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Software engineering

When an industry expert leads development, AI solves real problems.

We built Ailomera and AI.ZET together with domain experts, people who have worked in logistics and clinical research for years and know firsthand the processes these products automate.

  1. A domain expert as Product Owner

    Development is led by someone with many years of experience in the field whose problem we are solving. They know its business processes and biggest day-to-day challenges well, and what needs to be solved and automated.

  2. Our experience in software development

    Decades of software development and daily work with the newest tools, AI included. That is how we turn an expert’s knowledge into a product.

  3. Specially trained AI models

    Using that knowledge and experience, we train AI models that automate specific business processes in the field.

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Your AI and Technology Partner

Enterprise AI built for real business challenges

We design and develop domain-specific AI solutions tailored to real operational needs, from intelligent document processing and workflow automation to local LLMs, RAG systems, AI assistants and human-in-the-loop solutions.

Our solutions can be deployed on-premise or within client-controlled infrastructure, ensuring data privacy, security, regulatory compliance and full control over sensitive information.

Part of engineering, not apart from it

The AI team sits inside our engineering organization rather than beside it. That is why an AI project here reaches production instead of stopping at the demo, and the people who integrate AI with your ERP are people who have done it before.

How we work with AI

Applied AI, not an R&D experiment.

Our engineers use in-house AI tooling for analysis, code, tests and documentation, so more of your roadmap ships per sprint. AI speeds up the work, and quality stays in our engineers’ hands: every line of code is reviewed and tested before it reaches you.

  1. 01

    A dedicated AI division

    A standing team, funded to research AI tools, models and methods full time, not a side project between client deliveries.

  2. 02

    Tested on real problems

    Methods tried on document-heavy, regulated work bound to existing systems. What proved itself went into the final product.

  3. 03

    Developed with domain experts

    AI projects built for clinical research and logistics, in collaboration with leading domain experts.

  4. 04

    Captured in ADwAI

    Patterns, security rules and reusable components, kept in our AI platform so each new project starts from what the previous one taught us.

AI in your everyday work

The platform behind our products, applied to your operations.

AI.ZET and Ailomera are two applications of our ADwAI platform, not the limit of what it can do. For long-term clients we have already automated document and email intake, data validation and data entry between systems: work tailored to each client.

Today

  • Documents and email handled by hand, person by person
  • Data rekeyed between systems that do not talk
  • Checks done late, by whoever has capacity
  • Throughput limited by the number of people

Automated

  • Intake classified, extracted and routed automatically
  • Systems connected, data entered only once
  • Validation applied consistently, with review where it matters
  • Capacity able to grow without a proportional rise in hiring

Every improvement to ADwAI reaches existing clients first, in the systems already running for them.

How it starts

Start with one workflow, not a transformation programme.

Where we have delivered projects in your domain before, your pilot project carries forward what they taught us: validation rules, edge cases, integration patterns. If you are an existing client, the pilot can run inside your current delivery model.

  1. 01

    AI readiness discussion

    A working session that brings your operational and technical people together with our AI software architects and AI software engineers, to find where using AI actually pays off.

  2. 02

    Clearly scoped pilot project

    One workflow, with the scope and the success measures agreed up front. Built on ADwAI, so it starts from working components, not a blank page: you gain security, reliability and speed, at a lower overall project cost.

  3. 03

    Validation

    Results measured against an agreed baseline, with your reviewers involved, and the deployment choice confirmed: cloud, private cloud or on-premise.

  4. 04

    Production and scale

    Integration into your live environment, rollout and long-term support. Then the next workflow.

That combination lowers the two risks that matter most:

  1. A pilot project that never reaches production: your project will be built on the platform AI.ZET and Ailomera already run on.
  2. A partner who disappears when you need support: our teams often stay with one client for years, and we are still here to maintain what we built.