AILO

We build and orchestrate data infrastructure on cloud platforms as part of a broader data foundation, this forms the backbone underneath the dashboards and models our clients rely on daily. For Madklubben, we built a unified data platform in Snowflake, orchestrated on Azure, ingesting data automatically every day with no manual handling required. The same approach applies for SaaS companies consolidating product and usage data, and for consultancies that need a cloud-based delivery layer behind a client engagement. Our cloud work is always in service of a clear business outcome: reliable, current data that the rest of the organisation can build on and scale with as new needs emerge.

We work as your external data department - building the infrastructure, dashboards, and predictive models that turn fragmented data into a single source of truth your team can act on. Most businesses generate data continuously across systems that were never built to talk to each other. We connect those systems into unified, automated data warehouses, then design the reporting layer on top, from daily operational dashboards to 360° customer views, so the right people see the right numbers and trends without manual reporting. We work across the full data stack: connecting source systems, structuring raw data into usable signals, building interactive Tableau and BI environments, and developing predictive models for forecasting, segmentation, and decision support. Every engagement is built to last, we stay close to what we build, maintaining and extending it as your needs evolve. We've built and now maintain data foundations for hospitality and fitness groups including Madklubben (40+ locations) and Jagger/Otto/Ritta, as well as supporting strategy consultancies and SaaS businesses as their technical delivery partner.

We design and build the data warehouses, pipelines, and analytical layers that turn scattered systems into one reliable data foundation. For multi-location businesses, that means connecting POS, booking, and marketing data into a single warehouse. For SaaS companies, it means consolidating product, usage, and billing data to understand what's actually driving growth and churn. Once that foundation is in place, we build live, interactive BI environments (primarily in Tableau or Power BI) that surface the numbers automatically, with no manual handling. Reporting is designed to serve every level of the organisation, from leadership dashboards to team-level operational views. We deliver this work directly for our own clients, and we also act as the technical execution layer for strategy consultancies - handling the data engineering and BI build behind a strategic recommendation, so the consultancy's idea becomes a live tool rather than a one-time presentation.

We build predictive models as a natural extension of the data infrastructure we design, not as a standalone product, but as the next layer once a reliable data foundation is in place. This includes forecasting, customer segmentation, and decision-support models built on operational, product, or customer data already consolidated in our clients' data warehouses. Recent work includes a churn prediction and customer segmentation model (and the pipeline behind it) for a hospitality client, an AI agent project currently underway on Snowflake, and a sales demand forecasting model in scoping to help a client optimise staff scheduling. Our predictive work is grounded in real business questions, such as anticipating demand, segmenting customers, or surfacing patterns that inform decisions, rather than experimental AI for its own sake. Because we also own the underlying data infrastructure, our models are built on accurate, well-structured data from day one.Our predictive work is grounded in real business questions, anticipating demand, segmenting customers for targeted activation, or surfacing patterns that inform operational decisions, rather than experimental AI for its own sake. Because we also own the underlying data infrastructure, our models are built on accurate, well-structured data from day one.