Sharper data for better decisions

We build the infrastructure that makes data available, reliable and genuinely usable. The first building block is collection: production-grade scraping (handling structural changes, sensible anti-bot measures and incident recovery), API integrations (CRM, ERP, e-commerce, advertising, support and payment systems), file-based feeds (SFTP, exports and webhooks) and application telemetry. We also capture the “grey data” that often goes unused: logs, application events, tickets, forms, browsing histories and product signals.

We then industrialise delivery through batch and real-time pipelines with orchestration, monitoring, alerting, automatic recovery and end-to-end traceability. We design the right data warehouse or data lake architecture: schemas, historisation, key management, reference tables, a single source of truth where appropriate, and a clear separation between raw, clean and curated layers.

Finally, we address the costliest issue to ignore: quality. Deduplication, normalisation, enrichment, consistency checks, automated tests, missing-value handling and quality scoring. The goal is simple: stable data over time, ready for analytics, BI and AI, without every team interpreting reality differently.

We treat measurement as a system, not a collection of tags. It starts with a tracking plan built around business outcomes—qualified leads, sales, margin, average order value and retention—not merely “page_view”. We model events, properties, deduplication rules, sources of truth and edge cases such as consent, cross-domain journeys, external payment providers, third-party apps and iOS.

We then implement robust measurement: a clean GA4 setup, advertising pixels, e-commerce events, offline conversions when needed and, above all, server-side or hybrid tracking to improve reliability and reduce dependence on browsers. We establish tracking governance through naming conventions, versioning, pre-production QA and regular audits to prevent drift, duplicate events, inconsistent parameters and lost conversions.

For decision support, we turn data into practical management tools: BI dashboards (Looker Studio, Power BI, Metabase and others), automated reporting, executive and operational views, cohort and funnel analysis, pragmatic attribution, and ROI by channel, campaign and creative.

Once the data is reliable, we put it to work. We focus on practical use cases with measurable ROI: forecasting (demand, revenue, inventory and workload), scoring (purchase likelihood, value and risk), segmentation (behavioural, RFM and LTV), and anomaly detection (fraud, tracking incidents and trend breaks). We favour useful, explainable models that integrate into real processes—not theoretical demonstrations.

For churn prevention, we build attrition models around weak signals: declining usage, growing friction, response delays, support tickets and journey anomalies. The output is an actionable score, risk segments and an activation plan: whom to contact, when, with which message and through which channel. We measure actual impact—uplift, retention and net gain—not merely a flattering AUC.

We also build automated valuation models (AVMs) for real-estate estimates, asset or product valuation, dynamic pricing and offer recommendations. The key challenge is production: how the model runs, where it lives, how it updates, how drift, performance and bias are monitored, and how retraining is handled.

Finally, we prepare what makes AI models perform: high-quality datasets. We collect, clean, structure and annotate data; define labels and guidelines; quality-control annotations; and create sound train, validation and test sets. This is where real model performance is won.

Governance prevents data from becoming an unmanageable grey area. We establish ownership through data owners and data stewards, official definitions through glossaries and KPIs, quality rules and conventions for naming, sources and lifecycles. We also structure documentation: lineage, data dictionaries, contracts between teams and change procedures. The goal is to make data transferable, auditable and scalable without depending on the two people who “just know”.

For compliance and security, we take an operational approach: GDPR by design, processing maps, data minimisation, consent management, appropriate anonymisation or pseudonymisation, retention policies and secure access through RBAC, secrets management, segmentation and logging.

We establish standards for data classification, development, staging and production environments, export controls and incident procedures. Data can create value without exposing the business. Teams can move faster, share more confidently and pass internal, client or partner audits without improvisation.