We don't sell an AI product. We make your development processes more efficient.

Your company builds software for demanding technical products – and you know AI can make your development faster. What you're missing isn't another tool demo, but someone who steps into your processes: analysing where time drains away, enabling your developers and putting guardrails in place. That's exactly what we do – measurably, in your existing stack, without turning your operation upside down.

Who this page is for

You lead development or engineering at a mid-sized industrial company: big enough for your own developer teams and a budget – too small for a dedicated AI department. Your people are good. But between "tried ChatGPT privately" and "AI works productively in our development process" lies a gap that nobody closes on the side of day-to-day business.

Do you recognise these four problem areas?

The Copilot licences are in place – the impact is unclear.

Some developers swear by AI assistants, others ignore them. Usage patterns, best practices and honest measurement are missing.

Your domain knowledge sits in tools and people's heads.

Requirements in the ALM system, architecture in the wiki, history in tickets, experience with a few senior developers. Every search costs hours, every onboarding takes months.

There are no guardrails.

Which AI tools may be used by whom, with which code and which data? Without guidelines, every use remains an unquantified risk – with them, it becomes scalable.

From above comes the question: "What are we doing with AI?"

Management expects a plan, the team wavers between curiosity and scepticism – and both land on your desk.

What we do: field work in four building blocks

No off-the-shelf product, no standard package. We work in your processes, with your people, in your stack.

Assessment of your development processes

We map your development cycle and find the spots where time drains away: routine work alongside coding, research, documentation, knowledge access. You get prioritised areas of application with an effort/benefit assessment – not slides, but an actionable sequence.

Process mapping Prioritised areas of application Effort/benefit assessment

Developer enablement

Coding assistants like Copilot in Visual Studio Code or Claude Code only unfold their value with the right introduction: usage patterns per task type, training on real tasks from your code, best practices that stay in the team. We build multipliers – an internal AI community that carries on once we're gone.

Usage patterns & best practices Training on real tasks Internal multipliers

Building a knowledge base

We structure your domain knowledge – from tools, documents and people's heads – into a curated knowledge base on which an assistant works that answers exclusively from this vetted knowledge, not from open model knowledge. Traceable, low on hallucination, and it secures the knowledge of colleagues who are foreseeably leaving. We use this approach ourselves every day in development work – it was our tool before it became our offering.

Curated knowledge base Assistant with source binding Knowledge preservation

Guidelines & governance

Clear rules make AI use scalable instead of risky: which tools, which code, which data, which review obligations. We develop usage guidelines for your development, clarify GDPR and confidentiality questions, and support the rollout so that works council and team come along – cultural change instead of decree.

AI usage policy GDPR & confidentiality Team acceptance

How we work together: two formats

Pilot project

The Process Pilot

The entry point with proof.

◷ 5–10 days

Together we pick a concrete, well-defined bottleneck, measure the baseline and solve it. You see at a real point in your process what AI delivers for you – before you decide on more.

Working solution in use Before/after measurement Documentation & handover Honest recommendation for the next step
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Ongoing

Ongoing Sparring

For teams who build themselves and want an experienced sparring partner at their side.

◷ flexible as needed, remote

Architecture reviews, knowledge transfer on LLMs and agent design, guidance on building the knowledge base, evolving your AI strategy. Your team stays the maker – we are the corrective with hands-on experience.

Regular sparring sessions Architecture & concept reviews Knowledge transfer into the team
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In both formats: after each stage, you decide whether and how it continues. And if a simple script is enough for your problem, we say so – we don't build AI agents for the sake of AI.

Why TRAIL1

We do this ourselves every day.

Our founder works every day in the development practice of the automotive industry and introduces AI-supported ways of working there in practice – from coding assistants to a curated knowledge base. You get hands-on moves from everyday development work, not slides about AI.

We come from software development.

Infotainment for premium OEMs, IT consulting, digitalisation for mid-sized companies. Grown system landscapes, regulatory obligations and confidentiality are our everyday, not our surprise.

Enablement instead of dependency.

Our goal is for your team to eventually no longer need us. Knowledge is transferred during the collaboration, not afterwards in a training session.

Data protection from the start.

Your code and your domain knowledge are your capital. Every solution is designed to be GDPR-compliant and NDA-friendly – local processing where necessary, EU cloud where possible.

Let's find out in 30 minutes where the greatest leverage lies in your development – and which format suits you.

A no-obligation introductory call – we listen, ask questions, and honestly assess how we can help.

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30 minutes
Free of charge
Confidential