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Frame the use case
We reconstruct the current workflow, the decision to support, the users involved, and the constraints the project must respect.
For companies and product teams deciding whether, where, and how to apply artificial intelligence.
AI consulting services for deciding what is worth building
We examine the use case, data, integrations, and operational constraints. The outcome is a reasoned decision, supported when needed by a measurable technical proof.
Assess the use caseDecisions to make before writing code
- Define the workflow, user, and metric the system is expected to improve
- Check data quality, availability, permissions, and provenance
- Compare custom development, existing services, and deterministic solutions
- Design integrations, human review, and error handling
What the engagement produces
- A documented assessment of opportunities, constraints, and risks
- Architecture options with explicit dependencies and assumptions
- A proof of concept using agreed data and test criteria
- A technical roadmap for implementation, integration, or handover
From a business question to a technical decision
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Test the uncertain part
We evaluate the main risk using representative data, a baseline, and metrics defined before the experiment.
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Choose the next step
We deliver findings, limitations, and options: stop, integrate an existing product, or proceed with development and deployment.
Consulting that reaches the working system
Screeba, Mirror, and 2Sequel show three different paths: turning audio into documents, making open data queryable, and providing controlled natural-language access to databases.
Screeba
An AI product that turns lecture recordings into structured Word notes ready for study.
Read the case studyMirror
A Data Ops platform making ISTAT open data easier to use through documentation, APIs, search, and guided interfaces.
Read the case study2Sequel
An enterprise text-to-SQL interface designed to make data queryable without giving up control or auditability.
Read the case studyQuestions about AI consulting
Do you always start with an AI model?
No. We start with the decision or workflow to improve. If deterministic rules or an existing product solve the problem better, we make that clear.
Do we need clean data already?
No, but we need to understand what data exists, how it is collected, and whether it can be used. Data preparation may become the first output of the engagement.
What is the difference between an audit and a proof of concept?
An audit clarifies opportunities, constraints, and options. A proof of concept tests a narrow technical question with defined data and evaluation criteria.
Can you work with our internal team?
Yes. We can work alongside product and engineering, then transfer decisions, code, and documentation at the end of the phase.
Do you have a workflow, data, or an assumption to test?
Describe the use case, who makes the decision today, and what data is available. From there, we can determine whether you need an audit, a technical proof, or neither.
Discuss the use case