Data and AI solutions that pay for themselves.
From demand forecasting to process optimization. At an international manufacturer the forecast went from 40% to 68% accurate. That was worth €778K a year.
Data solutions are judged here on the outcome:
cost reduction, revenue growth and ROI.
With a figure attached, before the investment.
Case Studies
Real impact, in numbers
84% accurate quote prediction with AI-driven sales intelligence
Masparts, the specialist in exclusive car parts for Maserati, Ferrari, and Lamborghini, turns its webshop data into revenue. Three AI models for cross-sell, quote scoring, and early customer signals run directly inside the Laravel backend. The sales team now knows in advance which quotes are likely to convert and which parts sell together.
71% better demand forecasting for new product launches
A global market leader in construction tools wanted to improve demand forecasting for new products. With our ML models we achieved 71% improved accuracy and €778K annual savings.
28% higher conversion with AI-driven personalization
A major Dutch retailer increased online conversion by 28% through personalized product recommendations and dynamic content.
Smarter CPC bidding with AI-driven budget allocation
TravelAround, an innovative travel company, optimized their Google Ads performance with a custom CPC bid engine. Machine learning now automatically determines optimal bids per keyword and campaign.
45% less unplanned downtime with predictive maintenance
A major manufacturer reduced machine failures by 45% by combining sensor data with machine learning for predictive maintenance.
What does it deliver?
Three ways data and AI pay for themselves
We always start from a concrete outcome, proven on your own data, before any quote. Pick where your biggest gain sits.
Improve forecasting
Forecast more accurately, benchmarked against your current forecast on your own data.
€778K
saved, 40 to 68% accuracy
Automate work
The retyping of email orders goes away. Your team only checks the exceptions.
60 hrs
of manual work per week back
Data foundation for AI
Your data sources, connections and access ready for AI, foundation first.
60%
stall without AI-ready data (Gartner)
ML models in production
Business impact delivered
Less unplanned downtime (case)
ROI (average)
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Book a free 30-min strategy call
Pick any open slot. We'll go through your data or AI question and what is concretely achievable.
Measured at these companies
From problems to solutions
Three patterns that show up in almost every operation. Not solved in months, but in two-week sprints.
Organizations collect vast amounts of data and never see it in the results. What is missing is rarely the data. It is the step from analysis to something that runs every day. At an international manufacturer that step was worth €778K a year.
View our cases
The Data Impact Scan
In 30 minutes we map out what your data is worth. You speak directly with the data scientist who builds the model, no handover to a junior team.
- No-obligation, 30 minutesNo sales pitch, no slides
- ROI clear upfrontHonestly substantiated, even if nothing comes of it
- From insight to productionWe build working models, not POCs in slides
No value potential after the exploration? Then the project stops and you pay nothing.
Steps
From data to results
in four steps
- 01
The Data Impact Scan
In a no-obligation 30-minute session we explore available data and identify 3 to 5 concrete opportunities with estimated euro-impact. We give honest advice on next steps, valuable even if no project follows.
No-obligation30 minutesNo commitments - 02
Feasibility Study & ROI Validation
We validate whether data science truly creates impact before any investment: data quality, feasibility, detailed ROI and project scoping. We only propose projects where the value potential is on the table.
Data validationROI calculationNo-obligation - 03
Agile Development & Implementation
Sprint-based development with working deliverables every 2 weeks. Foundation in week 1-2, core solution in week 3-8, refinement in week 9-12. Measurable progress and knowledge transfer each sprint.
2-week sprintsIterativeMeasurable - 04
Delivery, Training & Continuity
Sustainable impact through adoption and knowledge transfer. Complete documentation, hands-on training, performance monitoring and 3 months support. The team becomes self-sufficient, no vendor lock-in.
TrainingDocumentation3 months support
Why companies choose Stratalytic
Proof before you pay
We validate feasibility and ROI upfront. No value potential? We stop and you pay nothing.
Business-first approach
We solve real business problems. Technology is the means, the result is the goal.
Results without long lead times
Working solutions every 2 weeks. No months in the dark. Iterative, de-risked and with visible progress from sprint one.
Built for adoption
70% of data projects fail due to lack of adoption. We involve teams from day one so solutions are actually used.
Not ready for a call? Start with the guide.
Free PDF: how to pick the one AI use case that demonstrably pays off, why most AI projects fail, and three measured cases, from 71% lower forecast error to 84% accurate quote scoring.
FAQ
Frequently asked questions
What teams usually want to know before booking.
For organizations that gather enough data to steer by and make decisions that depend on it: inventory, pricing, planning, capacity. Sector barely matters, whether you are in manufacturing, logistics, retail or services. The common thread is a recurring decision that today is made on gut feel or in a spreadsheet.
A first working model is typically live in 2 to 4 weeks. We work in two-week sprints with visible progress, so you are never left in the dark for months.
The Data Impact Scan is free and without obligation. If the exploration shows no value potential, it stops and you pay nothing. The price of a first project depends on scope and data complexity and is set after the feasibility study. We only send a proposal when the expected saving or gain clearly outweighs the investment.
We sign an NDA before you share anything. Processing happens in your own or an EU cloud, GDPR-compliant, and we never train on third-party data.
With Rutger, founder and data scientist, who works on your project himself. No handover to a junior team after the first call, and no account manager between you and the person who builds the model.
Our tech stack
We work with modern, mature technologies. The stack follows from the problem and from what already runs, not from a preference.
