Cloud, data and AI look the same on an architecture diagram and very different inside a bank, a retail network or a utility. These are the sectors we work in, and the problems we are usually brought in to solve.
Payments and lending systems have no quiet hours and no tolerance for a lost transaction. We move them off legacy platforms without downtime, run them to the standards your regulator audits against, and automate the reporting that currently eats a team's month-end.
Migration from ageing platforms to AWS or Azure in reversible waves, with parallel running so no transaction is lost at cutover.
Architectures that absorb salary-day and campaign peaks, and the monitoring to prove they did.
Automated pipelines for regulatory returns, with lineage that shows exactly where every figure came from.
Retail margins are made in the supply chain: the right stock in the right place before the customer asks for it. We connect the systems that track it, forecast the demand behind it, and keep the storefront up when a campaign lands.
Inventory synced in near real time across warehouses, stores, marketplaces and distributors.
Forecasting built on your own sales history, feeding the reorder and last-mile decisions your team already makes.
Cloud ERP integration and supply-chain automation that replace the spreadsheet hand-offs between them.
Utilities run physical assets over long lifetimes, often where connectivity cannot be taken for granted. We turn the data those assets produce into maintenance and demand decisions, and modernise the systems that have to stay up while we do it.
Smart-grid and sensor data landed, cleaned and analysed, so demand peaks are seen coming rather than after the fact.
Predictive maintenance for field and off-grid hardware, so crews go where a failure is likely rather than where the schedule says.
Carbon tracking built on measured data, with the audit trail reporting frameworks expect.
