AI data analysis and dashboards for businesses in Corsica
AI data analysis turns your sales, booking, production or invoicing figures into information you can act on: trends, forecasts, anomalies, priorities. From Bastia, we build tools that make use of the data you already have and present it in a clear dashboard.
Business data analytics with AI: what does it involve?
Every business produces data: sales, quotes, bookings, stock, call-outs, emails, customer reviews. It often sits idle in spreadsheets, a till system or management software, used for little more than the year-end accounts.
A data analysis tool brings this information together, cleans it up and makes it readable. Artificial intelligence adds three capabilities: forecasting, meaning estimating future demand from past history; classifying, sorting thousands of rows, requests or comments into categories automatically; and spotting, flagging anything out of the ordinary.
These techniques come from machine learning: programs that learn patterns from past examples. Language models (LLMs), which can understand text, also make it possible to analyse written content such as reviews or emails, and to query your data in plain language.
AI data analysis: examples by sector
- 01
Retail
Sales forecasts by product and period, help with restocking, spotting items that have stopped selling, basket analysis.
- 02
Tourism and hospitality
In an economy as seasonal as Corsica's, anticipating occupancy from bookings already made, analysing reviews by theme and tracking booking channels.
- 03
Trades and construction
Profitability per job, quote conversion rate, actual time spent compared with time planned.
- 04
Professional practices
Activity tracking by file or client, billable time, late payments, deadline alerts.
- 05
Local authorities
Residents' requests classified automatically, processing times tracked, dashboards on the services delivered.
The smart dashboard at the heart of the tool
A smart dashboard does more than display charts. It highlights what needs your attention: an unusual drop, stock running out faster than expected, a customer who has stopped ordering, a forecast drifting away from target.
It can also provide a written summary of the period, drafted by a language model from the figures, and answer simple questions in plain language, such as: which products grew most this quarter? The figures themselves are still calculated in the conventional, verifiable way; AI is there to explain and highlight them, never to invent them.
We design these screens around how you actually work: a handful of well-chosen indicators, viewable on a computer or a phone, rather than a sprawl of charts nobody looks at.
What you gain by putting your data to work
- 01
Decisions based on facts
Purchasing, pricing and staffing choices rest on up-to-date figures rather than gut feeling.
- 02
Anticipate instead of react
A forecast, even an imperfect one, helps you adjust purchasing, staffing or cash flow ahead of the busy period.
- 03
Less time spent compiling
Figures update automatically: no more consolidating spreadsheets by hand at the end of every month.
- 04
Early warning signs
Anomalies and subtle trends surface on their own instead of being discovered too late.
How we build your data analysis tool
- 01
Understand
Which decisions do you want to make better, and with what data? We inventory your sources, assess their quality and clarify your goals, budget and timeline.
- 02
Design
Choice of indicators, forecasting or classification methods, hosting and connections. Dashboard mock-ups are approved by you before development.
- 03
Build
Connecting the sources, cleaning the data, developing the models and the dashboard. Forecasts are tested against your own history to measure how reliable they are before anyone relies on them. You follow progress throughout.
- 04
Launch and support
Go-live, hosting, maintenance. Models are reassessed as new data comes in and your business evolves.
Data quality, reliability and confidentiality
- 01
Data quality
Duplicates, empty fields, different labels for the same product: an analysis is only as good as the data behind it. Cleaning and data-entry rules are part of the project.
- 02
Available history
A forecast relies on the past. With little history, or a business that has just changed, it must be read with caution, and we will say so.
- 03
Explainability
You need to understand where a figure or an alert comes from. We favour methods that can be explained simply.
- 04
Personal data
Customer, employee or patient data falls under GDPR. We limit the analysis to what is necessary, favour aggregated data and choose suitable hosting, including local AI where sensitivity demands it.
What affects the cost of a data analysis project
- 01
Number and type of sources
A single management system, or several tools, spreadsheets and exports to reconcile.
- 02
State of the data
Clean, consistent data needs less preparation than mixed files maintained by hand.
- 03
Type of analysis
A dashboard of indicators takes less work than a forecasting or classification model to train and validate.
- 04
Access and follow-up
Number of users, access rights, update frequency, hosting, maintenance and model reassessment.
Technologies and methods
- Machine learning
- Forecasting
- Classification
- Dashboards
- SQL
- TypeScript
Frequently asked questions
Do you need a lot of data to use AI?
Not necessarily. A useful dashboard can be built from the data in a single management system. Forecasts, on the other hand, need enough history: we assess what your data allows before proposing anything.
My data is spread across several spreadsheets: is that a problem?
It is a very common situation. We can bring them together and clean them up, then, if useful, suggest a sturdier set-up for the future.
How reliable are AI forecasts?
They give an estimate, never a certainty. We measure their accuracy on your own history before go-live and show a margin of uncertainty, so you know how much weight to give them.
How is this different from pivot tables in my spreadsheet?
Spreadsheets remain useful for one-off analyses. The tool we build updates automatically, combines several sources, adds forecasts and alerts, and is available to the whole team according to each person's access rights.
Can I query my data in plain language?
Yes. A language model can translate a written question into a query on your data, then phrase the answer. We keep this feature tightly controlled so that the figures returned stay accurate and verifiable.