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Artificial intelligence for companies in Italy

Faraday AI Studio builds chatbots, agents, automation, custom software and data products for companies in Italy. We start from the process, not from the technology.

Which problem do we start from

We don't start from technology. We start where the work gets stuck: a process that no longer scales, an archive nobody can query, a flow that crosses five different tools.

Repetitive requests

The same questions arrive every day across different channels and are handled by hand by people who could be doing something else.

Manual hand-offs

Copying data between systems, refilling forms, forwarding attachments: predictable steps, and therefore automatable ones.

Scattered documents

Contracts, specifications and procedures live in separate folders: the answer exists, but finding it costs more than it's worth.

Systems that don't talk

ERP, CRM and e-commerce stay isolated, and every reconciliation turns into recurring manual work.

Data that is hard to query

The data is there, but technical skills are needed to get an answer the business needs right away.

Flows that break at peaks

The process holds on an average day and collapses under load, which is exactly when it matters most.

Decisions made on instinct

Demand, stock or churn estimated by feel when there is already enough history to build a testable forecast.

Knowledge tied to people

Procedures and criteria live in a few people's heads: when they are away, work slows down and quality depends on who answers.

Spot checks only

Documents, orders or content reviewed by hand on a fraction of the volume, with errors surfacing only downstream.

If none of these describes your situation, an AI project probably isn't the priority right now: we prefer to say so before starting.

Services

Chatbot, agent or automation?

They are different tools and they solve different problems. This is the rule of thumb we use when choosing.

AI chatbot

When the value sits in the conversation: answering, guiding, collecting information on a channel where people already write.

Automation

When the flow is deterministic: stable rules, predictable inputs, an outcome you can verify without interpretation.

AI agent

When the process is multi-step and requires querying tools and choosing the next action, with explicit permissions and approvals.

Custom AI software

When you need an actual product: interfaces, roles, your own data and logic no off-the-shelf tool covers.

Predictive analytics

When reliable history exists and a future decision needs support: demand, maintenance, customer churn.

Data engineering

When the constraint is the data, not the model: quality, access, pipelines and documentation come first.

When AI is not the right answer

Useful advice includes knowing when to stop. These are the cases where we suggest a different route, or none at all.

The workflow is already simple

Few exceptions, stable rules, low volumes: a macro, a structured form or a setting in your existing software solves it without a model to maintain.

Deterministic automation is enough

If the outcome can be computed from explicit conditions, a rules engine is cheaper, faster to verify and needs no output-quality checks.

The data is not there

Fragmented history, archives nobody can query, inconsistent fields: fix the data foundation first, or the model will learn the existing mistakes.

The process is still changing

Automating an unstable process locks in a temporary version. Stabilise it first, then decide what is worth delegating.

Cost outweighs benefit

Rare or already fast tasks rarely justify integration, oversight and recurring inference costs. That comparison belongs before the project, not after.

The decision must stay human

Clinical, legal, disciplinary or contractually binding calls: the system can prepare the material, the decision stays with a named person.

AI connected to the systems you already run

A demo works in isolation. A business system has to read and write where the work happens, with permissions, error handling and predictable costs.

CRM and business software

Records, opportunities, orders and tickets: AI updates what already exists instead of creating a parallel archive nobody reconciles.

ERP and internal databases

Stock, price lists, jobs and history read through dedicated connections, with access limited to the tables actually needed.

Email, channels and ticketing

Shared inboxes, WhatsApp Business, forms and helpdesks: wherever repetitive requests land is usually the first thing worth covering.

Documents and cloud storage

Folders, DMS and internal repositories become searchable through semantic retrieval, keeping the permissions you already defined.

E-commerce and catalogues

Product data, availability, shipping and after-sales support, kept in sync with the platform in use.

APIs and proprietary software

Where a programmable interface exists we connect to it; where it does not, we assess the sustainable option together with your vendor.

We do not advertise ready-made connectors: every integration is verified against the system documentation and the actual permissions before it enters the project scope. AI integration via API

How a project takes shape

Four phases, each with an output you can check. If an output doesn't arrive, the project stops there instead of drifting forward.

Understand the flow

We rebuild the process with the people who run it: volumes, exceptions, tools and the points where work piles up.

Pick the first use case

We rank use cases by frequency, cost of error and data quality. The first one is the most verifiable, not the most ambitious.

Build on real data

A prototype wired to the actual systems, with evaluation criteria agreed upfront: test cases, expected outcomes, human-intervention threshold.

Move to production

Limited rollout, role-based permissions, action logs, team training and maintenance over time.

How we work

No list of adjectives: these are the operating conditions under which we take on a project.

Analysis and build in one team

The people who study the process are the same ones who write and maintain the code: no hand-off between advisor and vendor.

Built onto existing systems

We connect to the ERP, CRM and channels already in use. Having to switch platform to make AI work usually signals a badly framed project.

Your code, your configuration

Source code, prompts, data pipelines and documentation belong to the company when the project closes.

Vendor-independent stack

We pick the model per use case — commercial providers or open-weight models — and keep the architecture portable so it can be swapped.

Ownership after release

The hard part starts in production: answer quality, edge cases, cost, model updates, rule revisions.

Stated governance

We write down what the system may decide alone, what needs human approval and how actions stay traceable.

Data, security and human oversight

Architecture is designed around the type of data involved, the purpose, access, hosting and the requirements that apply to the sector. There is no single badge valid for every project: there are documented choices, discussed before any code is written and auditable after release.

Data perimeter

We define which data enters the system, where it is processed and how long it stays available.

Access and traceability

Role-based permissions, logs of automated actions and the ability to reconstruct what happened and when.

Human oversight

Decisions affecting people, contracts or payments go through a person, not the model alone.

Regulatory references

We work with the GDPR and Regulation (EU) 2024/1689 (AI Act) in mind, in particular the transparency duties that apply when a person interacts with an AI system.

References

AI changes with the sector

A restaurant, a law firm and a manufacturer share neither processes, data, software nor risks. Our sector guides start from that difference.

Restaurants

Bookings, reviews, recurring orders and unpredictable service load across scattered channels.

Law firms

Research across filings and precedent, document handling, case confidentiality.

E-commerce

Product content, pre and post-sales support, returns and demand planning.

Manufacturing

Quality control, maintenance, technical documentation and workplace safety.

Publishing

Editorial production, archives, rights and transparency on generated content.

Explore all sectors

A team, not a platform

Faraday AI Studio is based in Rome and works with companies across Italy. The studio combines software development, data engineering and communication, with specialists selected project by project according to the skills required.

FAQ

Where should we start?

From the process that costs the most time and produces the most errors today, not from the most interesting use case. In the first phase we map volumes, exceptions and available data: that map tells us which intervention is actually sustainable.

How much does an AI project cost?

We don't publish a price list, because cost depends on the number of integrations, data volume and quality, channels involved, security requirements and the level of oversight needed. After the analysis the first scope is quoted at a fixed amount with explicit stop criteria.

Do we need a lot of data already?

It depends on the use case. An assistant answering over company documents works with a modest archive; a predictive model needs consistent history over a long enough period. When in doubt, the first check is about the data, not the model.

Does our data stay confidential?

Processing is defined in the contract: which data enters the system, where it is processed, who can access it and how long it remains available. Where confidentiality is critical we consider EU processing, self-hosted open-weight models or architectures that send nothing to external services.

How long does it take?

A narrow automation and a multi-role software product with integrations are very different. We agree on milestones with verifiable outputs instead of a single delivery date: it is the only way to notice early when something isn't working.

Which technologies do you use?

We choose the model per use case, across commercial families (OpenAI, Anthropic, Google, Mistral) and open-weight models when control, local processing or a different cost profile is needed. The architecture stays portable: replacing a provider must not mean rewriting the system.

What happens after release?

We stay on the project: monitoring answer quality, revising rules and prompts, updating models, handling edge cases. Support terms are set in the contract according to how critical the system is.

Do you only work in Rome?

The studio is based in Rome. Projects run largely remotely, with on-site meetings when they genuinely help: field analysis, team training, visits to plants or stores.

Is there a process you want to improve?

You do not need to arrive with a defined solution. Tell us where time is lost, which systems you run and what outcome you expect: we assess whether the problem calls for AI, automation, custom software or a combination. If the answer is that no AI project is needed, we say so.