AI tools we build, not resell.
We do not sell “AI”. We build systems that solve a problem you recognise: a knowledge base that answers by citing your documents, agents that propose rather than decide, costs that stay under control.
AI-native, not AI-first
AI enters where it solves a concrete problem — information that exists but you cannot find, data copied by hand, a team that cannot keep up. It does not enter as a label.
The problem first, then the technology
Baseline defined before we start
The final word stays with a person
Three ways to put AI to work
Depending on what you need: a base already built, a solution for your specific case, or a path to adopt it without burning budget.
AIA Platform
The business AI platform we already built: hybrid knowledge base, agents, desktop agent and governance dashboard. We adapt it to your context instead of starting from scratch.
- Hybrid knowledge base: vector + graph
- Agents per function, under supervision
- Desktop agent with human approval
- Costs and usage separated per company
Custom AI solutions
We build AI for your process: assistants over your documents, agents, automation and integration with the systems you already use.
- Assistants over your documents
- Automation of repetitive steps
- Integration with Italian ERPs
- Document classification and extraction
AI Method
A four-phase path to adopt AI without waste: assessment, prioritisation, guided adoption and measurement of the result.
- Maturity assessment
- Use cases ordered by impact
- Policy and governance
- Before/after comparison
How it starts: four phases, nothing behind a curtain
It is the same path we use to decide whether AI is worth it — including when the answer is no.
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Assessment
Interviews with decision makers and with the people doing the work, analysis of high-friction processes, real state of data and documentation. You leave with a picture, not a deck.
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Governance and priorities
We define what is allowed, which data does not leave, and which cases to focus on. We agree on how the result will be measured before, not after.
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Guided adoption
The tools enter real workflows, with hands-on sessions on everyday tasks and regular checks on quality, cost and security.
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Measurement and autonomy
We compare the numbers against the starting baseline. The team uses the tools on its own; we stay where we add something.
What you get
Written assessment, policy, prioritised use cases, a working system and a before/after comparison. The criterion is declared up front: without a measurable problem, we do not start.
No slides: tools that exist, with the code behind them
Behind this page there are no presentations. There are systems we built and use: here is what they contain, concretely.
Hybrid knowledge base
Semantic search, full-text and graph navigation over your documents, with results combined by rank. Not keyword search: every answer can cite the source it comes from.
Knowledge graph
Documents, tickets, services, files, people and decisions become connected nodes. It answers questions similarity alone cannot: what depends on what, who decided, what breaks if this changes.
Agents with a contract
Multi-step flows with explicit limits on iterations, tokens and available tools. Every step leaves a trace: what ran, under which permission, with which outcome.
Permissions inside the query
Access control is a condition in the query, not a filter applied afterwards. Unauthorised users never receive the data: they cannot see it or cite it. Without permissions, no results.
Documents as untrusted content
A document is checked before it enters the index: credentials and secrets block it, malicious instructions are quarantined. What is written in a document does not become an order for the system.
Desktop agent under supervision
AI works on the PC: reads files, runs commands, fills forms, looks at the screen. Every action is classified by risk, and sensitive ones go through human approval.
Real integrations (MCP)
Jira, Confluence, Grafana, GitLab, Neo4j, email, calendar, Office, business systems and browsers. Agents talk to the tools you already use through an explicit integration layer.
Evaluation before enthusiasm
A set of test cases with declared thresholds measures answer faithfulness, relevance and retrieval coverage. If it does not pass the measure, it does not go into use.
Costs under control
A single gateway for every model call, with routing by cost and complexity, quotas per user or company and usage tracking. The cheap model does routine work, the stronger one only where it matters.
Where AI enters, without becoming a separate service
AI is not a separate channel to buy. It enters the two ways we work, when it genuinely helps.
Technical Direction
In the governance of team and system: where AI pays off, how it is measured, which policies are needed, how people are trained. Before writing a line of code.
Technical Direction →Software in Hand
In the product: knowledge base, agents, data extraction, automation, integration with existing systems. When AI is the right way to solve it, we build it.
Software in Hand →Fit / no-fit
It makes sense if
- You have documented processes, even partially, that you want to make accessible.
- The team wastes time searching for information or repeating manual steps.
- You want a system in use, not a demo that ends in a drawer.
- You accept defining a baseline before starting.
It does not make sense if
- You are looking for generic AI training without a concrete problem.
- You want to “add AI” without a business metric.
- You need someone to decide in your place: here the final word stays yours.
- You have no documents or data to work on yet — in that case we start from the assessment.
What we do not do
Saying it first is more useful than saying it later.
- We do not resell model licences or third-party tools.
- We do not promise full autonomy: decisions that matter stay human.
- We do not use AI as a sales argument in the first call.
- We do not start without a problem and a measure.
- We do not add AI where a simpler process solves the same problem.
The AI tools deck, ready to forward
For decision makers: why generic AI does not produce results, what we already built, three ways to put it to work and how to start without burning budget.
Download the AI Tools brochureFor consultancies and system integrators
When your client’s problem is AI: how to recognise it, what you can promise and what you cannot, and how to include us in a bid as subcontractor or co-delivery.
Download the partner kit