The kind of work we take on

The bank had logging across its core systems but no one watching it after hours, so suspicious activity sat unreviewed until the next working day. Regulators expected a documented detection and response capability the bank could not yet evidence.

We deployed and tuned a SIEM (security information and event management) platform against the bank's actual estate, writing detection rules for credential abuse, lateral movement and privilege escalation rather than relying on vendor defaults. A 24/7 security operations centre took on monitoring, triage and escalation, and we built and rehearsed an incident-response runbook with the bank's own staff.

Intrusions and policy violations are now surfaced and triaged in minutes rather than days, with a tested incident-response runbook and named escalation paths the bank can show its regulator.

The provider was scaling its card-payment product and faced a PCI DSS (Payment Card Industry Data Security Standard) assessment with no recent independent testing. Leadership suspected gaps in the payment flow but had no evidence of where they were or how serious they were.

We ran a grey-box penetration test across the web application, APIs and supporting cloud infrastructure, combining authenticated testing with black-box probing of the public attack surface. Each finding was reported with a full proof of concept, the request and response involved, business impact and a prioritised remediation step.

The provider went into its PCI DSS assessment with the high and critical findings fixed and verified on retest, and a clear remediation record for the auditor.

Administrative staff spent hours each day sorting inbound referrals and clinical documents into the right queues by hand. Volume was rising and misrouted documents were delaying patient care.

We built a document-classification pipeline using a fine-tuned language model with a human-in-the-loop review step, so staff confirm low-confidence cases rather than the model deciding alone. Patient data stayed inside the provider's own environment, and we added audit logging and an escalation route for anything the model could not classify confidently.

Routine referrals are now sorted automatically with staff reviewing only the uncertain cases, freeing administrative time while keeping a clinician in control of clinical decisions.

The retailer's monolithic storefront was slow on mobile and buckled during seasonal sales, costing checkouts at exactly the moments that mattered most. Adding features meant long release cycles and frequent regressions.

We rebuilt the storefront as a headless commerce front end using Next.js with server-side rendering and a content delivery network in front, decoupling the buyer-facing layer from the commerce back end. We added automated testing and a continuous deployment pipeline so changes ship safely and often.

The retailer now has a fast, mobile-first storefront that holds up under peak-sale load, with a release process that lets the team ship features in days instead of weeks.

Drivers reported delivery status by phone call and paper, so dispatch never had an accurate live picture and customers chased updates by email. Proof-of-delivery records were easy to lose and slow to reconcile.

We built a cross-platform mobile app for drivers with offline-first data capture, GPS shipment tracking and photo proof of delivery, backed by a dispatch dashboard and APIs into the existing transport management system. The app syncs automatically once a connection returns, so depots with poor signal do not lose updates.

Dispatch now sees shipment status in real time and customers get accurate updates, with digital proof of delivery captured at the door and reconciled automatically.

The firm's buyers increasingly demanded verifiable provenance for components moving through a multi-supplier chain, but records lived in separate systems that each party could quietly edit. Disputes over who handled what, and when, were hard to settle.

We designed a permissioned blockchain on which each supplier records custody events as signed transactions, giving every party a shared, tamper-evident history without exposing commercial data to competitors. Smart contracts enforced the handover rules, and we audited the contract code before it went live.

Every participant now works from one tamper-evident record of custody, so provenance claims can be verified and handover disputes resolved against an audited shared ledger.

The company ran its platform on ageing single-region servers that were expensive to scale and painful to recover after an outage. Deployments were manual, so releases were risky and recovery from failures was slow.

We migrated the platform to a cloud provider using infrastructure as code, containerised the services and orchestrated them with Kubernetes across multiple availability zones. We added a continuous integration and deployment pipeline, automated backups and monitoring with alerting, so the environment is reproducible and observable.

The platform now scales with demand and recovers automatically from a zone failure, with releases shipped through a tested pipeline instead of by hand.

The agency ran a mix of unpatched servers and unmanaged endpoints with no central asset register, so it could not say with confidence what it owned or what was exposed. Staff had nowhere reliable to report IT issues and downtime went unrecorded.

We took on managed IT for the estate, starting with a full asset inventory, a patch-management programme and centralised endpoint protection. We stood up a service desk with defined service levels and migrated core workloads to a managed cloud environment, documenting the configuration so the agency keeps control of its own systems.

The agency now has a known, patched and monitored estate with a service desk staff can rely on, and a documented configuration it owns rather than depends on a single vendor to understand.

The provider delivered the same fixed exercise sets to every student, so stronger learners were held back and struggling ones fell further behind. Teachers had no clear view of where individual students were getting stuck.

We built a recommendation engine that adapts each student's next exercises to their demonstrated strengths and gaps, paired with a teacher dashboard that surfaces where a class is struggling. We trained and evaluated the model on the provider's own anonymised performance data and built in safeguards against reinforcing early mistakes.

Students now get practice matched to their level and teachers see where to intervene, with a model evaluated on the provider's real data rather than generic benchmarks.