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How we work

One team from design to production

The engineers who map your problem are the ones who design, build, deploy and run the system. There are no hand-offs between consultants, architects and developers.

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Every skill a system needs, in one team

  1. AI engineering

    Agents, retrieval over documents, MCP servers and evaluation.

  2. Data engineering

    Pipelines and data transformation.

  3. Cloud

    Infrastructure as code, deployment pipelines and identity.

  4. Enterprise applications

    SAP, NetSuite, Dynamics 365 and Odoo.

  5. Governance

    Access control and audit trails.

  6. Product and UX

    Dashboards and apps people use every day.

Four habits on every project

01.

Always measure

Every AI-related change passes an evaluation before it ships. On the catalogue assistant every change is tested on 50 real questions, and the social-data pipeline has twelve release gates.

02.

AI only where it is needed

Ordinary code where the answer must be exact, models where language or images are involved. In the pipeline, rules match genres and keywords and a model reads posts only for artist names; the KPIs of the distributor are defined once, in SQL.

03.

Governance by design

Access rules, policies and audit from the first sprint. The catalogue assistant reads the ERP as the signed-in user, and the KPI connector acts as the user who asks and can only read.

04.

AI-native delivery

Our engineers direct coding agents, and a separate pass checks everything the agents write.

AI evaluation before every release

The evaluation is how we show that a system works. The same four steps run on every project, with tools adapted to the problem.

  1. Step 1

    Agree what good means

    We agree with you, before we build, which answers count as right and how often they must be right.

  2. Step 2

    Build the test set from real cases

    Questions, documents and records taken from your own work.

  3. Step 3

    Measure every change against a baseline

    Each change is compared with the version before it, so a regression shows before release.

  4. Step 4

    Release on the result

    A change ships only when it meets the bar.

Platforms we build on

  1. Microsoft Azure

    The production base of every system we deliver.

  2. AWS

    Cloud services for clients who run on Amazon Web Services.

  3. Claude, by Anthropic

    The platform our AI training runs on.

  4. Microsoft Dynamics 365

    Business Central, one of the ERPs our systems connect to.

  5. Odoo

    The ERP Velais itself runs on, and one our systems connect to.

  6. SAP, NetSuite and ElevenLabs

    SAP Business One and NetSuite, which our systems read from, and ElevenLabs for voice AI.

What we do not do

Our work is AI engineering and AI training, so printers, networks and computers stay with your IT team. After a course, Claude runs on your company's own licence, because we do not resell licences. The training ends with a working skill for each person and no certificate.

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