Track App Events
Monitor important user actions within your mobile application.
We develop mobile applications, connect analytics, integrate Google Ads, and set up reliable conversion tracking — helping you understand performance and make better marketing decisions.

Understand how users interact with your app and which marketing activities drive valuable results. We configure analytics and conversion tracking to provide your team with clear, actionable data.
Monitor important user actions within your mobile application.
Send relevant conversion data to connected advertising platforms.
Access the key metrics needed to evaluate app and campaign performance.
Create clear reports that help teams understand results and make informed decisions.
Connect your mobile app data with Google Ads to improve campaign measurement and understand which ads lead to meaningful actions.
Securely connect the relevant Google Ads account to the platform.
Match important in-app actions — such as registrations, purchases, or subscriptions — to the corresponding Google Ads conversions.
Transfer configured conversion data so campaign results can be measured more accurately.
View relevant metrics and reports through a clear, straightforward workflow.
Flash 4 GmbH is organised as an engineering studio rather than a staffing firm. Every engagement is led by senior practitioners who are accountable for the outcome from the first conversation to the system that runs in production. There is no account manager between the client and the people writing the code.
We work on problems where software materially affects how a business operates: internal platforms that must not fail, data systems that inform decisions, customer-facing products that carry a brand, and infrastructure that has to be secure, observable, and affordable to run for years rather than quarters.
Full-lifecycle development of web, mobile, and internal software — from research and interaction design through to code, release, and operation.
Cloud-native platforms on AWS, Google Cloud and Azure, with declarative infrastructure, automated delivery, and clear cost governance.
Warehousing, streaming, and analytical systems that turn operational data into a durable asset the business can actually query.
Language models, retrieval systems, and workflow automation applied only where the mathematics and the economics both work out.
Threat modelling, secure development practices, identity, and continuous verification of systems already in production.
Technical audits, architecture review, and phased renewal of legacy systems whose original context has moved on.

Transformation programmes fail when they are framed as marketing exercises. We approach them as engineering projects: identify the parts of the business that create real friction, quantify the cost, and rebuild those parts on foundations that are documented and observable.
The output is a running system and a team that understands it — not a slide deck. Change that survives contact with the operating reality of the organisation.

Our engineers build web, mobile, and back-end systems in the mainstream languages of the last two decades. What matters is not the choice of framework but the discipline behind the code: clear boundaries, testable interfaces, and honest naming.
Every codebase we produce ships with structured logging, a test strategy proportionate to the risk it carries, and documentation a new engineer can use to become productive without an oral tradition.

Cloud platforms only pay off when the entire environment can be described, reviewed, and reproduced. We build infrastructure as code from day one, use immutable delivery pipelines, and treat the cloud account as software artifact rather than a shared drive.
Modern organisations already produce more data than they can interpret. The engineering problem is not collection — it is curation, semantics, and access. We build data platforms whose warehouse tables, metrics definitions, and dashboards can be trusted by finance, product, and operations at the same time.
Modelling in dbt, streaming through Kafka, storage in the warehouse of the client's choice, and observability of the pipelines themselves — because a broken metric is worse than no metric.


Security is treated as part of how software is built, not as a gate at the end. Threat models are written alongside the architecture; identity, secrets, and dependencies are managed programmatically; production systems are monitored for the specific failure modes that matter to the business.
We design and deploy machine-learning and language-model systems where the underlying problem genuinely benefits from them. Retrieval, classification, extraction, summarisation, internal copilots, and workflow automation are the areas where the mathematics is stable and the return is measurable.
We are equally comfortable declining an AI feature that does not survive a cost, latency, or accuracy analysis.

We start with the business context: constraints, users, existing systems, and the outcomes that matter. No implementation is proposed before it is understood.
A written technical plan describes structure, interfaces, data flow, and trade-offs. It becomes the shared reference for everyone involved in the work.
Small, integrated increments are shipped continuously. Every change is observable, documented, and reversible in production.
The system is instrumented, monitored, and iterated. Runbooks, dashboards, and post-mortems keep responsibility clear once the software is live.
Technology choices are made per engagement. This is a truthful inventory of what we currently operate in production for clients — not a wish list.
A feature is considered done when it is deployed, monitored, documented, and reversible. Not when it is merged. Not when it is demoed.
Every engagement carries an explicit definition of quality — covering testing depth, code review, operational readiness, accessibility, and performance budgets — that is agreed before code is written.
"We would rather build something small and correct than something large and impressive. Every line of code we leave behind will be read, patched, or removed by someone else — and that someone deserves clarity."
A discrete system with defined outcomes, a written architecture, and a fixed timeline. Suited to greenfield products and well-scoped platforms.
A senior team is retained for a rolling period. The client sets priorities monthly; we deliver against them. Suited to organisations without an internal engineering leadership layer.
Our engineers integrate into a client team, follow their process, and raise the standard of the internal codebase and practice.
A short engagement to answer a technical question that a business decision depends on — architecture, vendor selection, or system audit.

We prefer to begin with written correspondence. A short summary of the context, the problem, and the outcome you have in mind is enough to understand whether the studio is a fit.