Outcomes we've delivered.

The client's analytics were fragmented across legacy Qlik and Tableau estates, with duplicated data models, escalating licensing costs, and dashboard logic locked in proprietary formats. Every migration or change was a slow, manual rebuild, and the lack of a single governed source of truth made audit and compliance reporting difficult.

The organisation consolidated onto Power BI and Microsoft Fabric with a single, governed semantic layer and row-level security. BI licensing and maintenance costs fell by around 40%, dashboard rebuild time dropped from weeks to days, and the business gained a trusted, audit-ready source of truth.

We built AI agents that parsed the existing Qlik and Tableau workbooks — extracting measures, data models, and visual definitions — and auto-generated equivalent Power BI datasets and reports. Automated validation compared legacy and new outputs for parity, so migration was fast and verifiable. We landed the semantic layer in Fabric on OneLake, established governance and security, and upskilled the client's analysts to run it independently.

An ageing on-premise data centre was approaching end-of-life. Capacity constraints, unpredictable downtime, and a fragile disaster-recovery posture put business-critical systems at risk, and scaling for demand meant expensive, recurring hardware refresh cycles with long lead times.

The client moved to a right-sized cloud platform with a zero-downtime cutover. They gained elastic scalability, multi-zone resiliency, a regularly tested disaster-recovery capability with defined RTO/RPO targets, and roughly 30% lower run cost compared with the on-premise hardware-refresh path — with no capital tied up in ageing hardware.

We began with a full environment assessment and dependency mapping, then produced a migration roadmap sequenced around the client's uptime requirements. We stood up a secure landing zone using infrastructure-as-code, migrated workloads in phased, zero-downtime waves, and engineered multi-region resiliency with automated backups and tested DR runbooks. Post-migration, we optimised for cost and performance and handed over a documented, repeatable operating model.

High volumes of repetitive knowledge work — intake, triage, document processing, and routine client responses — consumed skilled staff time, created persistent backlogs, and produced inconsistent outputs. The work couldn't scale with demand without proportional headcount, and quality varied by individual.

Autonomous AI agents now handle these workflows end-to-end with human-in-the-loop oversight. Turnaround time fell by around 70%, backlogs cleared, output became consistent and fully auditable, and skilled staff were freed to focus on higher-value, judgement-based work.

We identified the highest-ROI workflows, then designed a multi-agent architecture — planning, tool use, and retrieval — integrated directly with the systems the team already used. We built in guardrails, automated evaluations, and human approval steps for sensitive actions, and shipped iteratively with monitoring so accuracy and coverage improved safely over time.

Production assets across multiple sites generated data that was siloed, unmonitored, or discarded entirely. With no real-time visibility into equipment health, maintenance was reactive — unplanned downtime interrupted throughput, and there was no reliable data foundation for optimisation or forecasting.

A secure IoT platform now streams telemetry from thousands of sensors into a unified data platform, powering real-time dashboards and predictive maintenance. The client achieved a measurable reduction in unplanned downtime, improved overall equipment effectiveness, and a trusted data foundation for future analytics.

We designed an edge-to-cloud architecture with secure device connectivity and gateways, then built scalable streaming ingestion and storage as a unified data platform. On top, we delivered operational dashboards and machine-learning models for anomaly detection and predictive maintenance, with security, identity, and governance built in from day one rather than bolted on.

The organisation lacked a clear, evidenced view of its security posture across endpoints, network, cloud, and identity. Facing compliance obligations without a documented baseline or a prioritised remediation plan, leadership had no reliable way to understand or communicate real exposure.

A comprehensive assessment gave the client a prioritised risk register, a remediated baseline that closed critical vulnerabilities, and clear alignment against recognised compliance frameworks. Leadership gained measurable assurance and a costed roadmap for continued improvement — turning uncertainty into a defensible, evidence-backed position.

We conducted vulnerability scanning and penetration testing, and reviewed identity and access, cloud, and network configurations against established security frameworks. We delivered both an executive summary and a detailed technical report with prioritised, actionable findings, then supported the team through remediation and rolled out security-awareness training to reduce human-factor risk.