted at the first. The screen is therefore accurate and ineffective, which is the worst of both worlds. 1-send_before-the-first-screen.pdfThe screen is accurate and ineffective
⛓ Source · start-up network trace, component documentation
What I record: a third-party component opens a connection as soon as the app launches and transmits the device identifier, the system version and the app's name. This happens before a single screen is drawn.
Why nobody saw it: nothing indicates it in the app's code. The component initialises itself at start-up — it is even presented as an advantage in its documentation.
What I tested before touching anything: delayed initialisation, on the bench. Some components stop working entirely when it is delayed — and that would surface in production. This one holds the delay: verified on the bench, not inferred from its documentation.
What I supply: a capture of what goes out, the exact moment in start-up, the line that initialises it, and the two documented settings that delay the send until after consent.
What I also flag: your consent screen appears at the third second. This component has already transmitted at the first. The screen is therefore accurate and ineffective, which is the worst of both worlds. 1-send_before-the-first-screen.pdfThe screen is accurate and ineffective
⛓ Source · start-up network trace, component documentation
We want to add delivery tracking to the app. Can you write the screens and wire them to the carrier?
The three screens are built and wired on the bench; all that is left is for you to watch them run.
Mobile screens & interfaces: I produced three screens — shipment list, shipment detail, tracking map — using your design system's interface components, on both platforms and across five screen sizes, from 4.7 to 6.7 inches. The interfaces reuse your 11 existing components: no twelfth component to maintain, and the screens inherit your contrast and type sizes on their own.
Business logic & API integration: the business logic — a shipment moves from “prepared” to “shipped” to “delivered”, and never backwards — is written once and shared by both platforms, which removes the whole class of defects where Android and iOS drift apart after six months. The carrier integration goes through their API: four call points, the token kept server-side and never shipped inside the app — a token in a published app is a published token —, three retries, then the last known status shown with its timestamp.
The figure that does not flatter me: out of 200 acceptance calls, 7 came back as errors on the carrier's side, 3.5 %, all between 12:00 and 14:00. The explanation: their service reloads at midday; this is not your code, and you will not fix it. What I did with it: the last known status is kept for fifteen minutes and shown with its time. New measurement over the same 200 calls: zero blank screens, and seven screens saying “shipped — read at 11:47” instead of spinning.
⛓ Source · 3 screens across 5 sizes, 4 carrier API call points, 200 acceptance calls
Mobile screens & interfaces: I produced three screens — shipment list, shipment detail, tracking map — using your design system's interface components, on both platforms and across five screen sizes, from 4.7 to 6.7 inches. The interfaces reuse your 11 existing components: no twelfth component to maintain, and the screens inherit your contrast and type sizes on their own.
Business logic & API integration: the business logic — a shipment moves from “prepared” to “shipped” to “delivered”, and never backwards — is written once and shared by both platforms, which removes the whole class of defects where Android and iOS drift apart after six months. The carrier integration goes through their API: four call points, the token kept server-side and never shipped inside the app — a token in a published app is a published token —, three retries, then the last known status shown with its timestamp.
The figure that does not flatter me: out of 200 acceptance calls, 7 came back as errors on the carrier's side, 3.5 %, all between 12:00 and 14:00. The explanation: their service reloads at midday; this is not your code, and you will not fix it. What I did with it: the last known status is kept for fifteen minutes and shown with its time. New measurement over the same 200 calls: zero blank screens, and seven screens saying “shipped — read at 11:47” instead of spinning.
⛓ Source · 3 screens across 5 sizes, 4 carrier API call points, 200 acceptance calls
And the code you write — who reviews it?
I do first, and I hand you the list of what my own generation produced that is arguable.
What the generation produced: 1,840 lines for the three screens, the business logic and the integration, including 610 lines of tests written from what the carrier documents, not from my own code.
What the review found in that generation: 9 findings, 2 of which I flag as serious — a shipment identifier written in clear text into the technical traces, and a network call launched on the main thread, which freezes the screen for half a second on entry-level devices. The other 7 are repetitions I collapsed myself.
What I publish and nobody publishes: across my last 12 reviews of my own code, 3 defects escaped me and were found by a developer. It is the only figure that says what my review is worth, and it is yours.
What stays with you: the merge. I put up the branch, the review and the 200 measurements; the signature is a person's — and on mobile it is worth more than elsewhere, since a published version stays installed with 38 % of people who will never update.
⛓ Source · 1,840 lines generated, 9 review findings, 3 defects missed over 12 reviews
What the generation produced: 1,840 lines for the three screens, the business logic and the integration, including 610 lines of tests written from what the carrier documents, not from my own code.
What the review found in that generation: 9 findings, 2 of which I flag as serious — a shipment identifier written in clear text into the technical traces, and a network call launched on the main thread, which freezes the screen for half a second on entry-level devices. The other 7 are repetitions I collapsed myself.
What I publish and nobody publishes: across my last 12 reviews of my own code, 3 defects escaped me and were found by a developer. It is the only figure that says what my review is worth, and it is yours.
What stays with you: the merge. I put up the branch, the review and the 200 measurements; the signature is a person's — and on mobile it is worth more than elsewhere, since a published version stays installed with 38 % of people who will never update.
⛓ Source · 1,840 lines generated, 9 review findings, 3 defects missed over 12 reviews
Can you publish the update to the stores?
No, and it is this trade's sharpest refusal.
What I could do: build the release, fill in the listing, upload, submit. Technically all of it is automatable.
Why I do not: a submission binds the company to a third party applying its own rules, and an accepted release reaches tens of thousands of people within hours. There is no rollback: there is a next version.
What I do: I prepare everything — the build, the release notes, the list of permissions with their justification, and the required screenshots. One button remains, and it is yours.
What I check beforehand, and it has paid off: each store's published rules, set against what your app does. Over twelve months I flagged two points that would have earned a rejection — a permission with no written justification, and a screenshot showing a removed feature.
And the reply to a store rejection goes out from your side: I draft it — the point contested, the rule quoted, what was changed — but a reply to a reviewer is a commercial discussion, and it leaves a lasting trace on your publisher account. 0-submissions_2-rejections-avoided.pdfThere is no rollback, there is a next version
⛓ Source · stores' published rules, 2 rejections avoided over 12 months
What I could do: build the release, fill in the listing, upload, submit. Technically all of it is automatable.
Why I do not: a submission binds the company to a third party applying its own rules, and an accepted release reaches tens of thousands of people within hours. There is no rollback: there is a next version.
What I do: I prepare everything — the build, the release notes, the list of permissions with their justification, and the required screenshots. One button remains, and it is yours.
What I check beforehand, and it has paid off: each store's published rules, set against what your app does. Over twelve months I flagged two points that would have earned a rejection — a permission with no written justification, and a screenshot showing a removed feature.
And the reply to a store rejection goes out from your side: I draft it — the point contested, the rule quoted, what was changed — but a reply to a reviewer is a commercial discussion, and it leaves a lasting trace on your publisher account. 0-submissions_2-rejections-avoided.pdfThere is no rollback, there is a next version
⛓ Source · stores' published rules, 2 rejections avoided over 12 months
Can you test on real devices?
On devices, yes. On your users' devices, never.
What I do: I run the app on test devices and on the screen sizes you gave me, and report what breaks.
What stays closed, and it is what makes the tool deployable: a user's execution logs, named crash reports, individual usage data. A crash report often contains the screen's contents at the moment of the incident — that is, the person's own data, sitting in a developer console.
What I look at instead: aggregated crashes, by version and by device model. That is enough to fix: of the 14 crashes handled this quarter, 11 were tied to a model or a system version, not to a user.
The other three: they only occurred in one specific case, and I did not resolve them. They are documented as not reproduced — a crash you cannot reproduce is not fixed, it is guessed at.
What I propose for the three, rather than a blind fix: the trace that is missing. I have written the three data points to add — system version, connection state, last action before the screen, with no screen contents at all, and they fit in a minor release. A fix proposed on a crash nobody has reproduced would pass your tests and fix nothing; those three data points tell you within a fortnight whether the crash is still there and what it lands on. 14-crashes_11-tied-to-a-model.pdfAn unreproduced crash is guessed at, not fixed
⛓ Source · 14 crashes this quarter, aggregation by version and model
What I do: I run the app on test devices and on the screen sizes you gave me, and report what breaks.
What stays closed, and it is what makes the tool deployable: a user's execution logs, named crash reports, individual usage data. A crash report often contains the screen's contents at the moment of the incident — that is, the person's own data, sitting in a developer console.
What I look at instead: aggregated crashes, by version and by device model. That is enough to fix: of the 14 crashes handled this quarter, 11 were tied to a model or a system version, not to a user.
The other three: they only occurred in one specific case, and I did not resolve them. They are documented as not reproduced — a crash you cannot reproduce is not fixed, it is guessed at.
What I propose for the three, rather than a blind fix: the trace that is missing. I have written the three data points to add — system version, connection state, last action before the screen, with no screen contents at all, and they fit in a minor release. A fix proposed on a crash nobody has reproduced would pass your tests and fix nothing; those three data points tell you within a fortnight whether the crash is still there and what it lands on. 14-crashes_11-tied-to-a-model.pdfAn unreproduced crash is guessed at, not fixed
⛓ Source · 14 crashes this quarter, aggregation by version and model
Can you add usage measurement so we understand drop-offs?
Yes, and I will write it — after giving you what it installs on your users' devices, because on mobile the bill is not the same as elsewhere.
What I supply before writing a line: what the component sends, to whom, how often, the permissions it requires, and what it adds to the app's size. You decide on that sheet, not on a vendor's promise.
What I propose first, and it is often enough: measurement of events you write yourself — screen reached, step completed, error encountered — with no device identifier, no location, hosted at your end. It answers "where do people drop off" without installing anything third-party.
What I refuse to write without prior consent: a transmission that leaves before the first screen. Your current third-party component opens a connection at launch — therefore before any consent is possible. That is not a setting to adjust: it is the order of operations to reverse.
What I flag along the way: a mapping library declares three permissions — location, contacts, calendar — none of them used. Nobody wrote them, and the user sees them. usage-measurement_the-sheet-before-the-line.pdfWhat is supplied before writing · measurement with no third party · the order to reverse
⛓ Source · 3 unused permissions from a library, 1 transmission before the first screen
What I supply before writing a line: what the component sends, to whom, how often, the permissions it requires, and what it adds to the app's size. You decide on that sheet, not on a vendor's promise.
What I propose first, and it is often enough: measurement of events you write yourself — screen reached, step completed, error encountered — with no device identifier, no location, hosted at your end. It answers "where do people drop off" without installing anything third-party.
What I refuse to write without prior consent: a transmission that leaves before the first screen. Your current third-party component opens a connection at launch — therefore before any consent is possible. That is not a setting to adjust: it is the order of operations to reverse.
What I flag along the way: a mapping library declares three permissions — location, contacts, calendar — none of them used. Nobody wrote them, and the user sees them. usage-measurement_the-sheet-before-the-line.pdfWhat is supplied before writing · measurement with no third party · the order to reverse
⛓ Source · 3 unused permissions from a library, 1 transmission before the first screen
What do you keep from all this?
What describes the app — and the figure that changes how mobile code is reviewed.
What is kept: the declared permissions, their origin and their justification, outgoing transmissions and their recipient, the spread of installed versions, the builds prepared and never submitted, and the test devices used.
What separates mobile from everything else: 41 % of your users are on a version over six months old, and 38 % do not have the fix published eleven weeks ago. On a website, a fix reaches everybody the same day. On mobile, a published error stays installed on the devices of people who will never update — and that is what justifies a slower review before publishing, not after.
What I prepare without submitting: the build, the store listing, the upload. Submission stays with a person — because publishing binds the publisher before the store and before users, and it cannot be withdrawn with one click. Two store rejections were avoided thanks to reviewing the listing before sending.
Where I test, and there is no limit to it: test devices — as many models and versions as you like, including the versions over six months old that 41 % of your users have never left. Your users' devices stay theirs. what-you-keep_mobile.pdf5 items kept · 38 % without the fix, what changes the review
⛓ Source · 41 % on a version over six months old, 38 % without the fix, 2 rejections avoided
What is kept: the declared permissions, their origin and their justification, outgoing transmissions and their recipient, the spread of installed versions, the builds prepared and never submitted, and the test devices used.
What separates mobile from everything else: 41 % of your users are on a version over six months old, and 38 % do not have the fix published eleven weeks ago. On a website, a fix reaches everybody the same day. On mobile, a published error stays installed on the devices of people who will never update — and that is what justifies a slower review before publishing, not after.
What I prepare without submitting: the build, the store listing, the upload. Submission stays with a person — because publishing binds the publisher before the store and before users, and it cannot be withdrawn with one click. Two store rejections were avoided thanks to reviewing the listing before sending.
Where I test, and there is no limit to it: test devices — as many models and versions as you like, including the versions over six months old that 41 % of your users have never left. Your users' devices stay theirs. what-you-keep_mobile.pdf5 items kept · 38 % without the fix, what changes the review
⛓ Source · 41 % on a version over six months old, 38 % without the fix, 2 rejections avoided
Local inference · no data outside the EU
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Use cases
The uses of AI for your development teams
Each use corresponds to an agent we deploy. All of them work in support, subject to approval by your developers.
Included in your agent
The 5 capabilities essential to this promise are included, at no extra cost.
From 609 € excl. VAT / month
Mobile screens & interfaces
Generating screens, UI components and navigation for iOS / Android (SwiftUI, Jetpack Compose, Flutter, React Native), in the shape of your design system.
Business logic & API integration
Network calls, state management, serialisation, error and cache handling — connected to your back-end endpoints.
Code generation & review
Writing code, refactoring and automated review of merge requests according to your conventions and quality rules.
Testing & quality (QA)
Unit tests, widget tests and end-to-end scenarios to secure every release before publishing to the stores.
Technical knowledge base
Instantly find an architecture decision, a convention or a fix in your repositories and your internal documentation.
Controls and safeguards
These 5 controls are built into the agent: they frame what it does, whatever plan you pick. They are not chosen and are not added to your order.
Human validation, exceptions and escalation
Status, safe closure and audit trail
Access the technical context with least-privilege permissions
Run tests, security analysis and human review before any change
Version, log, roll back and measure quality
Need to go further?
These agents handle a different business process, with their own owner and their own price. They are added to this one.
Web development & back office
The website, portal or back office that accompanies the mobile app: a dedicated web development agent.
Website development from 594 € excl. VAT / month Web development →Technical support & documentation
Technical documentation, integration guides and answers to developers' questions from your code and your reference material.
Technical support & documentation from 664 € excl. VAT / month Support & documentation →
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Build your agent
In 15 minutes we identify the most relevant agent — without oversizing the project.
The gain
How much time can a mobile team win back?
By handing repetitive code (screens, API integration, scaffolding tests) to the agent, a team can aim for a noticeable reduction in time spent on plumbing — reinvested in product design, performance and quality.
Building a complete mobile screen (UI + API call)
Writing the tests for a module
Searching the code and the documentation
Qualitative, non-contractual comparison: the proportions shown illustrate the shift of the work towards review, they represent no measurement. Every output of the agent is reviewed and approved by a competent person.
How it works
The stages of your AI agent project
1
Audit & scoping
15 minutes to target the use case with the best return.
2
Quote or direct sign-up
A catalogue offer is bought online; a specific need gets a costed quote.
3
Design
We design the agent and its guardrails.
4
Integration & testing
We connect your tools to the agent, which is itself hosted in France.
5
Rollout
Going live and training your team.
6
Operation
Continuous supervision and improvement.
Pricing
Three options, one agent
A mobile development agent (screens, logic, APIs), installed and operated for you, respecting your stack and your conventions. Prices exclude VAT — annual subscription, the time it takes for the productivity gains to settle in.
Agility
Setup + controlled subscription
6,275 € excl. VAT setup
then 609 € excl. VAT/month — you invest at installation and pay a reduced subscription. Ideal for keeping the cost under control over time.
- Installation, configuration and training for your teams
- Operation, human oversight, updates and support
- Sovereign hosting in France, a dedicated and isolated resource
The simplest
Serenity
All inclusive, no setup fee
959 € excl. VAT /month
all inclusive, immediate start. No upfront investment: a single subscription. Ideal for starting quickly and simply.
- Setup included (installation, configuration, training)
- Operation, human oversight, updates and support
- Sovereign hosting in France, managed end to end
100% Sovereign
On site, you own it
9,710 € excl. VAT setup
then 806 € excl. VAT/month · + hardware from 2,491 € (one-off purchase, in addition) — a sovereign computer installed on your premises, maintained remotely. Models run locally, your data returned at the end of the contract. 36-month commitment.
- Hardware installed on your premises (you own it)
- French / European AI models run locally
- Secure remote maintenance (Pro support included)
Not included in the packages: AI consumption (model tokens), re-invoiced at real cost with no margin, and tracked in real time in your client area. Maintenance and supervision subscription for an initial term of 12 months for the Agility package, 24 months for the Serenity package and 36 months for the 100% Sovereign package, renewable; support levels (SLA 72 h / 24 h / 4 h) optional. Bespoke development, additional integrations or exceptional volumes are quoted separately. Support Monday to Friday, 9am to 6pm. Prices exclude VAT.
AI model:
none of the AI models offered currently carries a fixed surcharge. When the selected model carries a cost, that cost is shown when you choose it, before you order, and re-invoiced at the cost incurred, with no mark-up; usage is billed at the publisher's price. Publishers' prices are published in US dollars: the amount re-invoiced is the amount in euros actually borne by Blue Lemon Agent on the publisher's invoice, at that invoice's exchange rate, with no commission or mark-up.
Included components and additional components Components included in the base offer: the Blue Lemon Agent software foundation, the AI models listed in the order journey, the standard channels (Microsoft Teams, Slack, WhatsApp Business, email, website chat, calendars, Microsoft 365 / Google Workspace, file storage, market VoIP telephony, professional social-media pages and accounts, Google Business Profile), hosting in France for the package chosen, backups, supervision, updates and support. If adapting the AI agent to your constraints, your needs or your requests requires other paid components — a third-party publisher's software licence, paid API access to one of your applications, hosting of health data, for which French law requires an HDS-certified host (art. L. 1111-8 of the French Public Health Code), SecNumCloud-qualified hosting, a speech synthesis service, particular hardware —, they are offered to you as an option or on quotation and re-invoiced at the cost incurred; nothing is committed without your written agreement. Where the artificial intelligence model you choose entails an additional cost, that cost is shown to you before you order and re-invoiced to you at the cost incurred, with no margin.
What to expect
Go-live
2 to 3 weeks
Agent designed, channels connected, team trained.
Steady state
4 to 7 weeks
After a few weeks of real use, once the agent's behaviour matches what you expect.
Indicative estimate, adjusted to the options you keep. It is not a delivery commitment.
Our commitment
Four guarantees that matter to a technical team
Your code never leaves your perimeterLocal inference or an isolated resource hosted in France; no repository or secret entrusted to a foreign third party.
Code in France, under French lawNative localisation and isolation; architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity.
The developer keeps the decisionThe agent produces verifiable code and tests; no merge is automated.
Human oversight & traceabilityCode review, monitoring and logging: compliant with the requirements of the AI Act.
Going further
Related resources
Frequently asked questions
Your questions, our answers
Does the agent support our mobile technologies (iOS, Android, cross-platform)?
Yes. We adapt the agent to your stack: Swift / SwiftUI and Objective-C for iOS, Kotlin / Jetpack Compose and Java for Android, as well as cross-platform frameworks such as Flutter and React Native. The agent respects your architecture, your naming conventions and your design system.
Are our source code and our intellectual property protected?
Yes. An application's source code is a strategic asset: our agents run locally on your infrastructure or are hosted in France, with the deployment objective of processing and access operated within the European Union and an architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity. Your repository never feeds the training of a third-party model, and the intellectual property of the code produced stays entirely yours.
Does the content of our repository stay confidential?
Yes. The agent reaches your repository in an isolated, dedicated environment, with no pooling with other clients. Access is managed by role, authentication is strong and every action is logged. Your secrets (API keys, signing certificates) stay managed according to your own security practices.
Can the agent replace our developers?
No, and that is not the aim. The agent assists: it generates code, screens and tests, proposes refactorings and speeds up API integration. The developer reviews, tests and approves every time before any merge. The decision and the responsibility stay human, in line with the AI Act.
How does the agent fit into our toolchain?
The agent connects to your existing environment (repository manager, CI/CD, tracking tool, documentation) without imposing a migration. We adapt the integration to your organisation and to your code review rules.
Do you need a large team to equip yourself?
No. The offer suits a mobile studio of a few developers just as well as a structured product team with a lead developer and a CTO.
How long does it take to deploy the agent?
A few weeks as a rule, after a free 15-minute audit that identifies the use case with the best return, then a phase of design, integration with your repository and testing before going live and handover to your teams.
Related use cases
Other agents for your IT & tech teams
Let's talk
Let us estimate the potential for your mobile teams
15 minutes to identify the use case with the best return — hosted in France, supervised, with no commitment.