Data & Technology
We build AI for clear tasks such as reading invoices, sorting documents, forecasting and spotting unusual transactions. Before we start we agree how the result will be measured, and we tell you when a simpler solution will do.
AI works well when the task is clear and the result can be measured. Before anything is built we assess the task and the data, and you do not invest in AI where a simpler rule or routine would do.
Partner
We often build on Claude, for example to read documents and produce suggestions that a person approves.
About our partnershipLocal models
When confidentiality, regulation or sensitive material require the data to stay with you, we run the language models in your own environment.
We give you a straight assessment and tell you if a simpler solution will do.
Invoices, receipts, contracts or orders keyed in by hand. AI can read and suggest while a person approves.
Sales, stock or churn are followed up after the fact. With enough history, a model can give forecasts to plan by.
Confidentiality or regulation requires the data to stay in your own environment. We then build with local models that run in your environment.
Trying to solve problems that are too broad all at once
Investing in AI without goals that can be measured
Underestimating how much data quality matters
Automating decisions that a person should approve
Our approach
When AI works best
We go through the task, the data and how the work is done today, and assess whether AI fits.
Before anything is built, we agree how the result will be measured, such as accuracy, time saved or forecast error.
A prototype on your own data shows whether AI handles the task better than the way you work today.
If the prototype holds up, we build the solution into your systems, with a person approving where needed.
We track the result against the criteria and adjust the model as the input changes.
Accounting
Every receipt and supplier invoice used to be keyed in by hand. We built an AI flow that reads the documents and suggests the account coding.
Accuracy ended up higher than with manual handling, and the consultants spend the time saved on reconciliation and advice. Nothing is booked without a person approving it.
Construction company
We built a solution that reads supplier invoices with OCR and AI and extracts the information needed to process them further.
Many people now expect AI to solve almost anything, but it works best on specific tasks such as pattern recognition, forecasting and classification. For broader problems, traditional methods are often both cheaper and better.
We only introduce AI once the prototype shows it beats the alternatives.
The AI projects that work have a clear problem, enough good-quality data and a way to measure the result. The ones that stall have often tried to solve too much at once.
So we narrow the scope hard from the start, test against the criteria and only build on what holds up.
Articles and news from us in this area
An AI assistant that mirrors your codebase as a living markdown knowledge base. End users describe new features, get realistic time estimates grounded in the actual code, and approve development on the spot.
Read moreSkip the manual receipt work. Our AI-driven workflow reads the receipt image, creates the journal entry directly in the general ledger and attaches the source document – automatically for 90 %+ of expense receipts.
Read moreWe start by assessing the task and the data. AI fits when inputs and outputs are clear, there is enough data and the result can be measured. If the problem is too broad or the data too weak, we suggest something else.
The prototype shows it. If AI does not beat simpler methods, we say so and help you find a better solution.
It depends on the task. For some classification problems a few hundred examples are enough, while forecasting models often need more history. We help you judge whether you have enough.
We agree the criteria before we build, such as classification accuracy, less manual work or better forecasts. If it cannot be measured, you probably should not invest in AI.
Yes. The integration is often the easier part. The hard part is making sure the task is well defined and the data is good enough.
It depends on the solution. With local models the data stays in your own environment. When we use Claude, we go through where the data is processed before we start, and we sign a data processing agreement when personal data is involved.
We are a licensed Anthropic partner and often build on Claude. When the data cannot leave your environment, we use local models.
Assessment and prototype typically take 4–6 weeks. The rollout time depends on how many systems the solution connects to. We recommend starting with one task and building on it once it works.
Fill out the form and we will get back to you within 24 hours.