Real delivery patterns. Anonymized.

Problem

A mid-sized industrial plant lost hours of production at every unplanned interruption. Maintenance operated reactively, with high emergency repair costs and direct impact on delivery deadlines.

Approach

We implemented a predictive maintenance system combining fusion of existing sensors, anomaly detection models trained on historical data, and native integration with the CMMS to automatically generate work orders when risk crosses threshold.

Result

Unplanned downtime fell significantly. The maintenance function moved from reactive to condition-based, with a clear reduction in emergency repair costs and better predictability in production planning.

Problem

A logistics company operated without real-time fleet visibility. Unstable cellular connectivity on rural routes caused loss of telemetry data and inability to identify operational exceptions in time.

Approach

We built an edge-first telemetry platform with secure local storage during connection loss, satellite-link fallback for critical events, and a centralized dashboard consolidating data as devices reconnect.

Result

Fleet visibility no longer depended on route connectivity. Position updates with sub-minute latency, automated exception alerts and reliable dashboards for the operations team.

Problem

A retail group wanted to apply GenAI to internal operations, but needed to guarantee response accuracy, control access to sensitive documents, and have full query auditability for internal compliance.

Approach

We implemented a RAG assistant with per-document access control (aligned to user role), mandatory citations on every response pointing to the original source, and an automated evaluation harness to detect hallucination before each new version deploys.

Result

Operations teams adopted the assistant as a daily working tool. Measured accuracy above the agreed pre-project target, with a full audit trail per query for compliance review.

Problem

A B2B SaaS with a six-figure monthly AWS bill observed costs growing out of alignment with revenue. No visibility per product and no established internal FinOps practice.

Approach

Full four-week audit: analysis of Cost and Usage Reports, systematic identification of opportunities in compute, storage, networking and third-party SaaS, and direct implementation of low-risk quick wins during the audit itself.

Result

Substantial reduction of recurring monthly billing in the first month post-audit. Prioritized roadmap for the following months and establishment of an internal FinOps practice with tagging and showback per product.

Problem

A post-Series B scaleup ran manual deploys at every release, with mean time to recovery above tolerable. Engineering team growth made the model unsustainable.

Approach

Migration to EKS with GitOps via ArgoCD, integrated observability with distributed tracing, CI/CD pipeline with automated testing per layer, and introduction of SRE practices — including SLO definition and incident review process.

Result

Deploy frequency went from occasional to multiple times per day. MTTR dropped to minutes. The engineering team reallocated time from manual operations to product build.