Michele Sciabarrà is a Nuvolaris founder who works on AI products, including private AI applications that connect company data and systems to business processes. Sciabarrà has described focusing on product development and taking on the CTO role.
Michele Sciabarrà is most useful for private AI product strategy and concrete deployment patterns.
Best for
A B2B AI infrastructure company wanting scoped product strategy advisory on private AI and agents.
Not for
Paid LinkedIn sponsorship with fixed deliverables, CPM or CPC pricing, and broad English small-business reach.
Key takeaways
Private AI belongs inside business processes, not above them.
Six private-AI patterns include wiki chat, email order intake, reporting, dashboards, database queries, conversational CRM.
Public AI is generalist; private AI matters when agents touch email, CRM, NDA documents, industrial secrets.
Check first: Confirm recent post-level results and audience survey data for job function, seniority, company size, geography.
Main ideas
Private AI belongs inside business processes, not above them · The product matters more than the title, and the company should be structured to protect it · AI changes what software people do, not whether people are needed
Audience
46018 LinkedIn followers (account-level, not post-level)
Michele Sciabarrà is a Nuvolaris founder who writes about building AI products, especially private AI connected to company data and processes.
Their posts move between practical examples—such as turning email orders into forms for a business system—and bigger claims about how AI could change software and work.
They also explain the shift from CEO to product and technical leadership, describing it as a way to focus on the product.
Follow their work for concrete private-AI use cases and an ambitious, sometimes speculative view of where AI is heading.
Sciabarrà makes the case for private AI by tying it to specific business tasks rather than presenting it as just an internal chatbot.
They name examples including document search, order processing, reporting, industrial dashboards, database queries, and a conversational CRM, and stress the need to account for privacy, permissions, and existing systems.
The umbrella-and-coat analogy makes the complementary roles of public and private AI easy to grasp.
Their writing also looks beyond deployment examples: one post argues that AI-generated software will change programmers’ work and imagines more distant possibilities, including space colonization.
That post offers a forecast, not a description of a current product.
Michele Sciabarrà’s posts make three connected arguments: private AI has to be built into a company’s processes and systems, not layered on top; the product, not the executive title, is where their work matters, so leadership roles should protect that focus; and AI will change what software people do without removing the human role in deciding what gets built.
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The first argument comes with named client deployments, the second with a leadership handover inside Nuvolaris, and the third is a longer-range thesis that extends beyond the company’s current products.
Private AI belongs inside business processes, not above them
Sciabarrà argues that confidential, scattered company data — documents, email, databases, management systems, industrial systems — usually cannot be handled by public AI tools.
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What Sciabarrà calls private AI is a controlled system integrated with those systems and built around the real process.
The framing is that this is application building rather than "putting a chat over the data": privacy, permissions, processes and existing integrations all have to hold.
Sciabarrà adds that private AI is not a replacement for public tools — the image is an umbrella and a coat, solving different problems — and that no single public model does everything, since Sciabarrà describes ChatGPT as generalist, Claude Code as strong for coding and Gemini as strong for retrieval.
The decision Sciabarrà puts to a buyer is about control: Sciabarrà describes AI agents as employees that work for the buyer and not for Google, Microsoft or Amazon.
The examples Sciabarrà gives come from client work at Nuvolaris, the company Sciabarrà founded.
The specific deployments include a wiki generated from company documents and interrogated by chat with custom ingestion rules; a system that collects orders arriving informally by email and produces order forms ready for the management system; a daily reporting aggregator that reads different sources, including internal chat and restricted sites; predictive dashboards for industrial systems; natural-language interfaces to company databases without writing queries; and a CRM created, updated and customised by conversation.
The two posts work at different levels: one argues through client deployment examples, the other through positioning and analogy, arriving at the claim that a business should effectively "hire" its first private agent.
The product matters more than the title, and the company should be structured to protect it
Sciabarrà argues that being CEO, which they call the usual life ambition, crowds out the work they do best.
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They describe the cost concretely: meetings with clients and partners, day-to-day operational decisions, email, and reading legal and commercial documents.
Technical product decisions, they argue, require sustained study, testing, hands-on work and verification — and that becomes more important, not less, when AI helps write the code.
Their conclusion: choose a focus and design roles around it.
Sciabarrà handed the CEO role to Mirella Di Girolamo, who had moved from CFO to COO, so they could concentrate on product.
They present this as a choice for the company, not a loss of control, and explain the governance: shareholders appoint a board, the board chooses the CEO, and Sciabarrà remains principal shareholder with a board seat and enough voting power to decide who holds the CEO role.
Mirella Di Girolamo came to Sciabarrà for decisions Sciabarrà could not make because Sciabarrà lacked first-hand information; Sciabarrà asked why she was asking Sciabarrà, and she answered that Sciabarrà was the CEO.
The reply was to remove the "almost" from her being CEO in practice.
Elsewhere Sciabarrà describes four years as CEO, the startup "valley of death," several attempts that did not all work, and an unchanging mantra of building a product that made sense and that people actually wanted to use.
One post describes the move to CPO; another records the formal board resolution appointing Mirella Di Girolamo as CEO and Sciabarrà accepting the wider CTO role at her insistence, after they would have been content with CPO.
AI changes what software people do, not whether people are needed
Sciabarrà argues that software will increasingly be generated from a plain description of what someone wants, and that programmers will shift to helping people realise those wants — like an architect drawing a house, a doctor treating a patient, a chef cooking a refined meal or a lawyer advising on rights.
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They compare AI to earlier tools that beat humans on one narrow measure, such as cars being faster or calculators more precise, and to the smartphone, which absorbed the music player, camera, telephone and television into one device.
They also push back on posts arguing that AI is a passing thing and nothing fundamental is changing, asking whether their authors believe that or are denying that a new and unknown territory has been entered.
This long-range thesis extends beyond software to physical AI and building things that are currently unthinkable; it is not a description of Nuvolaris products.
Sciabarrà illustrates scale with a palace, a dam or a spaceship: something one person could not build alone today but might be able to build tomorrow, with AI carrying the knowledge and robots doing the work. They also note that an AI chatbot already performs functions that used to require dozens of separate sites, seeing this as a sign of where the trajectory leads.
This is the clearest starting point for understanding private AI in practice.
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It answers “what does private AI actually deliver beyond an internal chatbot?” with named business processes a newcomer can evaluate.
Sciabarrà lists private-AI applications built with clients: a chat-interrogable wiki generated from company documents; an order intake system that turns informal email orders into ERP-ready forms; a daily reporting aggregator that reads internal chat and restricted sites; predictive dashboards for industrial systems; natural-language interfaces to company databases; and a conversational CRM created, updated and customized through conversation. The post frames private AI as building applications that respect privacy, permissions, processes and existing integrations—not simply putting a chat layer over data.
For brands planning a campaign
For a B2B AI infrastructure firm, this is a ready-made discovery agenda: map scattered company data, identify a high-friction process and scope a private-AI pilot. It supports a focused advisory engagement on product strategy, not a broad AI talk.
This offers a concise explanation of the distinction between public and private AI, and why data sensitivity changes the architecture.
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A newcomer can start here to understand Sciabarrà’s core market argument.
Sciabarrà uses an umbrella-and-coat analogy: public AI is generalist, Claude Code is strong for coding, Gemini is strong for RAG, and no single tool does everything.
Private AI becomes necessary when an agent accesses email, CRM, client documents under NDA or industrial secrets.
They argue that private AI is specialized and controllable, and describe AI agents as employees who work for the buyer, not for Google, Microsoft or Amazon.
For brands planning a campaign
Useful for positioning a private-AI product against general-purpose AI: frame the buyer’s decision around data sensitivity, integration and control. An advisory engagement can sharpen that narrative and connect it to product capabilities.
It explains Sciabarrà’s current operating role and why product strategy, rather than general management, is their focus.
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It answers “where does this person sit now?” for readers evaluating product leadership.
After four years as CEO of Nuvolaris, Sciabarrà handed the role to Mirella Di Girolamo to become CPO.
The post describes Mirella’s move from CFO to COO, new co-founders Michele and Massimo, the startup “valley of death,” and Sciabarrà’s view that product is what they do best.
They present the transition as a deliberate choice for the company’s benefit, not a loss of control.
For brands planning a campaign
For a product strategy advisory brief, this post establishes firsthand product leadership and operating context. It can support an engagement focused on product direction, prioritization and founder-to-product-team handoffs.
It explains who controls Nuvolaris and why the role shift happened.
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Read it to understand the governance context behind the leadership transition.
Sciabarrà corrects a public “demotion” description: shareholders name a board, the board chooses the CEO, and the CEO names the rest of the company.
They remain principal shareholder, hold a board seat and have enough voting power to choose the CEO; they chose Mirella.
The post explains that CEO work left little energy for technical product decisions, so the move to CTO/CPO let them focus on product.
For brands planning a campaign
Shows governance literacy and an ability to explain internal role changes to an external audience. Relevant to advisory conversations about product organization, technical leadership and founder-role design.
It captures the beliefs behind Sciabarrà’s product decisions and public commentary.
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Read it to understand the long-term direction they argue for.
Sciabarrà argues that AI-generated software is already happening, compares AI to a smartphone replacing many separate devices, and says programmers will help people realize what they want, as architects, doctors, chefs and lawyers do.
The post predicts powerful tools will accelerate progress, physical AI and even space colonization.
It’s a broad future thesis rather than a product description.
For brands planning a campaign
For a B2B AI infrastructure company, this offers a thought-leadership angle: AI-native software, agent orchestration and human-in-the-loop product design. An advisory engagement could pressure-test where this thesis meets current product realities.
Sciabarrà argues that private AI is useful when it can work with confidential company data and existing systems, rather than simply adding a chat interface.
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They illustrate the point with a document-based company wiki, email-to-order processing, reports built from multiple sources, industrial dashboards, database queries in natural language, and a conversational CRM. Another post contrasts private AI with public tools using the image of a coat and an umbrella: different tools address different needs.
Uses analogies to explain the distinction, then gives specific application examples to show what it means in practice.
For brands planning a campaign
A company assessing private AI could ask Sciabarrà to help prioritize a concrete workflow, then examine its data access, permissions, integrations, and operational output. The examples support a product-strategy discussion grounded in business processes, not just model capabilities.
Sciabarrà presents AI as a force that could change how software is made and what programmers contribute.
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They compare AI's potential to earlier tools that surpassed human abilities, and argue that people will increasingly guide machines toward outcomes people value. They extend this argument from software generation to the possibility of AI taking physical form.
Builds a broad argument through familiar comparisons, then uses imagined future scenarios to push the implications further.
For brands planning a campaign
This material is relevant to a forward-looking product strategy conversation about how AI changes software products and human roles. A useful engagement would distinguish near-term product decisions from speculative long-range scenarios, rather than treating every prediction as an implementation plan.
In two posts about Nuvolaris, Sciabarrà explains why they handed the CEO role to Mirella Di Girolamo so they could focus on product work.
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They describe the practical cost of executive duties—meetings, customer and partner conversations, operations, and decisions—and argue that technical product decisions require sustained study, testing, and hands-on work. A later post says they accepted the CTO role as well as responsibility for product.
Explains a leadership decision through the work involved, the tradeoff it addressed, and the roles of collaborators.
For brands planning a campaign
For an advisory brief, this provides evidence of a product-first perspective and an account of how leadership structure can protect technical focus. It could inform a discussion about decision rights and product leadership.
Start with the private-AI posts, where they explain why confidential company data and existing systems need controlled AI applications rather than a chat layer, with client examples including a document wiki, email-to-order processing, predictive industrial dashboards, and a conversational CRM.
Next, read the Nuvolaris leadership posts to understand why they handed the CEO role to Mirella Di Girolamo while keeping product work and the CTO role.
Finally, read the longer-range post on AI, software, and work, which argues that AI changes what programmers do rather than removing the human role.
The tags point to topics, specialties, and format; similar creators cover adjacent work in CRM implementation, procurement advisory, and ecommerce strategy.
Both work close to CRM, data, and AI-enabled business tools, explaining implementations through field-level detail and operational outcomes.
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Gnoli's posts centre on CRM data reconciliation, Salesforce setup, and agency pipeline systems; Sciabarrà's on private AI infrastructure, AI agents, and product leadership.
Both write in Italian for business operators, using client cases and market data to explain why commercial decisions work or fail.
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Rizzoli covers ecommerce product launches, Black Friday and Cyber Monday performance, and growth strategy; Sciabarrà covers private AI, AI agents, and Nuvolaris product leadership.
For a B2B AI infrastructure company assessing Michele Sciabarrà for product strategy advisory, the posts support a LinkedIn reach test, not a verified audience match.
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The profile shows 46,018 LinkedIn followers observed on 2026-10-07.
Five posts written in Italian for a technology and product audience cover private AI versus public AI, AI agents as business tools, the reshaping of SaaS, and Nuvolaris company/product updates.
To assess audience fit, request recent post-level results for private-AI and AI-agent posts, plus any audience survey results covering job function, seniority, company size, and geography.
LinkedIn followers (account-level, not post-level)
46018
LinkedIn profile account record in the research the research · 2026-10-07
Sciabarrà's posts describe no sponsorship, affiliate, or paid-partnership format.
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They document first-person accounts of running Nuvolaris and building private AI products; commercial work is presented as company client engagements, not a creator offer with rates, deliverables, or availability.
They are a founder who now works as CTO on product, and the AI work they write about is sold and delivered through Nuvolaris to business clients.
For a B2B AI infrastructure company, the posts support a product or advisory conversation about private AI, AI agents, and enterprise data integration—not a sponsorship booking.
Ask directly about partnership terms; there is no published package, rate card, or media kit to evaluate.
A B2B AI infrastructure company wants to assess Sciabarrà for a product strategy advisory role focused on private AI and AI agents.
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Decision: strong fit for a scoped advisory engagement.
Their posts describe firsthand product and deployment work, not general AI commentary.
Sciabarrà outlines six private-AI patterns built with clients: a wiki generated from company documents and queried by chat, an order intake system that turns informal email into ERP-ready forms, a daily reporting aggregator that reads internal chat and restricted sites, predictive dashboards for industrial systems, natural-language database queries, and a conversational CRM.
They also make a clear case for public versus private AI: public tools are generalist; private AI matters when an agent touches email, CRM, NDA documents, or industrial secrets, making control and integration key architectural questions.
That maps directly to product strategy work on data access, permissions, integrations, and agent design.
Their current role supports the advisory case: after four years as Nuvolaris CEO, Sciabarrà moved to CPO and then CTO while remaining principal shareholder and board member, explicitly to focus on product decisions.
The audience information is limited to an account-level follower count, with no data on job function, seniority, company size, geography, or per-post performance.
The fit is for expertise and product judgment, not a sponsored reach campaign.
Define the engagement around advisory outputs, not audience delivery.
When to choose a specialist
A software brand wants a paid LinkedIn sponsorship package with fixed post deliverables, CPM or CPC pricing, disclosure terms, and English-language promotion aimed at small-business owners or a broad technology audience.
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Decision: poor fit.
Sciabarrà's posts show no sponsorship, affiliate, or paid-partnership format, rate card, media kit, deliverable schedule, disclosure policy, or partnership inquiry contact.
The content is first-person founder and CTO material in Italian about private AI, Nuvolaris, and the future of software—not consumer product content or a general small-business acquisition channel.
The available audience information is a follower count, not a measured audience profile, so a buyer cannot verify that the account reaches the intended small-business or English-speaking segment.
No post-level engagement metrics are available either.
That does not rule out a sponsorship, but the posts cannot support a reach-based booking or a standard paid-format recommendation.
If the brand still wants to work with Sciabarrà, the realistic path is a direct conversation about a technical advisory or product-positioning engagement, not a sponsorship package priced from a rate card.
A worked campaign example
For a three-week private-AI product advisory sprint, the goal is to help the B2B AI infrastructure company test one enterprise use case and turn Sciabarrà's public deployment patterns into a concrete product decision.
Week one: map the buyer's confidential data sources and select one high-friction process.
Candidates that mirror Sciabarrà's examples include informal email orders that need to become structured ERP records, scattered internal documents that need a governed wiki, or customer data spread across CRM and restricted sites.
Week two: run an architecture session on agent access, ingestion rules, permission boundaries, integration with existing systems, human review points, and evaluation criteria.
Use the distinction between public and private AI to force the key product question: where does a general model suffice, and where do confidential data, control, and integration require a private deployment?
Week three: deliver a written decision memo with a build/buy/partner recommendation, a pilot scope, success metrics, a risk register, and a short list of product requirements.
If the company turns this into content, a recruiting angle could be: "From scattered company data to one governed private-AI workflow"—a practical narrative supported by Sciabarrà's posts.
This is not a sponsorship: Sciabarrà's public work supplies discussion material, while the buyer supplies its own systems, data, and constraints.
Keep Michele Sciabarrà in view for decisions about what the private-AI product or agent should do; choose Canovese when the buyer's blocking question is commercial: where the margin is, which contract to renegotiate, and how to fund a pilot.
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Canovese's posts describe being named Senior Partner at ERA Group EMEA after passing project and revenue thresholds.
They frame the method as listening before proposing, asking why a process has always been done a certain way, and building cost programmes on trust rather than templated answers.
A second post reads a shipping-lane risk (Bab el-Mandeb, roughly 12% of seaborne oil and over 20% of container traffic) into purchasing decisions, contract timing, and energy sourcing, arguing that cautious companies are becoming more selective rather than stopping.
For a B2B AI infrastructure buyer weighing which process to fix and what it is worth, Canovese speaks to the value case; the product-side creator speaks to the build.
Canovese's posts focus on cost, procurement, and energy advisory delivered through long client relationships.
They offer negotiation and cost-structure judgment, not the product and technical judgment the fit section rests on.
If the buyer needs both, they are complements rather than substitutes: only Sciabarrà's posts cover the product side.
Choose Gnoli when the task is customer-data consolidation, CRM hygiene, or a working pipeline system rather than private-AI product architecture.
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One post walks through an automotive CRM rebuild step by step: extracting scattered data from Qlik, DMS and portals; building a customer 360 on standard Salesforce objects; reconciling 105,169 records into 98,146 unique customers on strict match rules; separating private and company accounts (2,371 automatic links); defining lead conversion as quote-only (240 conversions previously invisible); setting a 60-day deal cut-off with a 'sale beyond cut-off' tag; linking vehicles, workshop spend and customer lifetime value; and exposing 682 mapped fields in a configurable console.
Another post lays out an agency pipeline build—cold outreach, an offer aimed at accountants, scraped lists, an A/B tracker and a CRM—reporting about €15 in setup cost and 13 positive replies in a week.
If the buyer's urgent problem is dirty data feeding an AI pilot or a lack of pipeline to sell it, Gnoli addresses that step.
Gnoli's edge is hands-on platform and go-to-market execution: Salesforce data models, deduplication and reconciliation rules, lead logic, and outbound systems.
Their AI-agent example is narrow: agents that catalogue regulatory updates.
For enterprise private AI, confidential-data access, permissioning, or agent architecture, Sciabarrà offers product direction; Gnoli offers operational CRM and pipeline delivery.
The tasks overlap on making data usable, not on what to build.
Choose Rizzoli when the buyer's decision shifts from enterprise AI infrastructure to consumer ecommerce: how to position a new product line and plan the peak trading calendar.
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One post analyses a fashion brand's failed sportswear launch, separates three launch scenarios (line extension, new line, upsell and gifts), and argues that a catalogue works like a place: products too far from the existing identity reduce the audience instead of expanding it.
The other reads BFCM data—over $70 billion spent in 24 hours, +5% year on year, Shopify up 24%, and average discounts rising from 20–25% in 2023 to 25–30% in 2024—and recommends concentrating spend in stronger markets, layering offers across a longer window, and treating Cyber Monday as the bigger day.
For a brand campaign focused on assortment, launch risk, and promo timing, Rizzoli's posts are closer to the decision than Sciabarrà's.
Rizzoli's posts focus on retail and D2C commercial strategy: assortment logic, launch risk, discount depth, and marketplace timing. For a need in ecommerce merchandising, Rizzoli is a genuine alternative; for the stated AI-infrastructure advisory need, the two are not substitutes.
This page describes five full LinkedIn posts written by Michele Sciabarrà in Italian; the posts have no publication dates.
Michele Sciabarrà's follower count is 46,018 as of 7 October 2026.
A comparison with adjacent creators draws on six dated posts published between October 2025 and September 2026 by Ivan Canovese, Giorgio Gnoli and Davide Rizzoli.
The posts cover private AI deployments built with clients, AI agents, Nuvolaris leadership changes, and a longer-range argument about AI and software work.
Known gaps: the posts carry no publication dates; they include no per-post engagement figures, audience demographics, or follower composition; no sponsorship rate card, media kit, disclosure policy, or partnership contact appears in the posts here; and the posts provide no separate information about books, shows, or earlier career.
Those gaps limit any claim about reach or paid formats, so the page is written for readers who want to understand the work itself.
