# Data & AI: forecasts and analytics | techport.ai > Data integration, forecasting and AI-supported analytics for the Mittelstand. GDPR-compliant, German-based servers, no vendor lock-in. URL: https://techport.ai/en/daten-ki --- data & ai # When a process needs data, we build the software around it. Data and AI for processes that need to become measurable. A lot of companies have plenty of data but no reliable workflow built around it, information sits in Excel, ERP, CRM, emails or specialised tools. We build systems that make that data usable: structured, traceable, and embedded in the processes you already run. [](/en/kontakt) index 01GDPR-compliant 02Integrates with your existing IT 03German-based servers 04Run and maintained for you starting point ## Data on its own doesn't fix a process. Dashboards, models and AI prototypes don't help much if no one uses them day-to-day. The bottleneck is rarely the algorithm. What's usually missing is a clear workflow: Which data is actually relevant? Who makes which decision? When does a result get reviewed? What happens when the model gets it wrong? So we don't start with 'AI'. We start with the process you're trying to improve. approach ## First the workflow. Then the data. Then the right technology. We look together at where data or AI genuinely helps: in forecasting, classification, text processing, analytics, or preparing decisions. Once the benefit is clear, we build it into a system your team can use day-to-day. Our principle No isolated prototype. No model without a process. No automation without oversight. case example ## Worked example: load forecasting in the energy sector In the energy sector, data quickly becomes commercially relevant. A better load forecast doesn't just reduce technical error, it changes procurement, shortfalls and cost risk. The example below shows a historical load day: actual load, a simple baseline forecast, and a data-driven approach. What matters isn't the prettier curve, but whether better data leads to a better decision. Historical load day MAE baseline forecast 4,996MW MAE data-driven approach 758MW Shortfall avoided 101,729MWh Error reduction 84.8% In a real client project, the same approach is tested against your existing forecast, your data quality, and the actual cost impact for your business. building blocks ## What we build Six typical areas, always embedded in a clear process. 01 ### Data-driven process tools Systems that bring data from different sources together and turn it into a clear workflow, for dispatching, planning, controlling, customer service, or internal steering. 02 ### Forecasts and early indicators Software that uses historical data to read what's likely coming next: demand, capacity, volumes, risks or operational deviations. Always with a clear baseline and an analysis you can follow. 03 ### Classification and prioritisation Systems that pre-sort incoming items automatically: enquiries, emails, tickets, documents or internal reports. Your team decides faster because the case is already prepared. 04 ### Text and document processing AI support for recurring text work: pulling out information, structuring content, drafting, reviewing documents, making unstructured sources usable. 05 ### Reporting and decision support Automated analyses you no longer have to stitch together from Excel, ERP and CRM by hand. Reliable numbers, clear history, less copy-paste. 06 ### Cost optimisation Systems that surface cost drivers and prepare decisions: procurement, energy, capacity, inventory, or make-or-buy. Data is prepared so the savings case is defensible and actually used day-to-day. ai components ### AI where it actually helps We use machine learning and AI only where they hold up technically and where they fit inside a stable process. Some problems need a model. Others need better data structure, cleaner interfaces, or a simple internal tool. The right answer is the one that holds up in daily use. fit ## Who this is for This page is for companies that … have a lot of data but no reliable way to use it build their analyses manually in Excel have to prepare decisions out of several systems want to reduce recurring text or document work want to use forecasts or classification operationally want to test AI without committing to a risky large-scale project process ## How we approach it 01 ### Understand the process We talk about the actual workflow, not technology in the abstract. 02 ### Look at the data We work out which data is there, how reliable it is, and where the gaps are. 03 ### Assess the value We check whether data science, machine learning, or plain old software has the bigger lever. 04 ### Build the system Once the value is clear, we build a usable system with the interfaces, UI, hosting and maintenance to match. ## An initial conversation about your process 30 minutes, a structured working session [](/en/kontakt)[](/en/it-beratung/reifegrad-check) You walk away with a clear basis for deciding your next step.