A dashboard that doesn't just show what has happened, but predicts what is going to happen. Demand, churn, stock, capacity, anomalies in the financial close: powered by an ML pipeline under the bonnet, a custom visualisation layer on top, and a natural-language layer for people who don't write SQL.
A classic BI dashboard looks backwards. A predictive analytics dashboard looks forwards: it delivers a forecast for next week, month or quarter, with a confidence margin and an explanation of why the model gives that outcome. The difference isn't a trend line in Excel, but an ML pipeline that continuously retrains on new data and adapts to reality.
We build that pipeline plus the dashboard on top as one cohesive platform. Under the bonnet sits a data warehouse, feature store, set of ML models and orchestration layer; on top, a custom visualisation layer with the charts, filters and alerts your team uses every day. We have been working on AI development projects since 2015, with data pipelines in production at retailers, financial institutions, logistics providers and SaaS companies.
The question we start with is almost always the same: which decision is made on the basis of this forecast, and by whom? A demand forecast that drives purchase orders requires different accuracy and explainability than a churn score that allocates marketing budgets. Forecasts that nobody uses to take action are expensive decoration. And always an audit trail of model versions, features used and predictions made: once a model supports decisions that affect people (HR, customer assessment, credit), the EU AI Act comes into play.
Demand forecasting, inventory optimisation and churn prediction are the most common use cases. Demand forecasting combines sales data with seasonality, weather, promotions and price changes to produce a forecast per SKU per region. Models that work well here include Prophet for seasonally dominated series, and XGBoost when there are many exogenous features. On top of a demand forecast we often build an inventory optimiser that takes into account lead times, minimum order quantities, storage costs and stock-out costs. For SaaS and subscription businesses we deliver churn scores per active customer, plus the top three reasons the model flags (gradient boosting with SHAP), linked to your CRM so the score lands automatically in HubSpot or Salesforce.
Anomaly detection in operations and financial close flags transactions, stock movements, energy consumption or ledger entries that deviate from the normal pattern. For finance, this saves time during the monthly close: instead of checking thousands of entries, the controller receives a short list of suspicious items. Models include Isolation Forest, autoencoders for multivariate patterns, and statistical detectors for time series with a stable baseline.
Capacity, headcount and financial close forecasting. For service providers managing by billable utilisation, a capacity forecast based on pipeline and historical conversion gives early signals for recruitment. For CFOs, an early-month close forecast provides a sense of direction rather than waiting until after the close. The output often feeds into our custom KPI dashboard, as predictive figures belong in broader management reporting.
Most predictive projects fall into one of these three profiles. Which one fits depends on the business decision, your organisation's data maturity and the compliance context. In the first conversation, we advise which profile is right, and we are honest when a standard BI tool is simply the better choice because the predictive element adds no value (yet).
One clear use case, such as SKU-level demand forecasting or a churn score per customer, presented in a dashboard that runs on your current data stack. We train the model in Python (scikit-learn, XGBoost or Prophet), deploy it in production behind a REST API, and build a React dashboard on top using Plotly or D3. Your team sees the prediction, the top features the model uses, and a comparison between prediction and actual outcome. This is often a sensible first step for organisations that want to complement their existing BI tool with a predictive layer without rebuilding the entire stack.
Multiple models running side by side on a shared data warehouse (Snowflake, BigQuery, Azure Synapse or Redshift), with a feature store that shares features across models. For example, a retailer with a demand model per category, a churn model for loyalty programmes and an anomaly detector on the transaction stream. Orchestration via Airflow or Prefect, MLOps for drift detection, and a dashboard layer where all predictions come together. Each use case has its own view, but there is one central layer for governance and monitoring.
For organisations that need forecasts on live data, such as logistics, financial markets, energy trading and fraud detection, we build a streaming pipeline on Kafka or Materialize, with models that update incrementally rather than in a nightly batch. On top of that platform sits a natural-language layer powered by an LLM (Claude or GPT-4): your manager asks a question in plain Dutch, and the system generates the SQL and the visualisation. Explainability via SHAP or LIME means every prediction comes with an explanation page that can be presented to a controller or compliance officer. This usually calls for a more thorough AI development approach and often a consultancy phase upfront to prioritise the use-case portfolio.
A working predictive platform, not a pilot prototype that gathers dust after three months. Plus everything around it needed to manage, retrain and extend it yourselves, without vendor lock-in to a commercial ML platform.
Four patterns where we help organisations build their own predictive platform. Recognise one of them? Then let's talk further. If you don't, a classic BI dashboard is probably the better choice; predicting without a decision attached to it is expensive decoration.
You order based on gut feeling or trend, and reality regularly proves you wrong. A demand forecast per SKU based on seasonality, weather and promotions significantly improves your chances of getting purchasing right. Often paired with building a BI tool when the wider reporting layer is being renewed at the same time.
You only find out a customer has gone when the cancellation email arrives. A churn score with top reasons per customer gives customer success a targeted priority list and gives marketing an audience for retention campaigns. Works best with a few hundred to several thousand active customers.
Your controller manually works through thousands of entries every month. An anomaly detector on your general ledger produces a short list of suspicious transactions, with the reason for each item. It can be combined with a close forecast that gives direction early in the month, and extended via our KPI dashboard page towards broader management reporting.
You're late to hire or bring in contractors because you only react once the bottleneck is visible. A headcount or capacity forecast based on pipeline data and historical conversion shows you what's coming earlier, early enough to act effectively. Applicable in service businesses, contact centres, clinics and agencies.
A conversation to understand which decision a prediction should support, who will act on it, and what data is available. We check straight away whether the business case justifies prediction or whether a backward-looking dashboard is enough; often a standard BI tool is the right step. An exploratory consultancy phase can also be run through our AI consultancy approach.
A workshop with your data team plus interviews with the end users. We map the sources, identify which features are likely to be predictive (and which contain data leakage because they only become available after the prediction point), and design the pipeline architecture. At the end: a concrete scope, tech stack choice and a baseline measurement of your current «forecasts» (often Excel trend lines or gut feel).
In the first sprints we train one model on your existing data and build a first dashboard view on top of it. Not a Kaggle experiment, but the simplest configuration that actually beats the baseline; surprisingly far can often be achieved with XGBoost or Prophet on well-built features. Your team tests alongside us.
The model and dashboard are embedded in a production pipeline: orchestration (Airflow or Prefect), experiment tracking (MLflow), drift detection and a feature store that makes derived variables reusable across models. From here, a second and third model can be added on the same infrastructure.
Before going live: GDPR DPIA, AI Act classification, penetration test, audit trail review and the explainability layer (SHAP per prediction). For sector-specific regimes (NEN 7510 in healthcare, DORA in finance), the additional governance documentation. Models that directly affect people get extra human oversight built in.
Phased rollout, training sessions for key users and ongoing management of security and model performance. Retraining on a schedule that suits your data velocity. Expansion to further use cases on the same infrastructure in subsequent sprints.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →The data warehouse layer is the central place where all sources come together: Snowflake for multi-cloud setups or heavy compute spikes; BigQuery for Google Cloud or pay-per-query; Azure Synapse for Microsoft environments; Databricks when ML and data engineering need to live on one platform. For real-time cases, a ClickHouse or Materialize often sits alongside. We stay vendor-neutral across all four.
We build the ML pipeline in Python: scikit-learn for straightforward classification and regression; XGBoost or LightGBM when there are many structured features and you need SHAP explainability; Prophet or statsmodels for classical time series; PyTorch where a neural network genuinely adds value. For most use cases, a well-tuned XGBoost outperforms a PhD-level neural network and is far easier to maintain. Orchestration runs on Airflow or Prefect, with MLflow as the experiment tracker so every run is reproducible.
The visualisation layer depends on the audience: Plotly Dash or Streamlit for data analysts and domain experts; a bespoke React front end with D3 or ECharts for end users who expect a polished interface. We build the AI layer with Claude or GPT-4, using a text-to-SQL controller that validates queries against an allowlist, and a semantic layer (dbt or similar) that makes business definitions explicit. The compliance layer is embedded in every component: PII redaction, EU data residency, an audit trail of model versions and predictions made, model cards, and human oversight where the AI Act requires it.
"Why not just use Power BI or Tableau?" comes up in almost every first conversation. For classic BI (looking back at your own data, ad hoc queries, standard reporting for your own team), both tools are excellent, and a custom build would waste both money and time. We don't build a replacement for Power BI; if you only need reporting, that is usually the right route.
The picture changes once machine learning enters the picture. Power BI has AI Insights and some AutoML, Tableau has Einstein Discovery. Both work well for demo cases, but they run into trouble as soon as you want to define your own features, train non-trivial models, or version and roll back models. For serious predictive work, a custom Python pipeline alongside (or instead of) standard BI is needed. Beyond that: vendor independence (Power BI ties you to Microsoft, Tableau to Salesforce, Looker to Google), code ownership (all code is yours, with no lock-in), and model explainability (SHAP and LIME are mature tooling within Python pipelines and are not standard in Power BI or Tableau, and "Power BI has it covered" is not an acceptable answer to a regulator under the AI Act). If you recognise yourself in any of these four, a custom platform is the logical step; if you recognise none of them, our honest advice is to use Power BI or Tableau and come back to us when you get stuck.
What clients typically want to know before we start on a predictive analytics dashboard.
A free, no-obligation 30-minute introductory call. We'll discuss which decision you want to support with predictions, which data sources are ready, and what compliance context applies. We'll also tell you honestly whether a predictive platform is the right route, or whether a backward-looking BI dashboard will deliver more value for now.
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