All about AI
All About AI: From Code to Impact
Welcome to our knowledge hub on artificial intelligence.
Here we gather articles, guides, and insights that explore AI from multiple perspectives – from technical implementation and prompt design to ethical considerations and business impact. We believe in understanding the technology in depth, but also in daring to ask what it means for us, our organizations, and our future.
All About AI is for those who want to understand, apply, and challenge AI – for real.
Things we have done
Notar – Mäklarwebben som tog hem Guldhemmet fem år i rad
Så tog vi fram en AI-bot som stärkte vårt interna arbete
Gör video sökbart och tillgängligt med AI-baserad transkribering
AI för avvikelsedetektering i energidata
AI som förstår vad du lyssnar på – ämnesklassificering för poddskapare
Nu rapporterar diskmaskinerna själva in sin hälsostatus
AI för smartare leadshantering och kompetens-matchning
AI för bygganalys – automatiserad kostnadsberäkning från ritningar
AI för smartare energidata – förstå hushållens behov i realtid
AI och Computer Vision för bildklassificering i nanometerskala
AI in 5 minutes
Read AI in 5 minutes and get hands on tips on how to get started with AI.
Questions about AI
Cloud-based AI solutions provide scalable capacity, flexible tools and faster development. We help you leverage the cloud to implement advanced AI cost-effectively.
We design UI/UX that makes AI and machine learning intuitive and accessible for employees and customers. User-centred design ensures your AI delivers value in practice.
AI Proof of Concept (PoC) is used to test and validate that the core problem is solvable before proceeding with scaling. By focusing on real use cases, we quickly demonstrate measurable impact.
AI prototyping is a quick and effective way to test how AI can create value in your business before full implementation. We build tailored prototypes that show concrete results and insights.
The key is to start with a clear goal and practical use case. We work iteratively with rapid prototyping, testing and validation to deliver measurable value and reduce risk.
We work agilely and collaboratively with our clients – from data collection and modeling to testing, validation, and deployment. Each step builds on insights and continuous delivery to ensure measurable results and fast business impact.
We use MLOps practices to create stable and scalable AI solutions. This includes automated pipelines, testing, versioning, monitoring, and continuous improvement in production environments.
Depending on the project, we either build models from scratch or fine-tune existing models. When fine-tuning existing models we of course ensure license compliance. We always choose the most effective approach for your goals.
We specialize in computer vision, image analysis, natural language processing (NLP), forecasting, anomaly detection, and MLOps. Our teams combine research and engineering to deliver robust solutions with real impact.
Our expert function within AI and machine learning helps companies take their AI initiatives to the next level. We provide advanced model development, data analysis, NLP, and computer vision with a focus on measurable business value and lasting quality.
Our AI Workshops are interactive sessions where we identify ideas, challenges, and use cases together. The goal is to quickly pinpoint where AI can create the most value in your organization.
An AI Assessment is our structured evaluation of your maturity across strategy, organization, data, technology, and operations. It gives you a clear picture of where you stand and how to move forward with AI.
Create a shared vision, assign ownership, train key roles, and establish a simple operational model (e.g., AI CoE/light). Follow up on results transparently through KPIs.
The cost depends on scope, number of use cases, data assessment, and regulatory context. We recommend a focused approach leading to PoV-ready initiatives.
Typically 4–8 weeks. The timeline depends on data landscape, stakeholders, regulatory requirements, and the need for pre-studies.
Implement privacy-by-design, conduct DPIAs where relevant, use encryption and access controls, and apply human oversight. Define policies for training data and generated outputs.
Match requirements like data volume, security, latency, MLOps, cost, and in-house expertise. Favor open standards and avoid unnecessary vendor lock-in.
Yes. A governance model defines roles, decision forums, policies, and controls for data, model quality, security, ethics, and compliance.
We help you understand what data you already have and what’s missing. By combining business and technical perspectives, we build a strong data foundation that makes AI projects possible.
PoV (Proof of Value) quantifies business impact and KPIs. PoC (Proof of Concept) tests technical feasibility. Together, they minimize risk before scaling.
Balance business value with feasibility — considering data availability, complexity, risk, dependencies, and time-to-value. Start with 1–2 low-risk, high-value PoV projects.
Typically: current state analysis, identification and prioritization of use cases, data maturity assessment, target architecture and platform choice, risk/ethics/GDPR, and a KPI-based roadmap.
The strategy ensures AI initiatives support business goals, have clear priorities and budgets, and that governance, data, and technology are in place for sustainable delivery.
An AI strategy outlines how your organization uses AI to achieve business goals. It includes vision, prioritized use cases, data needs, technology choices, governance, and a realistic roadmap.
upTech our latest episodes
See our previous episodes with Linus Ekenstam och Amer Mohammed.