AI where it really helps.
AI agents for analysis, marketing mix models for budget decisions and models that predict customer value and churn, only where they improve decisions.
AI that learns and predicts.
Applied AI, not a showcase.
Every vendor proposal has an AI section and every presentation talks about AI capabilities. In practice, much of it is either rebranded analytics or chat on top of dashboards that already worked fine.
We apply AI only where it genuinely improves decisions, and not where an analyst or a simpler model gives the same result more clearly.
So we work with marketing mix models for budget allocation, models that predict customer value and churn, AI agents that spot deviations at a scale no person can cover and forecasts that hold up through seasonality.
We say no to AI-generated content that costs more to fix than to write, to "AI dashboards" that hide what a simple query would show clearly and to models built on thin or unstable data. If something is solved just as well with simpler means, it doesn't need AI.
What changes for you.
Marketing mix modelling
Open-source frameworks for budget allocation decisions, not opaque services. The model runs transparently on your own data and is fully auditable.
AI analysis agents
Agents that spot deviations across accounts and run repetitive analyses that would otherwise be done by hand. Internal tools we use every day.
LTV and churn
Models that predict customer value and identify customers at risk, so retention actions have a real effect and feed marketing and the CRM.
Forecasting
Forecasts of revenue, demand and capacity that account for seasonality, holidays and market shifts, not just a short history.
Classification
Product classification at scale, sentiment analysis of reviews and content tagging, for e-shops that want to grow their catalogue or understand the customer.
Honest model choice
When a simpler model or a plain query gives the same result, we pick that. The goal is better decisions, not the most impressive model.
How we work.
Framing
We check whether AI is the right solution or whether simpler analytics answer the question more cheaply. Many proposals end here, with a simpler recommendation.
Design
We choose the model for the specific problem and set the success criteria up front. We explicitly examine the cases where the model could fail.
Testing
We test the model on data it hasn't seen and on historical periods before it goes live. Many models pass the first check and fail in practice.
Operations
The model is built into decisions (bidding, CRM, inventory, reporting) and monitored for loss of accuracy, so it is retrained or retired when needed.
Common questions.
Apply AI where it helps.
Let's talk about where AI gives better decisions and where simpler analytics answer the same question more cheaply.
Talk to us about AI