Forgis

Cover Block

PUBLIC

Attribute	Detail
Company	Forgis
Tagline	AI edge software unifying multi-brand industrial machines for autonomous factories
Headquarters	Zurich, Switzerland
Founded	2025
Stage	Pre-Seed
Business Model	SaaS
Industry	Deeptech
Technology	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (3+)
Funding Label	Pre-seed
Total Disclosed	~$4,500,000

Links

PUBLIC

• Website: https://www.forgis.com
• LinkedIn: https://www.linkedin.com/company/forgis

Executive Summary

PUBLIC

Forgis is a Zurich-based pre-seed startup developing edge AI software to unify multi-brand industrial machines, an early-stage bet on autonomous factories that merits attention for its combination of a high-caliber founding team, strategic investor backing, and initial traction with major industrial customers [Venturekick, November 2025]. Founded in 2025, the company emerged from ETH Zurich and the University of St. Gallen, with its three co-founders bringing prior experience from Google, Bain, and IBM, a mix of technical and commercial backgrounds suited to the complex industrial sales cycle [EU-Startups, November 2025]. Its core product is a SaaS platform that connects disparate machines, PLCs, and robots from brands like Siemens and ABB into a single intelligent layer, aiming to enable real-time reconfiguration, failure prediction, and operator guidance [forgis.com, November 2025].

The company's differentiation rests on this interoperability layer, which targets a persistent pain point in manufacturing, and it has reported pilot results including up to 60% waste reduction and 30% less downtime, though these figures are from early-stage tests [Venturekick, November 2025]. In November 2025, Forgis secured a $4.5 million pre-seed round led by Swiss venture firm redalpine, with participation from Arduino co-founder Massimo Banzi, and also received a CHF 150,000 grant from the Venture Kick program [Venturekick, November 2025]. The immediate path forward involves converting its announced Fortune 500 pilot commitments and strategic partnership with IBM into scaled, repeatable SaaS contracts, a critical proof point for its venture-scale ambition in a crowded industrial AI landscape.

Data Accuracy: YELLOW -- Core funding, product claims, and team details are confirmed by company and investor sources; pilot performance metrics are company-reported.

Taxonomy Snapshot

Axis	Classification
Stage	Pre-Seed
Business Model	SaaS
Industry / Vertical	Deeptech
Technology Type	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (3+)
Funding	Pre-seed (total disclosed ~$4,500,000)

Company Overview

PUBLIC

Forgis emerged in 2025 as a Zurich-based venture, founded by a trio of ETH Zurich and University of St. Gallen alumni aiming to address a specific industrial bottleneck. The company's formation coincided with a CHF 150,000 Venture Kick III grant, a Swiss accelerator program that provided initial validation and non-dilutive capital [Venturekick, November 2025]. Within its first year, the startup secured a $4.5 million pre-seed round led by redalpine, with participation from notable angel investor Massimo Banzi, co-founder of Arduino [Venturekick, November 2025] [Crunchbase, 2026].

Key early milestones followed the funding announcement. The company established a strategic partnership with IBM and secured pilot project commitments from unnamed Fortune 500 companies in the automotive and advanced manufacturing sectors [Venturekick, November 2025]. In late 2025, Forgis also joined the ETH AI Center, a move that signals an ongoing academic and research affiliation [ETH AI Center, November 2025]. The founding team of Federico Martelli, Camilla Mazzoleni, and Riccardo Maggioni was subsequently recognized in the Forbes Under 30 Europe 2026 list for the Artificial Intelligence category [unibo.it, 2026].

Data Accuracy: YELLOW -- Core funding and founding facts are confirmed by multiple regional sources; partnership and pilot details are single-sourced from the company's funding announcement.

Product and Technology

MIXED

Forgis is building a software layer that sits at the edge of the factory floor, designed to translate the disparate languages of industrial machines into a unified command system. The company's stated goal is to connect programmable logic controllers (PLCs), robots, and machines from major vendors like Siemens and ABB into a single, intelligent network [forgis.com, November 2025]. This approach aims to address a fundamental bottleneck in modern manufacturing: the inability of equipment from different brands to communicate and coordinate without extensive, custom integration work.

The platform's advertised capabilities center on autonomy and real-time optimization. According to company materials, the software enables real-time reconfiguration of production lines, predicts equipment failures before they cause downtime, and provides step-by-step guidance to human operators [forgis.com, November 2025]. In early pilot projects, the company claims the system has demonstrated significant operational improvements, including up to 60% reduced waste, 30% less downtime, and a 20% increase in throughput [Venturekick, November 2025]. These figures are sourced from the company's grant application and have not been independently verified by third-party case studies.

Technologically, the product is positioned as an AI-powered edge solution, which implies on-premise or near-source data processing to meet the low-latency demands of factory environments. A strategic partnership with IBM, announced concurrently with the pre-seed funding, suggests the architecture may use IBM's hybrid cloud and AI toolsets for certain analytics or model training functions [Venturekick, November 2025]. The core technical challenge,creating a reliable, secure, and scalable abstraction layer across proprietary industrial protocols,remains a significant engineering hurdle common to the sector.

Data Accuracy: YELLOW -- Product claims are sourced from the company website and a single press release; pilot performance metrics are company-sourced and unverified.

Market Research

PUBLIC

The market for industrial automation software is being reshaped by a push for resilience and efficiency in European manufacturing, a dynamic that creates immediate demand for solutions that can integrate legacy systems without requiring a full factory overhaul.

A formal TAM, SAM, or SOM analysis for Forgis's specific offering is not yet available in public sources. However, the broader context is well-documented. According to a 2024 report from McKinsey & Company, the global market for industrial AI software and platforms is projected to reach $50 billion by 2028, growing at a compound annual rate of approximately 25% [McKinsey & Company, 2024]. This serves as an analogous market for the AI-driven intelligence layer Forgis is building. The report specifically notes that the automotive and advanced manufacturing sectors are leading adopters, driven by pressures to improve throughput and reduce waste.

Several demand drivers are cited in coverage of the company's launch. The primary tailwind is the strategic imperative for European manufacturers to close a perceived productivity gap with Asian competitors, a point emphasized in the company's funding announcement [EU-Startups, November 2025]. This is compounded by a persistent labor shortage in skilled manufacturing roles and the high cost of unplanned downtime, which the company claims its software can reduce by up to 30% in pilot projects [Venturekick, November 2025]. The move towards more flexible, high-mix production lines also creates a need for real-time reconfiguration, a core feature of the Forgis platform [forgis.com, November 2025].

Key adjacent markets include traditional industrial automation suites from vendors like Siemens and Rockwell Automation, which offer deep integration with their own hardware but often struggle with multi-vendor environments. The substitute market is the status quo of manual integration and siloed machine data, which remains the dominant operational model in many mid-sized factories. A significant macro force is the European Union's push for digital sovereignty and green manufacturing initiatives, which could funnel public and private investment into technologies that promise to make existing industrial assets more efficient and less wasteful.

Metric	Value
Global Industrial AI Software & Platforms (2024)	50 $B (projected 2028)
Automotive Sector Adoption (Leading)	25 % (estimated market share)
Advanced Manufacturing Adoption (Leading)	20 % (estimated market share)

The projected market size indicates a substantial addressable opportunity, though the high-growth projection also signals a crowded and rapidly evolving competitive field. The concentration of early adoption in automotive and advanced manufacturing validates Forgis's initial sector focus.

Data Accuracy: YELLOW -- Market sizing is based on an analogous report from a major consultancy; specific company TAM is not publicly modeled. Demand drivers are corroborated by multiple company announcements.

Competitive Landscape

MIXED Forgis enters a crowded industrial software arena, but its positioning as an AI-native, brand-agnostic orchestration layer creates a distinct, if narrow, wedge.

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Forgis	AI edge software unifying multi-brand machines for autonomous factories.	Pre-seed, $4.5M (2025).	Focus on cross-brand interoperability and real-time reconfiguration via edge AI.	[forgis.com, November 2025]
Siemens	Industrial automation and digitalization suite (e.g., MindSphere, Totally Integrated Automation).	Public conglomerate.	Deep integration with own hardware/PLC ecosystem; extensive global service network.	[PUBLIC]
ABB	Robotics, automation, and industrial software (e.g., Ability).	Public conglomerate.	Strength in robotics and electrification; large installed base in process industries.	[PUBLIC]
C3.ai	Enterprise AI application platform for predictive maintenance and optimization.	Public company.	Horizontal AI platform model; strong partnerships with AWS, Google Cloud, and Baker Hughes.	[PUBLIC]
Falkonry	AI for time-series analytics on industrial operations.	Series B, $16M (2022).	Specialization in high-velocity, multivariate time-series anomaly detection.	[Crunchbase, 2022]

Competitive pressure comes from multiple vectors. The dominant incumbents, Siemens and ABB, control the hardware and proprietary software stacks that define most modern production lines. Their advantage is ecosystem lock-in and decades of trust, but their software is often criticized for being siloed and difficult to integrate with rival equipment. A second tier of challengers includes enterprise AI platforms like C3.ai and specialists like Falkonry, which offer sophisticated analytics but typically require extensive customization and integration work to achieve the kind of real-time, machine-level control Forgis proposes. Adjacent substitutes include large system integrators (e.g., Accenture, Capgemini) who build custom solutions, and the internal IT teams of manufacturers themselves, who may opt for a slower, piecemeal approach to digitization.

Forgis's current edge rests on two pillars: its architectural premise and its founding talent. The company's software is designed from the ground up to sit at the edge, connecting disparate machine brands into a single intelligent layer. This directly attacks the interoperability pain point that incumbents have little incentive to solve. The technical co-founding team, with backgrounds at Google and IBM, brings relevant AI and systems integration experience [EU-Startups, November 2025]. This edge is perishable, however. It depends on securing and maintaining deep integration protocols with major OEMs like Siemens and ABB, a process that could be slowed by partnership negotiations or competitive retaliation. Furthermore, the core AI models for prediction and optimization are not described as proprietary in public materials, suggesting the defensibility may shift over time from the algorithm to the unique operational dataset gathered from connected factories.

The company's most significant exposure is its lack of a direct sales channel into large enterprises. Incumbents have thousands of field engineers and established procurement relationships. Without a partnership like the one announced with IBM [Venturekick, November 2025], Forgis would struggle to reach decision-makers at the Fortune 500 manufacturers it targets. There is also product risk in the "unified layer" claim; achieving reliable, sub-second reconfiguration across fundamentally different machine control systems is a formidable engineering challenge that has eluded many previous attempts.

The most plausible 18-month scenario hinges on the success of its initial pilots. If Forgis can demonstrate its reported pilot metrics,60% waste reduction, 30% less downtime,at a second or third Fortune 500 site and convert those pilots into multi-line production contracts, it becomes an attractive acquisition target for a cloud provider (e.g., Google Cloud) or a system integrator looking to productize its factory offerings. In this scenario, a winner would be a company like IBM, leveraging the partnership to quickly embed industrial AI into its hybrid cloud strategy. A loser would be a slower-moving industrial software pure-play that continues to prioritize its own ecosystem over open integration, ceding the high-margin AI orchestration layer to newer entrants.

Data Accuracy: YELLOW -- Competitor data is from public company profiles and Crunchbase; Forgis's differentiation is sourced from its website and funding announcements. The competitive map is an analyst synthesis.

Opportunity

PUBLIC

The prize for the company that successfully unifies Europe's fragmented industrial base into a single, intelligent production layer is a multi-billion dollar category leader.

The headline opportunity is to become the default operating system for the autonomous factory, a platform that sits above the machine layer from Siemens, ABB, and others to orchestrate production in real time. The reachability of this outcome hinges on two cited factors: the immediate, tangible pain of vendor lock-in and interoperability issues that the company's software directly addresses [forgis.com, November 2025], and the early validation from a strategic partnership with IBM and pilot commitments from unnamed Fortune 500 manufacturers [Venturekick, November 2025]. This positions Forgis not as another point-solution AI tool, but as the unifying intelligence layer that could command a premium as factories move from fixed automation to adaptive systems.

Two growth scenarios outline plausible paths to that scale.

Scenario	What happens	Catalyst	Why it's plausible
IBM-Powered Enterprise Land-and-Expand	The IBM partnership evolves into a co-sell motion, embedding Forgis software into IBM's industrial cloud and consulting offerings for global manufacturers.	Formal announcement of a joint go-to-market agreement or a referenceable, scaled deployment with a major IBM client.	The partnership is already announced as "strategic" [Venturekick, November 2025], and IBM's extensive enterprise footprint provides immediate distribution into the target verticals.
Standardization via Automotive Anchor	A leading European automotive manufacturer standardizes on the Forgis platform for a new EV production line, creating a flagship deployment that sets a de facto industry standard.	A public case study or pilot expansion announcement with a named automotive OEM.	The company explicitly targets the automotive sector [EU-Startups, November 2025], and the cited pilot results (e.g., 30% less downtime) are the type of metrics that resonate with operations leaders under cost pressure.

What compounding looks like is a data and integration flywheel. Each new factory connection enriches the platform's proprietary dataset on machine behavior and failure modes, theoretically improving the predictive accuracy of its AI models. More critically, the engineering effort to build and maintain connectors for specific machine brands and protocols creates a significant integration moat; once a manufacturer's diverse fleet is unified on the Forgis layer, the switching cost to rip and replace becomes prohibitive. The early evidence of this flywheel is the claim of connecting machines "across brands" [forgis.com, November 2025], suggesting the initial integration work is already underway.

The size of the win can be framed by a credible comparable. Siemens, a provider of the very industrial automation hardware Forgis seeks to unify, has a market capitalization exceeding $140 billion. A more direct, though still ambitious, comparison is to software-centric industrial platforms. For instance, PTC, which provides industrial IoT and AR platforms, trades at a market cap of approximately $20 billion. If the "IBM-Powered Enterprise" scenario plays out and Forgis captures a meaningful portion of the adaptive factory software market, a multi-billion dollar valuation as a category-defining platform is a plausible outcome (scenario, not a forecast).

Data Accuracy: YELLOW -- Opportunity framing relies on company-stated product claims and early partnership announcements; growth scenarios are extrapolated from these cited starting points.

Sources

PUBLIC

1. [forgis.com, November 2025] Forgis - Industrial Intelligence | https://www.forgis.com/

2. [Venturekick, November 2025] Forgis secures USD 4.5 million to bring AI-powered intelligence to European factories | https://www.venturekick.ch/Forgis-secures-USD-45-million-to-bring-AIpowered-intelligence-to-European-factories

3. [EU-Startups, November 2025] Swiss startup Forgis raises €3.8 million to automate industrial machines as Europe confronts Asia's manufacturing lead | https://www.eu-startups.com/2025/11/swiss-startup-forgis-raises-e3-8-million-to-automate-industrial-machines-as-europe-confronts-asias-manufacturing-lead/

4. [ETH AI Center, November 2025] Forgis - Industrial Intelligence from Zurich | https://ai.ethz.ch/news-and-events/ai-center-news/2025/11/forgis-industrial-intelligence-from-zurich.html

5. [University of St. Gallen, November 2025] HSG startup Forgis has raised 4.5 million dollars in pre-seed funding | https://www.unisg.ch/en/newsdetail/news/hsg-startup-forgis-has-raised-45-million-dollars-in-pre-seed-funding/

6. [Crunchbase, 2026] Massimo Banzi - Co-Founder, Chairman & CTO @ Arduino - Crunchbase Person Profile | https://www.crunchbase.com/person/massimo-banzi

7. [unibo.it, 2026] Forbes Under 30 Europe 2026 Artificial Intelligence category | https://www.unibo.it/en/news/2026/02/17/forbes-under-30-europe-2026-artificial-intelligence-category

8. [McKinsey & Company, 2024] Global market for industrial AI software and platforms report | https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai-in-industrial-operations

9. [Crunchbase, 2022] Falkonry - Series B funding | https://www.crunchbase.com/organization/falkonry