Finding the signal. Building what ships.
We turn data into working models: forecasting, pattern recognition, GenAI, and geo-AI. The analysis is rigorous. The outputs are built to be used.
Most companies sit on data with real business value but lack the expertise to extract it.
Even with clean data, you still need the skills and process to turn it into decisions.
Evaluate data landscape and AI-readiness to identify the highest-value starting points.
Targeted data collection, cleaning, and pre-processing for reliable analysis.
Statistical analysis and opportunity identification: finding the signal in the noise.
Feasibility analysis and proof-of-concept development of AI applications.
Blueprint for intelligent solutions with an implementation roadmap for production.
Rigorous statistical analysis with clear visuals that make results easy to act on.
Models that are trained, validated, and deployed, not prototyped and shelved.
Plain-language explanation of what your AI outputs mean and when to trust them.
What data to collect, how to track it, and why each signal matters.
Workshops so your team understands what was built and can maintain it.
Economic forecasting for client advisors
A large international insurer needed advisors to have better economic context when talking to clients. We built forecasting models for key indicators and pushed outputs directly into the advisory tool, replacing guesswork with a clear, updated view.
Demand and sales forecasting
A consumer goods company was planning production largely on last year's numbers. We built models accounting for seasonality, promotions, and external signals. Planning cycles shortened and end-of-quarter surprises got fewer.
Customer care chatbot
An InsurTech wanted to automate routine customer care without losing quality. We built and deployed a proprietary chatbot trained on their own data. Handling time for routine queries dropped significantly.
Logistics efficiency analysis
A construction materials company suspected logistics inefficiencies but could not point to where. We analysed operational data and identified specific bottlenecks. They left with concrete actions, not a report full of charts.
Spatial risk scoring with GIS
An insurer wanted to price risk based on where a property actually is, not just its postcode. We integrated GIS data into their rating models on Google Cloud. The models became meaningfully better at distinguishing high-risk from low-risk addresses.
GPS telemetry reconstruction
A large IoT fleet operator needed to understand actual movement patterns, not just raw GPS pings. We built algorithms to reconstruct operational areas from telemetry data, giving a clear spatial picture of how the fleet was being used.
Let's talk through your situation.
Fredrik Moeschlin CEO & Founder
