ARSoftware helps software and consulting companies adopt AI-native engineering practices: agentic coding workflows, AI tool selection and rollout, quality assurance for AI-generated code, and hands-on team enablement.
Three kinds of company facing the same question: how does AI get into everyday engineering work without quality and traceability suffering?
You build your own product, and individual teams already work with AI tools – just without shared rules. We help turn scattered experiments into a practice the whole team can rely on: which tools are in use, how project context and prompts are maintained, and what a review looks for before generated code counts as production-ready.
Your developers work inside client projects, often under your clients' own security and compliance requirements. The job here is to introduce AI tools so that their use stays compatible with confidentiality agreements and those requirements – and so that your teams can show what the change actually delivered.
You ship many smaller projects in short cycles, often across changing technologies. Agentic workflows pay off mostly at the start of a project: scaffolding, recurring integrations, tests. We show which steps are worth automating and where writing the code yourself is still faster.
Our consulting covers four topics that belong together. Which of them takes priority depends on where your team stands today.
We set up agentic coding in your repository: project context, tool access, the limits an agent operates within, and how to handle long-running tasks. That includes drawing the line between tasks worth delegating and tasks your team keeps doing itself.
The market for AI engineering tools moves fast. Instead of an off-the-shelf recommendation, we compare the candidates against your own code and against tasks from your everyday work. What follows is a rollout plan: licences, access rights, the order in which teams come on board, and which parts of your source code may reach a vendor at all.
AI-generated code arrives faster than a team can review it – that is where the real bottleneck sits. We define checks that hold up under that pressure: mandatory test coverage for generated sections, static analysis in CI, rules on the size of individual changes, and clear ownership of every approval.
Tools alone do not change how people work. We work with your teams on real tasks from your backlog: when an agent is worth using, how to phrase the instruction, when to stop and write the code yourself. The results go into a guide your team can maintain without us.
This comes out of our own engineering work: ARSoftware builds AI software and ships its own apps. What we recommend, we use ourselves – tools we have not worked with, we do not recommend.
An engagement with ARSoftware runs in three steps: workshop, pilot, rollout. Each step has its own deliverable, and after every step you decide whether to continue.
One or two days with your developers: where things stand, which tools are already in use, and concrete tasks from your backlog. The result is an assessment of where agentic workflows hold up in your context – and where they do not.
One team, one clearly scoped repository, a fixed time frame. We set up the tooling and the review gates and stay alongside the first tasks. How you will measure success is agreed before the pilot starts.
What proved itself in the pilot moves to further teams – with a written guide, onboarding, and review rules adjusted to fit. From there your team carries on independently; longer support is available but not a condition.
Agentic coding is a way of working in which an AI agent handles a development task across several steps on its own: it reads the existing code, writes changes, runs the tests and corrects itself based on the results. What separates it from plain code completion is that autonomy – the agent pursues a goal over multiple steps, while the developer sets the task, defines the limits and signs off on the result.
AI consulting pays off most for teams that already use AI tools but have no shared rules – there the gain is not access to tooling; it is the way of working. Typical cases are software companies with their own product, IT consultancies working in client projects, and digital agencies with short project cycles. If a team has no experience with these tools at all, a single workshop is a more sensible entry point than a larger programme.
No – but the work shifts noticeably, and that should not be played down. An agent can take over large parts of the implementation today; what it does not take over are the decisions about what gets built, whether the architecture holds, and whether a result belongs in production. In practice that means less time on individual sections of code and more time on specification, architecture and review – and higher demands on experience, because reviewing unfamiliar code is harder than writing your own. Teams whose work consists mostly of simple, repetitive tasks feel the shift first.
We assure the quality of AI-generated code with the same means as for handwritten code, applied more strictly because more code arrives. Concretely: automated tests that are mandatory for generated sections, static analysis and type checking in the CI pipeline, review by a human, and a limit on the size of a change so that reviewing it stays feasible at all. On top of that comes one organisational rule: only someone who has understood the change may approve it. Without it, even the best toolchain does little.
An engagement starts with a workshop inside your team, usually one or two days. In it we look at where things stand, work through real tasks from your backlog, and record where agentic workflows hold up and where they do not. If the approach holds, a pilot follows with one team and one clearly scoped repository; only after that does a rollout to further teams make sense. We estimate the effort for the next step each time – you do not commit to a whole programme up front.
Tell us briefly what you are working on. You get an honest assessment of whether AI is the right fit, and what a sensible first step looks like.
Or email us directly: contact@arsoftware.tech
