We provide data science solutions in Oslo, specializing in machine learning model development, data pipeline engineering, and actionable analytics that drive business decisions.
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Technical strategy and product roadmap development. We help you define what to build, in what order, and with which technologies. From MVP scoping to multi-year platform evolution plans.
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Technical infrastructure review We analyze your data pipelines, storage systems, and infrastructure architecture to identify bottlenecks, security gaps, and optimization opportunities. You get a detailed report with actionable recommendations prioritized by impact and implementation effort.
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Pilot deployment and production rollout We handle the technical deployment of your application, from initial staging environments to full production launch. This includes CI/CD pipeline setup, environment configuration, monitoring integration, and performance optimization. We ensure your application runs smoothly from day one, with proper error tracking, logging, and scalability measures in place. Whether deploying to Vercel, AWS, or your preferred platform, we manage the entire process and provide documentation for your team.
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Technical standards & responsible development We follow established security practices, GDPR compliance, and accessibility standards (WCAG 2.1). Our code is tested, documented, and built to last. We consider the ethical implications of what we build — especially with AI features — and help clients navigate privacy regulations and data protection requirements.
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Infrastructure monitoring, alerting, and performance optimization. We set up comprehensive observability stacks (Datadog, Sentry, CloudWatch) and implement automated scaling strategies for your growing applications. From setting up proper logging pipelines to configuring auto-scaling policies and cost optimization — we ensure your systems stay reliable as they grow.
Data science services in Oslo from Dev, in focus on turning your data into practical business value. We analyze your existing data infrastructure, identify bottlenecks, and design systems that actually work with your current operations. Our approach covers the full spectrum: from initial data assessment and strategy development to building production-ready ML models and establishing governance frameworks. We implement solutions using modern tools and APIs, ensuring they scale reliably as your needs grow. Our process includes technical audits, pilot deployments, performance monitoring, and ongoing optimization. You get data science that integrates seamlessly with your business — not theoretical models that sit on a shelf.
Strategic roadmap outlining your use cases, technical implementation plan, and realistic timeline with key milestones
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Detailed workshop findings document including agreed objectives, measurable KPIs, and implementation priorities
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Comprehensive audit report covering your data quality metrics, infrastructure gaps, and a prioritized roadmap for ML readiness. Includes specific recommendations for data pipeline improvements and tooling upgrades.
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Detailed gap analysis identifying security vulnerabilities, performance bottlenecks, and technical debt. Actionable mitigation plan with prioritized fixes and implementation roadmap.
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Working pilot implementation with documented metrics and actionable recommendations for next steps
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Technical infrastructure documentation including API architecture, database schema, deployment configuration, and scaling roadmap for handling growth
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Documented governance framework defining ethical guidelines, compliance requirements, and decision-making processes for responsible AI/ML deployment
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Measurement framework with specific KPIs to track user adoption, system performance, and business impact
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Real-time monitoring dashboards with integrated feedback collection and performance metrics tracking
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Change management strategy including team training, documentation, and rollout communication plan
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