Application maintenance and infrastructure for DeLaval: performance and uptime

DeLaval is a global reference in solutions for the agricultural sector, with applications that carry traceability, remote monitoring and dairy farm data. Its digital operation supports a business where downtime is not an inconvenience: it is production stopped.

The problem was not in the code but underneath it: the infrastructure was not keeping up with growth, and that turns every demand peak into operational risk.

• An unoptimized virtualized environment: response times degraded exactly when the load was highest.
• Reactive support: the team stepped in after the incident, not before, and every contingency was a surprise.
• Architecture with no headroom: there was no base to build integrations or new development on.

The cost was not measured in hours of downtime: it was measured in business decisions postponed until the platform could take them.

We treated infrastructure as part of the product rather than its support, and organized the work from there: measure the environment first, optimize it next, and only then build on top of it.

The plan combined a technical audit, optimization of the virtualized environment, architecture improvements and a continuous support model, with documentation kept current so the knowledge does not live in one person.

Audit and diagnosis

• Technical audit of the infrastructure, to work from measurement rather than symptoms.
• The diagnosis set priorities by impact, not by perceived urgency.
• Technical documentation and an evolution roadmap that defines what comes first.

Optimization and performance

• Optimization of the virtualized environment, focused on demand peaks.
• Performance improvements on the applications the business depends on.
• High availability of web applications as a requirement, not an aspiration.
• Security and operational continuity criteria built into the environment.

Continuous strategic support

• The model went from reactive to proactive: the team steps in before the incident.
• The in house IT team leans on a technical partner, not a ticket vendor.
• An agreed evolution roadmap, so each engagement builds on the last.

Architecture and technology

• Scalable architecture standards applied across the application set.
• Environments aligned with DevOps practices.
• Traceability, remote monitoring and data management carried by the same environment.
• A base ready for future integrations and web development.

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Response times improved 35% and critical incidents dropped 40%, with availability above 99.8%.

The baseline is the environment before the engagement.

Starting point

• High response times at demand peaks.
• Operational risk in the face of contingencies.
• Infrastructure without advanced optimization, and reactive support.

Results achieved

• 35% better response times: measured across the production environment average.
• 40% fewer critical incidents: proactive support cuts in before they escalate.
• SLA above 99.8%: availability became a measurable commitment.
• Infrastructure ready to scale: new development no longer starts by asking for headroom.

Strategic impact

• The in house IT team gained operational efficiency.
• Infrastructure stopped being the ceiling on product decisions.