Forecasting and planning
Use historical data to support demand, stock or capacity decisions. We consider the planning horizon, available inputs and the cost of different errors, then compare the model with the approach your team already uses.
Common Sense AI · Belgium
Data Science & Analytics
We build forecasting, machine-learning and optimisation systems that expose patterns, improve decisions and keep working after the notebook is closed.
Where we help
Make your data earn its keep.
- Planning depends on estimates despite years of operational data
- Business teams need to understand the factors behind an outcome
- Manual analysis cannot keep up with incoming information
- Models exist in notebooks but lack reliable deployment and monitoring
In practice. A model needs to improve a useful baseline and fit the decision. Additional complexity has to earn its place.
What you leave with
- An assessment of the data and its limitations
- A model evaluated against a useful baseline
- Results explained in terms of the decision they support
- Production pipelines and an agreed feedback process
We agree the deliverables and responsibilities around your project before work starts.
The work
Connect the model to a decision.
We work with business and technical teams to define what needs to improve, how to measure it and how the result will be used.
Explainable machine learning and analytics
Explore the factors associated with an outcome and make the findings usable for business teams. We distinguish patterns in the available data from conclusions it cannot support, and explain uncertainty alongside the result.
MLOps and model operation
Help models move from analysis into repeatable deployment. Depending on the project, this can include data pipelines, model versioning, testing, monitoring and retraining workflows, with clear responsibilities for maintaining them.
Getting started
A clear starting point.
We use the first conversation to understand the question, identify who needs to be involved and agree a suitable next step. Scope, timing and commercial terms follow from that discussion.
- 01
Define the decision
Agree the outcome, baseline and cost of mistakes with the people using the result.
- 02
Assess and model
Explore data quality, build an appropriate approach and test it on suitable held-out data.
- 03
Explain and validate
Review results, uncertainty and practical usefulness with business and technical stakeholders.
- 04
Integrate and monitor
Agree how the result enters the workflow and how data changes and model performance will be reviewed.
Useful to bring to the conversation
- The planning or business decision you want to improve
- An overview of historical data, its coverage and known quality issues
- Your current baseline and the people who will use the result
An outline is enough. We can identify missing information together.
Relevant client work
See what changed.
Each case explains the problem, the approach and the context behind the result.
Practical questions
Before we start.
How much historical data do we need?
There is no single minimum that applies to every problem. We assess the coverage, quality, frequency and relevance of your data against the decision and the patterns you need to understand. That assessment informs what can reasonably be built.
Do we need generative AI for forecasting or analytics?
Not necessarily. Statistical models, conventional machine learning or a clearer reporting process may suit the task. We choose the approach around the data and the decision, and compare it with an appropriate baseline.
Can you work with models our team has already developed?
Yes. We can assess existing models, their evaluation and their deployment requirements. Our MLOps case describes taking two models developed and patented by the client into production on an Azure foundation.
What if our data is spread across several systems?
We first assess which sources are needed and how they can be accessed and combined. When the work also requires data engineering, architecture or business intelligence, we can bring in complementary expertise through Algorhythm Group within an agreed scope.
The team around the workFocused expertise.
Focused expertise.
Wider backing.
Common Sense AI works with enterprises, public organisations and mid-sized businesses in Belgium. Our team combines strategy, engineering and data science, with complementary expertise available through Algorhythm Group when your project needs it.
Services that work together
Projects can start at any stage. We can help explore a question, build a new solution or improve work already in progress.
AI Strategy & Adoption AI Engineering & Solutions About Common Sense AI and the group