Data Intelligence

A network of entities and their relationships: customers, products, processes and how they connect. It reveals patterns that stay invisible in tables, like which customers share dependencies.

Tables answer questions you already thought of; a graph surfaces the ones you did not: which supplier failure hits which customers, which products are bought together, where knowledge concentrates on one person. It is also the natural memory layer for AI systems that need context.

Databases, interfaces of your business tools, spreadsheets and event streams. We pull the sources together into pipelines, so numbers stop living in disconnected silos.

The usual suspects are CRM, shop or booking system, finance tool, support desk and a few grown spreadsheets. Connecting them is rarely the hard part; agreeing on one definition of truth per metric is, and that alignment is part of our job.

As current as your decision needs: from live streaming for operations to daily aggregates for management views. Around 2.4 million data points per day are processed in typical setups.

Freshness has a price, so we match it to the decision: an operations wallboard needs seconds, a monthly board report does not. Every metric on a dashboard shows its own last-updated timestamp, so nobody unknowingly argues with stale numbers.

No. We build pipelines incrementally around your first questions and grow the foundation with the use cases, instead of starting with a year-long infrastructure project.

The pattern is question first, platform second: the first dashboard proves value within weeks and teaches us which foundation is actually needed. Everything built on the way, pipelines, definitions, models, carries over when the platform grows underneath.

Yes. Forecast models project demand, revenue or load, and anomaly detection flags unusual patterns early, from a breaking sensor to a suspicious order spike.

Every forecast ships with its uncertainty, a range instead of a single seductive number, and is checked against reality month after month. Anomaly alerts are tuned to your tolerance so the system warns about the remarkable, not about every ripple.

Everyone who needs them, without technical skills: role-based views show each team its relevant numbers, and access control keeps sensitive figures restricted.

A dashboard nobody opens is a failed dashboard, so each view is built with the people who will use it: their questions, their vocabulary, their working rhythm. Sales sees pipeline and conversion, operations sees load and bottlenecks, leadership sees the condensed picture.

Validation and cleaning happen at ingestion, quality metrics are monitored continuously, and inconsistencies are flagged at the source instead of silently distorting reports.

Concretely that means schema checks, duplicate detection, plausibility ranges and completeness monitoring on every load. When a source system changes its format overnight, the pipeline flags it that morning, before a single wrong number reaches a decision.

Yes: encryption in transit and at rest, role-based access, audit logs and GDPR-compliant processing. Analytics never comes at the cost of data protection.

Personal data is minimized or pseudonymized before analysis wherever the question allows it, most business questions do not need names to be answered. Storage location, retention and access are documented, so your privacy officer gets answers instead of surprises.