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Ten practices, one standard: build what the business needs and prove that it works.
Most AI projects fail at evaluation, not at the model. We build LLM and ML systems with the test harness first, so every change to a prompt, a model, or a retrieval step gets scored before it ships. That discipline is what makes AI dependable enough to put in front of customers.
Our SAP consultants have lived through real implementations: upgrades that overran, transports that broke at midnight, integrations nobody documented. That experience shapes how we run S/4HANA migrations, BTP extensions, and the daily discipline of keeping a landscape healthy.
We build web products on Next.js and React with the boring things done right: rendering strategy chosen per page, a performance budget enforced in CI, and accessibility treated as an engineering requirement instead of an audit finding.
Mobile users forgive nothing. An app has to open fast, work offline, and survive bad networks, or it gets deleted. We build native and cross-platform apps that treat those constraints as the starting point, then handle store review, crash reporting, and release trains after launch.
Cloud bills grow quietly and architectures rot quietly. We design AWS and Kubernetes platforms where cost is a first-class metric: every resource in Terraform, environments reproducible from scratch, and a monthly bill your finance team can actually read.
A release should be an ordinary event, not a ceremony. We build CI/CD pipelines and observability so teams can deploy on a Tuesday afternoon without a war room: trunk-based development, GitOps, and dashboards that answer questions instead of decorating a wall.
Security that arrives as a PDF after the build is finished is theatre. We work OWASP-aligned from the first architecture diagram: threat modelling during design, SAST and DAST wired into the pipeline, and findings triaged by real severity instead of aging in a spreadsheet.
Good interface design is mostly decisions, not decoration. We run research, prototype cheap and early, then build the design system in code, so the gap between the mockup and the shipped product stays at zero as the product grows.
We treat marketing like engineering: hypotheses, experiments, measurement. Technical SEO is handled in the codebase, analytics events are designed before a campaign runs, and reports show cost per outcome, not impressions.
Testing is a design activity, not a phase at the end. We define what must never break, automate exactly that, and keep the suite fast enough that engineers run it without thinking. Slow, flaky test suites kill more releases than bugs do.
Straight answers to the questions we get most.