Artificial intelligence, data and analytics
Artificial intelligence applied to concrete problems: automation of repetitive tasks, support chatbots, business-intelligence dashboards, data cleaning and migration, reporting and geographic information systems.
Artificial intelligence is useful when it solves a concrete, measurable task: classifying incoming documents, extracting data from scanned forms, answering the twenty questions that dominate customer support, or anticipating warehouse demand. Framed as a goal in itself, it almost always ends as a pilot nobody uses.
Before any model gets proposed there is a precondition that is rarely met: the data. If information is scattered across spreadsheets in different formats, duplicated and unvalidated, the first job is to put it in order. A reliable dashboard on clean data usually delivers more immediate value than any predictive model on data nobody has reviewed.
What this covers
- Automation of repetitive tasks and document workflows
- Chatbots and assistants for customer support and internal queries
- Data extraction from scanned documents and forms
- Business-intelligence dashboards
- Data cleaning, deduplication and migration
- Automated, recurring reports for management and donors
- Prediction and classification models for specific cases
- Geographic information systems (GIS) and data mapping
Typical engagements
Public sector
Dashboards with indicators per directorate, automatic classification of incoming applications and archive digitisation with data extraction.
Private company
Demand and inventory forecasting, automated sales reporting and assistants that answer the most repeated queries.
Organisations and NGOs
Consolidation of data from several sources, project indicator dashboards and automatic donor-report generation.
Startups
Product analytics from day one, and model-based features where they add real user value.
A clear, five-step approach
No surprises and no unexpected invoices. You approve the plan before the first line of code is written.
Discover
Understand your goals, your users and your real constraints: budget, timeline, connectivity, available staff and legacy systems.
Design
Technical architecture, user experience and a written plan that you approve before a single line of code is written.
Build
Iterative development with frequent demonstrations. You see real progress every week, not a status report.
Deploy
Secure launch, verified data migration and training for the team that will use the system every day.
Support
Maintenance, monitoring, backups and continuous improvement under a clear agreement.
Frequently asked questions
Does AI make sense for a small organisation?
Sometimes yes and sometimes no, and saying so honestly is part of the job. Automating repetitive tasks and building a good dashboard usually pay off immediately. A custom predictive model needs data volume and quality that many organisations do not yet have.
Where is my data stored?
Wherever you decide, and it is written down before work starts. Local, cloud and hybrid hosting are all options. Where personal or sensitive data is involved, the decision is made explicitly rather than by default.
What do I need to have in place to start?
Whatever data you already have, in whatever state it is in. Disorganised spreadsheets are the usual starting point, not an obstacle: tidying them is typically the first phase of the project.
Can we run a pilot before committing?
Yes, and it is the recommended route. A scoped pilot on a real use case, with success criteria defined in advance, lets you decide on evidence rather than expectation.
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