# Service: Predictive Analytics with AI Source: https://lean-coders.at/en/service-predictive-analytics-mit-ki > Predictive analytics and AI forecasting from Vienna – from Excel chaos to real insights. GDPR-compliant, without vendor lock-in. Excel is not a forecast LEAN Evolution: Predictive Analytics with AI Analyses and Forecasts without Excel Chaos 1. Overview 2. Challenges 3. Benefits 4. Pilot Phase Your management makes decisions based on Excel exports that are three weeks old. We build AI-based forecasts, predictive analytics, and automated classification – on your data, not on generic models. ## What is it about? For companies whose decisions are based on outdated Excel exports. For teams that suspect patterns in their data but cannot make them visible. For management that wants forecasts instead of retrospectives. **Your benefits:** - Decision quality ↑ - Forecast accuracy ↑ - Reaction speed ↑ ## Do you know? Decisions based on outdated data Outdated data as a basis for strategic decisions. Reports are manually compiled – by the time they are finished, they are outdated. Data is lying fallow The data is there, but no one has the time or tools to recognize patterns. Forecasts are based on gut feeling instead of statistical models. Existing BI tools are no longer sufficient Dashboards show what was, not what is coming. For predictive analytics, internal know-how is lacking – and the fear of AI projects is significant. ## What does this bring you? Forecasts instead of retrospectives ML models predict revenue, demand, utilization, or churn based on your own data. Decisions are based on what is likely to happen – not on what happened three weeks ago. Data quality becomes visible Exploratory analysis shows where your data is strong and where the gaps are. Before we build models, you know what your data foundation offers and what it does not. Interactive visualization instead of PDF reports Dashboards that your management can operate themselves. Filters, drilldowns, scenarios – live, not as an attachment in last week's email. GDPR-compliant and without vendor lock-in Models run on your infrastructure, with your data. No dependency on external AI platforms, no data leakage to third parties. ## Pilot Phase Deliver first, then commit. That’s what the pilot is for. Duration 6-10 weeks Assessment Which data sources are available? Which use cases have the greatest leverage? What is the state of data quality and access? Derived from this Use case, model approach, dashboard scope Deliverables Exploratory Data Analysis and documentation of data quality/gaps Implementation of a first forecast model for 1 defined use case Dashboard Implementation of a sample interactive dashboard for result visualization Deployed for internal demo purposes ## Frequently Asked Questions FAQ ### Do we need clean data before you start? No. The exploratory data analysis is part of the pilot. We document data quality and gaps and show you what is possible with the current state – and what you need to improve to get more out of it. ### Which use cases are suitable for ML forecasting? Anything where historical data contains a pattern: revenue forecasting, demand predictions, churn prediction, classification of support requests, anomaly detection, capacity planning. In the assessment, we find the use case with the greatest leverage. ### Do we need to build a data science team? Not for the start. The pilot delivers a runnable model and a dashboard that your department can use. Whether you build your own know-how long-term or we take over in scale is up to you after the proof. ### Where do the models run? On your infrastructure – on-premise or in the cloud, depending on your requirements. For GDPR-relevant data, we recommend European data centers or self-hosting. No dependency on external AI platforms, no data to third parties. You retain full control. ### How accurate are the forecasts? Depends on your data – that’s why we measure in the pilot. Typical metrics are MAE, RMSE, or F1-score. In the proof, we transparently show you what the model can do and where the limits are. ### What is the pilot phase? A clearly defined project with a defined scope – typically 4–12 weeks. You don’t get a concept paper at the end, but a functioning result: real code, tested and deployed. The pilot shows you what we can do before you decide long-term. ### What happens after the pilot phase? After the pilot comes the proof: We look together at the results – what worked, what was worthwhile, where are the gaps? Everything measured against defined KPIs, not gut feeling. Based on this, you decide: scale, adjust, or stop. No pressure, no upselling. If the proof convinces, we go into scale – your project grows, your team grows with it, the knowledge stays with you. ### Do I have to start with a pilot phase? No. The pilot is our recommended entry point because it creates clarity for both sides – but it’s not a must. If you already know what you need and want to start directly, we can also join an ongoing project or start directly in a larger scope. We adapt to your pace. ### Do you work T&M or fixed price? Start as a timeboxed pilot in T&M (optional with cap). No fixed price risk, no lock-in. You see at any time what you are paying for – and can stop at any time. But very few do. Book a discovery call with your expert now This fella glances at your data and spots the patterns – before you've finished explaining the problem. Our early insight system in human form.