Analysis
Before you invest in AI, we tell you whether your data is good enough and where you will save.
Sound familiar?
- You have data in your ERP, spreadsheets and emails, and nobody knows whether it is any use.
- You get AI proposals and cannot tell which one is worth it.
- You do not want to invest without knowing how much you will save.
What you get out of it
- You will know what data you have
- You will see where AI saves you time
- You will know what doing nothing costs
- You will have a phased plan
Understand your data before investing in AI.
We assess the structure and maturity of the client's data, identify the highest-ROI use cases, and define the adoption roadmap before building any model.
Data audit
We review where your data comes from, what state it is in and what is missing to use it: ERP, spreadsheets, emails and PDFs.
Use case discovery
We talk to the teams and map manual or repetitive processes to see where AI saves time or prevents mistakes.
Return estimate
We estimate the hours, cost and errors of each case, so you know what doing nothing costs before you invest.
Roadmap
We rank the cases by impact and effort and phase them, with a recommended pilot and the indicators to measure it.
How it is integrated and validated
There is nothing to integrate: the result is a report and a roadmap, validated with your team at the final presentation.
With management and with the people who do the work every day.
What exists, where it lives and how good it is.
Each case with its estimated return and difficulty.
Report and roadmap explained to your team.
How we do it
What you get
- 01Report on the state of your data
- 02Ranked list of use cases, with estimated return
- 03Indicators to measure each case
- 04Phased roadmap with the recommended pilot
Where it runs
We decide with you where the system lives, depending on how sensitive your data is.
On your servers
We install it on your infrastructure or in your cloud. Your data never leaves your environment; in exchange, someone on your side has to maintain those servers.
On Vidra's servers
We host it on servers inside the European Union and you access it via web or API. We take care of maintenance, and access is restricted to your team.
With external AI APIs
For language models we use the OpenAI, Anthropic, Google or OpenRouter APIs. It is the most capable option and the fastest to set up. The text fragments needed are sent to that provider, and we tell you which one is used and what data it receives.
With local models
If your data cannot leave your environment, we use models that run entirely on your servers or ours, without sending anything to third parties. They are somewhat less capable than the large API models and need more hardware.
From pilot to production
We start with a scoped pilot: one specific case, with your real data and measurable goals agreed up front. If they are met, we integrate it into your tools and it goes into production. If not, we explain why and what it would take.
After delivery
When the system goes into production, a feedback stage begins: your team uses it day to day and, if something fails or does not fit, we fix it. After that you can hire maintenance: incident handling, updates and, for models, retraining with new data.
What you hire and what we build it with
What you hire
A closed diagnosis: the report on your data, the use cases ranked by estimated return and a roadmap with the recommended pilot.
Under the hood
Data analysis with scripts and queries over your own sources. There is no system left to maintain.
- Python
- SQL
- Pandas
- Client ERP and Excel
We choose the tools for your case; you do not need to know them.
