Investment strengthens Aformic’s capital position and accelerates North American expansion and deployment of autonomous mobile robots.

MCLEAN, Va., Nov. 4, 2026 — Veteran Ventures Capital (VVC) today announced it led a $6.95 million investment in Aformic, a robotics company that designs and deploys autonomous mobile robots and fleet-management software.  Aformic tackles one of the greatest barriers to rebuilding America’s industrial base: a growing shortage of skilled workers.  By automating material flow inside factories and warehouses, Aformic frees more workers to create value rather than move parts on the factory floor.

This Seed raise is Aformic’s first institutional investment round.  VVC led the round through its Fund II, which included participation from ff Venture Capital, Red Beard Ventures, Mana Ventures, and other investors. Together, these transactions strengthen Aformic’s capitalization and position the company to accelerate its expansion across North American manufacturing and defense industrial markets.

Headquartered in Duncan, South Carolina, Aformic operates in the heart of the Southeast’s automotive and manufacturing corridor. The company builds customized autonomous mobile robots, including underride platforms and autonomous forklifts, to move materials and supply production lines in factories and distribution centers. The robots use lidar-based mapping to navigate with one centimeter accuracy, while Aformic’s proprietary Qursor fleet-management platform coordinates their movements to keep materials flowing seamlessly through factories and warehouses.  Aformic originated as a spinout from AIUT, a global industrial automation and robotics group with more than 30 years of engineering experience.

This investment follows VVC’s extensive study of the global reindustrialization wave.  Countries around the world are rebuilding their industrial base inside brownfield factories designed for an earlier era, counting on a labor force that simply doesn’t exist. Manufacturing productivity in the US has stalled for more than a decade, up to 1.9M manufacturing jobs may go unfilled through 2033, and the US Army alone is spending $18B to modernize and automate World War II-era depots.  Autonomous mobile robots are the fastest, cheapest way to make an existing plant more productive without rebuilding it.

With multiple robots in various Fortune 500 companies and Tier 1 defense OEMs, Aformic stands out in terms of market traction, capability and addressable market.  Additionally, with the Pentagon initiating a 15-year modernization plan across much of its industrial footprint, Aformic stands to make substantial gains.

“We believe Physical AI will transform American manufacturing, and Aformic has what it takes to lead that shift: proven robots, proprietary software, and deep industrial expertise. With VVC helping open doors across the defense industrial base, Aformic will be a category leader,” said Derren Burrell, Founder and Managing Partner at Veteran Ventures Capital, who now serves on Aformic’s Board of Directors.

Various studies show that between 15%-50% of factory labor hours goes to moving parts between steps rather than adding value to them.  Aformic’s Qursor software, coupled with its robots that can carry up to 2,000 lbs, makes a meaningful dent in skilled labor moving parts via forklift, carts or simply carrying them.  When production changes, Aformic makes the changes in software and robot integration with plant systems, rather than rebuilding a plant floor.

“America’s manufacturers need skilled workers building products, not moving materials. Our robots free them to do that,” said Michal Fiuk, CEO of Aformic, a West Point graduate and U.S. Army veteran. “This investment will accelerate our North American expansion, and Veteran Ventures brings the defense expertise and relationships to help us reach the manufacturers and depots that need us most.”

About Veteran Ventures Capital

Veteran Ventures Capital invests in dual-use national security technologies, focusing on companies led by veteran entrepreneurs and operators. Committed to advancing U.S. technological superiority, Veteran Ventures Capital provides capital, mentorship, and strategic guidance to high-growth companies serving critical government and commercial markets. VVC’s portfolio includes leading companies in defense, aerospace, advanced manufacturing, cybersecurity, and other sectors essential to national security, including notable investments in Vatn Systems, Hidden Level, Cambium, Agile Space Industries, Turion Space, Hybron Technologies, Radian Forge, and Asylon Robotics, amongst others. For more information, visit www.veteranventures.us and follow the firm on LinkedIn.

About Aformic

Aformic automates the internal logistics of the industries building America’s future. The company designs and deploys customized F-series autonomous mobile robots and the Qursor fleet-management platform for material transport and line feeding in manufacturing and warehouse operations, with headquarters in Duncan, South Carolina, and an engineering and production center in Gliwice, Poland. Led by Michal Fiuk, a West Point graduate and U.S. Army veteran, Aformic draws on more than 30 years of industrial automation engineering through its heritage in the AIUT Group. For more information, visit aformic.com.

Media Contact

For media inquiries, please contact: investorrelations@veteranventures.us

SOURCE Veteran Ventures Capital

• General Catalyst’s Customer Value Fund commits $500 million in growth capital to accelerate demand for Nooks’ revenue agents
• The Nooks Revenue Agent Platform lets sales teams work with AI agents across the revenue lifecycle, from finding new customers to closing deals and growing accounts
• Node, the first AI personal assistant for sales, learns your sales process and proactively works every deal in the background, helping every rep sell like your best rep

SAN FRANCISCO, Nov. 4, 2026 — Nooks, the Revenue Agent Platform, today announced $500 million from General Catalyst’s Customer Value Fund (CVF). CVF provides non-dilutive capital to fund growth for companies with proven, predictable unit economics. Nooks qualified on the strength of 3x year-over-year growth and 135% net revenue retention – achieved by using its own revenue agent platform. Nooks also launched Node, the first AI personal assistant for sales, which works every deal around the clock, uncovers the risks and opportunities reps can’t see, and does the work to act on them.

The funding news follows a breakout year for Nooks. Nooks now serves more than 2,000 companies, including Glean, Notion, Deel, Replit and Factory. More than half of customers use more than one Nooks product.

Companies spend hundreds of billions annually on their sales forces, yet the average rep spends just 40% of their time selling, according to Salesforce. Nooks agents handle the prospecting, outreach and deal follow-through, so reps can spend more of their day with buyers.

“There are 12 million people who sell for a living in the US alone and all their jobs are about to change. We’re solving some of the hardest technical problems that enable sales teams to work with AI agents across the revenue lifecycle,” said Dan Lee, co-founder and CEO of Nooks. “In order to completely transform an industry, you need a lot of capital. General Catalyst CVF will enable us to bring Nooks and Node to every seller – helping them serve more customers and accelerate growth for the world’s best companies.”

“AI changed how software gets built. Nooks is changing how it gets sold,” said Pranav Singhvi, partner and co-head of the Customer Value Fund at General Catalyst. “Nooks has shown a highly predictable revenue engine and strong unit economics, which is what we look for in companies with CVF. It’s especially impressive that they’ve achieved this by powering their GTM with their own platform.”

From pipeline to the full revenue stack

Nooks started with AI for outbound, then launched Sequencing earlier this year, which quickly took share from incumbents like Outreach and Salesloft. Together they gave Nooks something few AI companies have: the execution layer where revenue work actually happens and more than one billion real sales interactions flowing through it.

Today, Nooks is unifying that foundation into the Revenue Agent Platform, agents that do the work of revenue, from first touch to renewal. To teach agents to sell, Nooks Research, the company’s applied AI research team, builds the models and systems that help AI understand the way people think, act and buy:

• Live context that persists for months. Every Nooks agent carries the full, continuously updated history of an account: calls, emails, objections, champion changes and signals. It reconciles conflicting information instead of treating each task as a fresh prompt.
• A learning loop that compounds. Nooks agents learn from outcomes, not just prompts. They are trained on real sales interactions, rehearse plays against simulated prospects before reaching real buyers, backtest strategies against similar historical deals and learn each rep’s style.
• Infrastructure that sees outcomes. Nooks agents work toward goals like pipeline created and deals advanced, not one-off tasks. Because Nooks makes the call and sends the email on its own infrastructure, they see what happens next: the reply, the meeting, the closed deal. Tools that sit on top of the CRM can’t learn from outcomes they never observe.

The results show up in the work: Customers typically source 3x more pipeline with Nooks than with their previous tools; 96% of emails from Nooks agents go out without edits.

The platform includes Deal, Outbound, Expansion and Coaching Agents, with Inbound Agents arriving in Q1 2027. Each agent runs on Nooks’ built-in dialing and sending infrastructure, the complex execution layer that customers rely on to connect with their buyers.

Introducing Node, the first EA for the AE

Node is the first AI personal assistant for sales. Most AI tools wait for a rep to ask for help and then complete one task. Node is goal-oriented, always on, and self-learning. A rep might be focused on hitting the quarter, breaking into a strategic account, expanding a key customer or rebuilding pipeline for next quarter.

Node works toward whatever goals matter to that seller. It decides which deals and steps will move those goals forward and either does the work or brings it to the rep, ready to approve. Node is also built for the way sales really works, as a team sport. Everyone on a deal, from the rep to the account manager to the solutions engineer, has their own Node. Those Nodes work together and learn from each other, so winning playbooks spread across the whole team automatically.

Early users are seeing up to 80% higher sales productivity, with Node saving them two hours a day that they put back into serving customers. Node will be GA in early 2027, and teams can sign up for early access today at nooks.ai/node.

Scaling to meet demand from revenue teams

Nearly 9 in 10 sellers plan to use AI agents by 2027 (Salesforce). Nooks will use the capital to launch new products that solve the biggest challenges in sales, reach more companies across the enterprise and internationally, and expand into new roles across revenue teams. Nooks is also hiring across engineering, research, product, design and GTM, with over 100 open roles.

About Nooks

Nooks is the Revenue Agent Platform where human sellers and AI agents work together to drive pipeline and win more deals. Sellers and agents operate within a single platform, sharing account context and infrastructure across the entire revenue lifecycle, from first touch to renewal. Trained on over a billion real sales interactions, Nooks agents learn and improve outcomes with every deal. More than 2,000 companies run their revenue teams on Nooks. Founded in 2020 by AI researchers from Stanford, Cerebras, Scale AI and Tesla Autopilot, Nooks is backed by General Catalyst CVF, Kleiner Perkins, Lachy Groom and other investors. Learn more at nooks.ai.

SOURCE Nooks

Phaze turns expensive, underused workstations into computing power on demand for people and AI agents.

NEW YORK, Nov. 4, 2026 — Phaze today announced it raised $8.75 million in seed funding led by a16z, with participation from Indie, NextView, Makers, and Lerer Hippeau.

Phaze gives people and AI agents remote access to powerful computers. For employees doing demanding work in 3D, design and software development, Phaze provides a high-performance remote desktop that makes a remote workstation feel local. For AI agents, it provides full control of a remote computer, including its graphical interface and applications, without requiring computer-use software to be installed on that machine.

As AI agents become capable of handling more complex work, companies want employees to use them to get more done, but they also want that work performed on machines they control. Phaze lets agents operate company-managed computers under existing identity and access management policies, whether they’re testing software, troubleshooting IT problems or working in the background without taking over an employee’s machine.

Phaze also lets companies manage expensive workstations as shared infrastructure rather than assigning each machine to one employee. With high-end workstations costing upwards of $10,000 and often sitting idle, companies can make better use of their existing hardware as both employees and AI agents need computing power.

One Remote Desktop for People and AI Agents

Phaze was built for demanding workloads where latency, responsiveness and visual quality matter. Its founders previously built Parsec, the high-performance remote access company acquired in 2021. They started Phaze after recognizing that people and AI agents would increasingly need access to powerful machines, creating demand for enterprise tools that make it easier to share and manage computing resources.

With Phaze, an artist or designer can remotely access a high-end workstation to work with complex 3D graphics. A developer can connect to a powerful machine to build software. IT teams can manage those workstations centrally, control who can access them, and assign and reassign machines as demand changes.

Phaze is now extending that same remote access to AI agents.

The company recently released an MCP server that lets computer-use agents remotely connect to and operate computers. Rather than simply connecting an agent to an API, Phaze gives the agent access to the computer itself, allowing it to interact with software and perform tasks on the machine.

That creates several new ways for agents to work. An agent can operate a separate computer in the background while its user continues working on their own machine. Developers can give coding agents access to computers where they can test applications and features. IT teams can allow support agents to remotely access employees’ computers and attempt to resolve problems.

Phaze also lets people and AI agents collaborate within the same remote session, passing control back and forth as needed. A person can take over when an agent needs help, then hand control back so the agent can continue working.

In one early implementation, Phaze built an IT support agent that can remotely connect to an employee’s computer, diagnose and attempt to fix an issue, then hand control to an authorized IT employee when it needs help. The employee can take over the remote session and then hand control back to the agent to finish the task.

Turning Workstations Into Shared Infrastructure

Making computers remotely accessible to people and agents changes how companies can manage the hardware they already own.

Instead of permanently assigning a workstation to one employee, IT teams can manage machines as a shared resource, control who or what can access them, and assign and reassign computers as demand changes. The hardware can sit on a desk, in a server room or in a data center while users and agents access it remotely.

“The computer sitting on your desk shouldn’t determine what compute you can access,” said Benjy Boxer, CEO and co-founder of Phaze. “People need powerful workstations to do their jobs, and now AI agents need computers they can actually operate. We’re building the access layer that lets either one connect to the machine they need, wherever it happens to be.”

Key capabilities include:

• High-performance remote desktop: Remotely operate powerful computers with the responsiveness and visual quality required for demanding professional workloads.
• Shared workstations: Assign and reassign machines across users rather than permanently tying expensive hardware to individual employees.
• AI agent access: Give computer-use agents access to remote computers through Phaze’s MCP server.
• Background agent work: Let an AI agent perform work on a separate computer without taking over the machine its user is actively using.
• Human-agent collaboration: Allow authorized employees to take control from an AI agent when needed and hand the machine back when their work is complete.
• Centralized access controls: Enterprise authentication, directory sync, and audit logging let companies manage who can connect to which machines, enforce existing security policies, and ensure AI agents inherit the access permissions of their users.

New Funding Supports Product Development and Growth

Phaze will use the funding to expand its engineering team, accelerate product development and support customer growth.

“High-end compute is too expensive to sit idle on individual desks,” said Martin Casado, General Partner at a16z. “Phaze is turning those machines into shared infrastructure for people and AI agents. Benjy, Chris, and their team have been building in this space for more than a decade, and they bring that experience to a new era of remote access and machine management. We’re excited to support them again.”

About Phaze

Phaze provides high-performance remote access to computers for people and AI agents. Phaze is headquartered in Utah. For more information, visit phaze.app.

SOURCE Phaze

Company emerges from stealth with an agentic operating system that unifies development and construction and puts AI agents to work across every stage of project delivery

NEW YORK, Nov. 4, 2026 — Shackleton AI, a provider of agentic technology for the construction industry, today emerged from stealth, unveiling its platform that unifies development and construction management workflows and deploys AI agents to automate administrative project work. Launched in June 2026, the Shackleton platform currently manages over $1 billion in project value across more than 15 states for midsize general contractors in multifamily, industrial, and commercial construction, as well as multi-location brands including Red Lobster, Dog Haus and Pepper Lunch.

The company also announced it raised $5 million in seed funding led by Battery Ventures, which it will use to expand the workflows its AI agents can manage and accelerate go-to-market efforts. Brick & Mortar Ventures, Trust Ventures, Correlation Ventures, Motivate Partners, Thayer Investment Partners, Act One Ventures, Brainstorm Ventures, Silence VC and others also participated in the round.

“Construction is one of the largest and least digitized sectors in the economy, and the teams running these projects are buried in manual work that software should have automated a decade ago,” said Michael Brown, general partner at Battery Ventures. “The Shackleton team built this platform to run their own business before bringing it to market. We believe their agentic approach is how construction projects will be managed going forward, and we are proud to lead this round.”

Solving the Operational Drag Slowing Modern Construction

For many construction teams, critical project information is spread across separate systems for bidding, documents, scheduling and finances. It is not uncommon for a company to juggle seven to 10 systems and have its teams spend 10 to 15 hours a week on data entry and administrative work. Tariffs, higher interest rates and owners pushing for faster rollouts leave little room for that overhead, while legacy construction platforms were designed primarily as systems of record, leaving the follow-up to project teams.

Shackleton was built by founders who lived the problem. Michael Ragheb, Hassan Yasin and Olexandr Kaltsev grew Ernest, a tech-enabled commercial general contractor, to $35 million in revenue across the Southeastern United States, while facing numerous challenges with fragmented solutions.

“I was spending more time moving data between systems than running my jobs at Ernest,” said Michael Ragheb, co-founder and CEO of Shackleton AI. “Once we saw how much this was costing us, and how many other builders were dealing with the same problem, we knew we had to start from scratch and bring everything into one place. Every hour a project manager spends chasing status across disconnected tools is margin lost. When everything lives in one place, projects move faster, teams spend less time on administrative work and projects cost less. With Shackleton, multi-location customers have completed buildouts up to 35% ahead of schedule and as much as 18% under budget.”

One Platform for the Entire Construction Lifecycle

For construction firms and developers, the Shackleton platform brings workflows – from site selection and bidding through project turnover – into a single system. AI agents handle many of the administrative tasks that consume project managers’ time, including drafting RFIs, following up on overdue submittals, flagging budget variances before payment requests are submitted for approval and converting field notes into daily reports.

Unlike point solutions that automate work within a single application, Shackleton’s AI agents operate across the entire platform, enabling teams to coordinate activities across projects and portfolios, while maintaining human oversight through review and approval workflows.

“As we roll out our rebrand across all of our locations, speed and quality are of the utmost importance to our brand. Consolidating our communication and project tracking on Shackleton has kept us on track and on schedule,” said Andi Cramer, senior director of construction and rebuild at Red Lobster.

Shackleton is building toward a future in which construction projects can largely manage themselves, bringing the proactive, always-on assistance people are increasingly accustomed to from consumer AI tools into every stage of project delivery.

To request a demo, visit shackleton.ai/demo.

About Shackleton AI

Founded in 2026 and headquartered in New York, Shackleton AI is the agentic operating system for development and construction. The platform unifies development, preconstruction, project and field management, finance and communications in a single system spanning site selection through project turnover. AI agents operate across shared data to handle administrative tasks, coordinate projects and identify risk. Shackleton was founded by Michael Ragheb, Hassan Yasin and Olexandr Kaltsev, who previously scaled a tech-enabled general contractor to $35 million in annual revenue. Today, Shackleton supports owners and builders in more than 15 states, with more than $1 billion in project value managed on the platform. Learn more at shackleton.ai.

SOURCE Shackleton AI

19 años de investigación predictiva, respaldados por un historial de inversiones de nueve años en condiciones reales que arroja una rentabilidad del 235,19%

SAN FRANCISCO, 4 de noviembre de 2026 — Q-Ball Intelligence anuncia hoy su lanzamiento como una nueva iniciativa de inteligencia predictiva nativa de IA, que se fundamenta en casi dos décadas de investigación y aplicaciones prácticas que se remontan al año 2007.

La empresa está desarrollando una nueva generación de inteligencia artificial capaz de identificar señales emergentes, reconocer patrones y anticipar acontecimientos futuros, yendo más allá del análisis de lo que ha ocurrido para comprender lo que es probable que suceda a continuación.

La investigación que sustenta Q-Ball ya se ha aplicado en la práctica. Entre el 17 de febrero de 2016 y el 17 de febrero de 2025, una cartera de inversión basada en Q-Ball generó una rentabilidad acumulada del 235,19%.

A modo de comparación, el análisis de la cartera muestra una rentabilidad del 129,86% para el índice OMX Stockholm All-Share Gross (OMXSGI) —que representa el mercado bursátil sueco en su conjunto— y del 106,55% para el índice OMX Stockholm 30 (OMXS30) durante el mismo periodo. Esto supone un rendimiento superior de 105,33 y 128,64 puntos porcentuales, respectivamente.

El historial de inversiones de nueve años ofrece un ejemplo tangible de la aplicación práctica de la investigación predictiva desarrollada bajo el liderazgo del profesor adjunto Peter Majanen. Se trata de un registro de una cartera de inversiones real, y no de una simulación basada en datos históricos.

Desde el análisis de la información hasta la anticipación del cambio

La mayoría de los sistemas analíticos están diseñados para procesar información, identificar tendencias históricas y explicar acontecimientos que ya han tenido lugar.

Q-Ball persigue un objetivo distinto: reconocer las señales y las expectativas cambiantes que pueden revelar lo que vendrá a continuación.

Su metodología predictiva conecta información, expectativas, patrones emergentes y dinámicas con posibles catalizadores y resultados futuros. El objetivo es transformar información compleja en pronósticos estructurados y medibles.

La investigación subyacente tuvo su origen en 2007 y ha evolucionado a lo largo de casi dos décadas de desarrollo y aplicación práctica. Q-Ball Intelligence representa la siguiente etapa: transformar esta base en una plataforma tecnológica escalable, diseñada para los mercados internacionales.

Construcción de una nueva plataforma de Inteligencia Predictiva

Q-Ball está desarrollando una plataforma nativa de IA diseñada para identificar señales relevantes, evaluar acontecimientos emergentes, generar pronósticos estructurados y contrastar las predicciones con los resultados reales.

El enfoque de la empresa pone especial énfasis en comprender los cambios en las expectativas y el comportamiento, en lugar de basarse exclusivamente en datos históricos y en el análisis de tendencias convencional.

Al combinar una metodología predictiva con un análisis basado en inteligencia artificial, Q-Ball aspira a crear tecnología que ayude a organizaciones y particulares a anticiparse a los cambios, evaluar oportunidades y tomar decisiones mejor fundamentadas.

Un aspecto importante de la filosofía de desarrollo es la rendición de cuentas: las predicciones deben estar estructuradas, fechadas y evaluadas en función de lo que realmente sucede. Esto sienta las bases para la medición, el perfeccionamiento y el aprendizaje continuos.

Aplicaciones en diversos mercados

Q-Ball Intelligence está desarrollando su tecnología para clientes institucionales y comerciales, con aplicaciones adicionales en los mercados financieros, el deporte y el sector del juego.

Las áreas de enfoque iniciales incluyen:

Inteligencia predictiva empresarial: ayuda a las organizaciones a anticipar la evolución del mercado, las condiciones cambiantes, los riesgos emergentes y las oportunidades estratégicas.

Mercados financieros: desarrollo de inteligencia predictiva para acciones y mercados financieros, identificando patrones emergentes y evaluando la probabilidad de posibles acontecimientos en el mercado.

Deportes y juegos: aplicación de modelos predictivos a resultados deportivos, entornos competitivos y mercados relacionados con los juegos.

La empresa está desarrollando estas aplicaciones sobre una base predictiva compartida, extendiendo sus capacidades fundamentales de inteligencia a diversos mercados y segmentos de clientes.

Un nuevo capítulo para la inteligencia predictiva

«La predicción no consiste en conocer el futuro. Consiste en comprender el presente lo suficientemente bien como para reconocer en qué está empezando a convertirse el futuro», afirmó Peter Majanen, fundador y arquitecto jefe de inteligencia predictiva.

Q-Ball Intelligence tiene como objetivo establecer la Inteligencia Predictiva como una categoría diferenciada dentro del panorama de la IA en constante evolución, yendo más allá de los sistemas que se limitan a describir el mundo para avanzar hacia una tecnología diseñada a anticipar cómo este podría cambiar.

Con una base de investigación que se remonta a 2007, la empresa entra ahora en una nueva etapa centrada en el desarrollo tecnológico, la comercialización y la expansión internacional.

Q-Ball Intelligence: prediciendo lo que vendrá a continuación.

Acerca de Q-Ball Intelligence

Q-Ball Intelligence es una iniciativa de inteligencia predictiva nativa de IA que desarrolla tecnología diseñada para identificar señales emergentes, reconocer patrones y anticipar acontecimientos futuros.

Sus fundamentos combinan investigación predictiva, una metodología analítica propia y aplicaciones prácticas desarrolladas desde 2007. La empresa se centra en transformar esta base en una plataforma de inteligencia predictiva escalable, destinada a aplicaciones institucionales, comerciales y de consumo.

Sitio web: www.qballintel.com

Contacto de prensa:
Richard Sjoberg
Q-Ball Intelligence
press@qballintel.com
www.qballintel.com

Divulgación sobre el rendimiento histórico: La rentabilidad de la cartera, del 235,19%, corresponde al periodo comprendido entre el 17 de febrero de 2016 y el 17 de febrero de 2025. Las cifras de referencia se basan en datos de Avanza para los índices OMX Stockholm All-Share Gross Index (OMXSGI) y OMX Stockholm 30 Index (OMXS30) durante el mismo periodo. El rendimiento histórico no constituye un indicador de resultados futuros. El rendimiento histórico de la cartera no debe interpretarse como un resultado generado por la nueva plataforma Q-Ball Intelligence, ni como una garantía de los resultados de inversión futuros.

Strategic capital investment led by Capital One Ventures will expand Sentra’s platform for protecting data at rest, where it lives, and controlling what AI agents can reach.

NEW YORK and TEL AVIV, Israel, Nov. 4, 2026 — Sentra, the agentic data security company, today announced that Capital One Ventures has led a strategic capital investment of $150 million in the company, joining existing investors Bessemer Venture Partners, Zeev Ventures, Standard Investments, Munich Re Ventures and Key1 Capital. As part of the investment, a Capital One representative will join Sentra’s board of directors. The investment follows a year of more than 300% year-over-year growth, as enterprises turn to Sentra to understand their data, control which identities, human and AI, can access it, and keep it governed and protected for AI and autonomous agents.

Your data has a new kind of consumer

Data security was built for people. Three forces have changed that at the same time:

• The business wants AI now. Copilots, assistants and cloud AI services are rolling out faster than governance can keep up, leaving security teams balancing business speed with data visibility and governance.
• The data explosion. Sensitive data is spread across cloud, SaaS and on-prem, outpacing legacy visibility tools and fragmenting visibility into where sensitive records live or who and what can reach it. Without continuous governance, sensitive information can quickly become over permissioned and overexposed.
• AI agents are scaling exponentially. Each agent is a new identity, accessing data continuously and at machine speed, with no central control plane. Large organizations that ran a handful of agents a year ago are on track to run exponentially more within a few years, yet only a small minority say they have the governance in place to control what those agents can access.

“AI agents are now the busiest consumers of enterprise data, and many of them inherit access to data that hasn’t been fully classified or governed,” said Yoav Regev, co-founder and CEO of Sentra. “Traditional perimeter controls weren’t built for agents moving at machine speed. What organizations can do is make sure the data those agents reach is already secured. Sentra protects data where it lives and controls which identities, human and AI, can reach it, so organizations can deploy AI more quickly while staying in control. This investment lets us bring agentic data security to every enterprise putting agents to work.”

Agentic data security starts with data at rest

Rather than trying to inspect every agent request as it happens, Sentra protects data at rest, where it lives, across cloud, SaaS, data warehouses, on-prem file shares and the knowledge stores AI draws from. By governing the data and access agents depend on ahead of time, Sentra maintains continuous protection beyond traditional perimeters.

• Before: Sentra minimizes the exposure footprint. It finds and classifies sensitive data across environments, and maps what people, service accounts and AI agents can reach, including inherited access. It enforces least privilege for non-human identities and removes redundant, obsolete and high-risk data agents should never find.
• During: Sentra helps contain an agent fast. Data-aware detection flags unusual access to sensitive data, and automated remediation revokes access and removes risky sharing, routing high-risk issues to data owners and existing workflows.
• After: Sentra shows what an agent could have reached, with business context for the datasets involved, giving security, legal and compliance teams the evidence to respond quickly and defensibly.

Where Sentra is investing next

The funding accelerates Sentra’s innovation in agentic data security: broad visibility into agents, copilots and models and the sensitive data they touch, continuous least privilege for human and non-human identities, protection of data at rest before agents reach it, and rapid containment when an agent behaves unexpectedly.

Sentra will also expand its go-to-market by scaling its field and channel programs, growing its brand and marketing, and entering new geographies.

Why enterprises choose Sentra

AI agents now act on enterprise data at machine speed, so security has to understand both the data and who, or what, can access it. Sentra uniquely unifies the two, especially in large, complex environments where other tools fall short. It finds sensitive data and classifies it in ways that fit each business. It also maps every identity, human and non-human, including AI agents, along with the sensitive data each one can access. This lets enterprises govern who has access to what, and fix risky access before agents or attackers exploit it.

Sentra does this without moving the data. It analyzes data where it lives, entirely inside the customer’s environment, so sensitive data never leaves it. Alternatives that copy customer data into their own environment create a separate data estate to secure, adding retention, residency and compliance risks for regulated industries. Leaving data in place also lets Sentra scale efficiently across clouds, SaaS, data warehouses and on-prem systems, with broader coverage at a cost advantage. Customers consistently rate Sentra among the highest in data security posture management on independent peer review platforms.

Chaim Sanders, CISO at Lyft, adopted Sentra as the data security solution to support the organization’s growth. According to Sanders: “Our teams at Lyft want to move fast with AI, but we need to know what data our agents can reach before we turn them on. Sentra gives us that answer across our whole environment, so security helps the rollout instead of slowing it down”.

About Sentra

Sentra is the agentic data security company. Its platform unifies data security posture management (DSPM), data detection and response (DDR), data access governance (DAG) and data loss prevention (DLP) to protect data at rest, where it lives, and gives enterprises continuous visibility and control over which identities, human and AI, can reach their data, so they can adopt AI quickly and stay governed and compliant. Founded in 2021 by Yoav Regev, Asaf Kochan, Ron Reiter and Yair Cohen, Sentra is headquartered in New York and Tel Aviv. Learn more at sentra.io.

Advisors

H-F & Co., Law Offices served as legal counsel to Sentra, and Wachtell, Lipton, Rosen & Katz served as legal counsel to Capital One Ventures.

SOURCE Sentra

Proceeds will fuel BRIJ Medical’s continued commercial expansion of BRIJ-SEAL™ and the upcoming launch of BRIJ-Ag™, building on strong investor confidence and growing market momentum.

SAN DIEGO, Nov. 3, 2026 — BRIJ Medical, a medical technology company advancing surgical incision management, today announced that it has closed a $5M financing round. The round was led by the company’s Board of Directors, with most of the participation from existing investors.

The company initially set out to raise $3M and increased the round to $5M in response to investor demand.

BRIJ’s product portfolio is now being used by surgeons across orthopedic, plastic and spine surgery, spanning a broad range of surgical procedures. Following the July 2026 launch of BRIJ-SEAL™, the company’s advanced wound closure device, BRIJ is building commercial momentum and preparing to introduce BRIJ-Ag™, its silver-based antimicrobial wound dressing, later this quarter.

“We set out to raise $3 million and chose to upsize the round because our investors saw the same opportunity we do,” said Tim Gleeson, CEO of BRIJ Medical. “This investment allows us to put BRIJ-SEAL and Brijjit® in front of more surgeons while accelerating the launch of BRIJ-Ag. We’re seeing growing interest across multiple surgical specialties, and we’re focused on building the commercial reach and infrastructure to meet that demand.”

John Grotting, Chairman of the Board, added: “We increased our commitment because we believe BRIJ is at an important inflection point. The company has developed a differentiated portfolio addressing meaningful needs in surgical care, and we’re pleased to support the team as it enters its next phase of growth.”

Funding to Accelerate Commercial Growth

Proceeds from the round will primarily support BRIJ Medical’s commercial expansion and upcoming product launch initiatives. With an expanding product portfolio, growing surgeon engagement and increased investment in its commercial organization, BRIJ Medical is positioned to build on its momentum and advance its mission to redefine surgical closure and incision management.

About BRIJ Medical

BRIJ Medical is redefining incision management through active tension offloading and advanced incision protection. With a portfolio of innovative solutions that cover, protect, and reinforce incisions, BRIJ empowers surgeons from closure through recovery.

BRIJ-SEAL is an advanced wound closure device that provides a 14-day bacterial barrier, while Brijjit® is an active tension offloading device clinically shown to eliminate up to 100% of incisional tension.

Trusted by plastic and orthopedic surgeons across the U.S. and Canada, BRIJ Medical brings greater control and confidence to incision management. 

Learn more at www.BRIJmedical.com.

*Clinical data available at Brij Medical’s website: Clinical Evidence – BRIJ Medical

SOURCE BRIJ Medical

19 Jahre Prognoseforschung, untermauert durch eine neunjährige Praxiserfahrung mit einer Rendite von 235,19 % bei Investitionen.

SAN FRANCISCO, 3. November 2026 — Q-Ball Intelligence gibt heute seine Gründung als neues KI-natives Unternehmen im Bereich Predictive Intelligence bekannt und baut dabei auf fast zwei Jahrzehnten Forschung und praktischer Anwendungen auf, die bis ins Jahr 2007 zurückreichen.

Das Unternehmen entwickelt eine neue Generation künstlicher Intelligenz, die in der Lage ist, aufkommende Signale zu identifizieren, Muster zu erkennen und zukünftige Entwicklungen vorherzusehen, indem sie über die Analyse des Vergangenen hinausgeht und versteht, was als Nächstes wahrscheinlich geschehen wird.

Die Forschung hinter Q-Ball wurde bereits in der Praxis angewendet. Zwischen dem 17. Februar 2016 und dem 17. Februar 2025 erzielte ein mit Q-Ball-Methoden informiertes Anlageportfolio eine kumulierte Rendite von 235,19 %.

Zum Vergleich zeigt die Portfolioanalyse Renditen von 129,86% für den OMX Stockholm All-Share Gross Index (OMXSGI), der den breiteren schwedischen Aktienmarkt repräsentiert, und 106,55% für den OMX Stockholm 30 Index (OMXS30) im gleichen Zeitraum. Dies entspricht einer Überperformance von 105,33 bzw. 128,64 Prozentpunkten.

Die neunjährige Investitionsbilanz liefert ein konkretes Beispiel für die praktische Anwendung der unter der Leitung von Assistant Professor Peter Majanen entwickelten prädiktiven Forschung. Es handelt sich um eine tatsächliche Portfoliohistorie und nicht um einen simulierten Backtest.

Von der Informationsanalyse bis zur Antizipation von Veränderungen

Die meisten Analysesysteme sind darauf ausgelegt, Informationen zu verarbeiten, historische Trends zu identifizieren und bereits stattgefundene Entwicklungen zu erklären.

Q-Ball verfolgt ein anderes Ziel: Die Signale und sich ändernden Erwartungen zu erkennen, die Aufschluss darüber geben könnten, was als Nächstes kommt.

Die Vorhersagemethodik verknüpft Informationen, Erwartungen, sich abzeichnende Muster und Dynamiken mit potenziellen Katalysatoren und zukünftigen Ergebnissen. Ziel ist es, komplexe Informationen in strukturierte, messbare Prognosen zu übersetzen.

Die zugrundeliegende Forschung entstand im Jahr 2007 und wurde in fast zwei Jahrzehnten der Entwicklung und praktischen Anwendung weiterentwickelt. Q-Ball Intelligence stellt die nächste Stufe dar: Diese Grundlage in eine skalierbare Technologieplattform für internationale Märkte umzuwandeln.

Aufbau einer neuen Plattform für prädiktive Intelligenz

Q-Ball entwickelt eine KI-basierte Plattform, die relevante Signale identifizieren, neue Entwicklungen bewerten, strukturierte Prognosen erstellen und Vorhersagen mit tatsächlichen Ergebnissen vergleichen soll.

Der Ansatz des Unternehmens legt besonderen Wert darauf, Veränderungen in Erwartungen und Verhalten zu verstehen, anstatt sich ausschließlich auf historische Daten und herkömmliche Trendanalysen zu stützen.

Durch die Kombination von prädiktiver Methodik mit KI-gestützter Analyse zielt Q-Ball darauf ab, eine Technologie zu schaffen, die Organisationen und Einzelpersonen dabei hilft, Veränderungen vorherzusehen, Chancen zu bewerten und fundiertere Entscheidungen zu treffen.

Ein wichtiger Bestandteil der Entwicklungsphilosophie ist die Verantwortlichkeit: Vorhersagen sollten strukturiert, mit einem Zeitstempel versehen und anhand der tatsächlichen Ereignisse überprüft werden. Dies schafft die Grundlage für kontinuierliche Messung, Verbesserung und Lernen.

Anwendungen in verschiedenen Märkten

Q-Ball Intelligence entwickelt seine Technologie für institutionelle und kommerzielle Kunden mit zusätzlichen Anwendungsmöglichkeiten in den Bereichen Finanzmärkte, Sport und Glücksspiel.

Zu den ersten Schwerpunkten gehören:

Enterprise Predictive Intelligence unterstützt Organisationen dabei, Marktentwicklungen, sich ändernde Bedingungen, aufkommende Risiken und strategische Chancen vorherzusehen.

Finanzmärkte Entwicklung von Prognoseanalysen für Aktien- und Finanzmärkte, Identifizierung neuer Muster und Bewertung der Wahrscheinlichkeit potenzieller Marktentwicklungen.

Sport und Gaming Anwendung von Vorhersagemodellen auf Sportergebnisse, Wettbewerbsumgebungen und spielebezogene Märkte.

Das Unternehmen baut diese Anwendungen auf einer gemeinsamen prädiktiven Grundlage auf und erweitert so seine Kernkompetenzen im Bereich der künstlichen Intelligenz auf verschiedene Märkte und Kundensegmente.

Ein neues Kapitel für prädiktive Intelligenz

„Bei Vorhersagen geht es nicht darum, die Zukunft zu kennen. Es geht darum, die Gegenwart so gut zu verstehen, dass man erkennt, was die Zukunft zu werden beginnt”, sagt Peter Majanen, Gründer und Chief Predictive Intelligence Architect.

Q-Ball Intelligence hat sich zum Ziel gesetzt, Predictive Intelligence als eigenständige Kategorie innerhalb der sich entwickelnden KI-Landschaft zu etablieren und über Systeme hinauszugehen, die die Welt primär beschreiben, hin zu Technologien, die darauf ausgelegt sind, vorherzusehen, wie sie sich verändern könnte.

Mit Forschungsgrundlagen, die bis ins Jahr 2007 zurückreichen, tritt das Unternehmen nun in eine neue Phase ein, die sich auf Technologieentwicklung, Kommerzialisierung und internationale Expansion konzentriert.

Q-Ball Intelligence – Vorhersage dessen, was als Nächstes kommt.

Über Q-Ball Intelligence

Q-Ball Intelligence ist ein KI-basiertes Unternehmen im Bereich der prädiktiven Intelligenz, das Technologien entwickelt, um aufkommende Signale zu identifizieren, Muster zu erkennen und zukünftige Entwicklungen vorherzusehen.

Seine Grundlagen vereinen vorausschauende Forschung, firmeneigene Analysemethoden und seit 2007 entwickelte praktische Anwendungen. Das Unternehmen konzentriert sich darauf, diese Grundlage in eine skalierbare Plattform für prädiktive Intelligenz für institutionelle, kommerzielle und Verbraucheranwendungen umzuwandeln.

Website: www.qballintel.com

Pressekontakt:
Richard Sjoberg
Q-Ball Intelligence
press@qballintel.com
www.qballintel.com

Offenlegung der historischen Wertentwicklung: Die Portfoliorendite von 235,19 % bezieht sich auf den Zeitraum vom 17. Februar 2016 bis zum 17. Februar 2025. Die Vergleichszahlen basieren auf Avanza-Daten für den OMX Stockholm All-Share Gross Index (OMXSGI) und den OMX Stockholm 30 Index (OMXS30) für den gleichen Zeitraum. Die bisherige Wertentwicklung lässt keine Rückschlüsse auf zukünftige Ergebnisse zu. Die historische Portfolio-Performance sollte nicht als Performance der neuen Q-Ball Intelligence-Plattform oder als Garantie für zukünftige Anlageergebnisse interpretiert werden.

19 ans de recherche prédictive, appuyés par neuf années d’investissements concrets affichant un rendement de 235,19 %

SAN FRANCISCO, 3 novembre 2026 — Q-Ball Intelligence annonce aujourd’hui son lancement sous la forme d’une nouvelle entreprise d’intelligence prédictive conçue nativement pour l’IA, soutenue par près de deux décennies de recherche et d’applications pratiques.

L’entreprise développe une nouvelle génération d’intelligence artificielle capable de détecter les signaux émergents, de repérer les tendances et d’anticiper les évolutions futures, en allant au-delà de l’analyse du passé pour comprendre ce qui est susceptible de se produire à l’avenir.

Les recherches à l’origine de Q-Ball ont déjà été mises en pratique. Entre le 17 février 2016 et le 17 février 2025, un portefeuille d’investissement basé sur les données de Q-Ball a généré un rendement cumulé de 235,19 %.

À titre de comparaison, l’analyse du portefeuille montre des rendements de 129,86 % pour l’indice OMX Stockholm All-Share Gross (OMXSGI), qui représente l’ensemble du marché d’actions suédois, et de 106,55 % pour l’indice OMX Stockholm 30 (OMXS30) sur la même période. Cela équivaut à une surperformance respective de 105,33 et 128,64 points de pourcentage.

L’historique des investissements sur neuf ans fournit un exemple concret de l’application pratique de la méthode de recherche prédictive mise au point sous la direction du professeur adjoint Peter Majanen. Il s’agit d’un historique réel du portefeuille d’investissement plutôt que d’une simulation rétrospective.

De l’analyse des informations à l’anticipation du changement

La plupart des systèmes analytiques sont conçus pour traiter les informations, repérer les tendances historiques et expliquer les événements passés.

Q-Ball poursuit un objectif différent : reconnaître les signaux et les attentes changeantes qui peuvent révéler les évolutions futures.

Sa méthode prédictive relie les informations, les attentes, les tendances émergentes et la dynamique aux catalyseurs potentiels et aux résultats futurs. L’objectif est de traduire des informations complexes en prévisions structurées et mesurables.

Entamées en 2007, les recherches sous-jacentes ont évolué sur près de deux décennies de développement et d’application pratique. Q-Ball Intelligence représente l’étape suivante : la transformation de cette base en une plateforme technologique évolutive destinée aux marchés internationaux.

La création d’une nouvelle plateforme d’intelligence prédictive

Q-Ball construit une plateforme intégrant nativement l’IA, pensée pour repérer les signaux pertinents, évaluer les évolutions émergentes, générer des prévisions structurées et les confronter aux résultats réels.

Plutôt que de se fier exclusivement aux données historiques et à une analyse conventionnelle des tendances, l’approche de l’entreprise met l’accent sur la compréhension de l’évolution des attentes et des comportements.

En associant méthode prédictive et analyse pilotée par l’IA, Q-Ball entend créer une technologie qui aide les entreprises et les particuliers à anticiper les changements, à évaluer les possibilités ouvertes et à prendre des décisions plus éclairées.

La responsabilité constitue un élément important de la philosophie de développement : les prévisions doivent être structurées, horodatées et évaluées par rapport aux événements réels. Cela crée ainsi une base pour la mesure, le perfectionnement et l’apprentissage continus.

Des applications sur plusieurs marchés

Mise au point pour les clients institutionnels et commerciaux, la technologie de Q-Ball Intelligence comporte des applications supplémentaires pour les marchés financiers, le sport et les jeux.

Les premiers domaines d’intérêt sont les suivants :

Intelligence prédictive d’entreprise : aider les entreprises à anticiper les évolutions du marché, les changements de situation, les risques émergents et les possibilités stratégiques.

Marchés financiers : développer une intelligence prédictive pour les marchés d’actions et les marchés financiers, repérer les tendances émergentes et évaluer la probabilité des évolutions potentielles du marché.

Sports et jeux : appliquer des modèles prédictifs aux résultats sportifs, aux environnements compétitifs et aux marchés liés aux jeux.

L’entreprise construit ces applications sur une base prédictive commune, étendant ainsi ses capacités fondamentales d’analyse à différents marchés et segments de clientèle.

Un nouveau chapitre pour l’intelligence prédictive

« La prédiction ne consiste pas à connaître l’avenir. Il s’agit de comprendre assez bien le présent pour pouvoir y reconnaître les prémices de l’avenir », explique Peter Majanen, fondateur de Q-Ball Intelligence et architecte en chef de l’intelligence prédictive.

Q-Ball Intelligence vise à établir l’intelligence prédictive comme une catégorie distincte au sein du paysage en constante évolution de l’IA, en dépassant les systèmes qui décrivent principalement le monde pour aller vers une technologie capable d’en anticiper les changements.

S’appuyant sur des bases de recherche qui remontent à 2007, l’entreprise entre désormais dans une nouvelle phase axée sur le développement technologique, la commercialisation et l’expansion internationale.

La prédiction de l’évolution à venir avec Q-Ball Intelligence.

À propos de Q-Ball Intelligence

Q-Ball Intelligence est une entreprise d’intelligence prédictive intégrant nativement l’IA, qui met au point une technologie pensée pour détecter les signaux émergents, repérer les tendances et anticiper les évolutions futures.

Elle se fonde sur une combinaison de recherche prédictive, de méthode analytique exclusive et d’applications pratiques développées depuis 2007. L’entreprise s’attache à transformer cette base en une plateforme d’intelligence prédictive évolutive pour les applications institutionnelles, commerciales et destinées au grand public.

Site internet : www.qballintel.com

Relations avec la presse :
Richard Sjoberg
Q-Ball Intelligence
press@qballintel.com
www.qballintel.com

Information sur les performances historiques : Le rendement du portefeuille de 235,19 % se rapporte à la période du 17 février 2016 au 17 février 2025. Les chiffres de référence sont basés sur les données d’Avanza relatives à la même période pour l’indice OMX Stockholm All-Share Gross (OMXSGI) et l’indice OMX Stockholm 30 (OMXS30). Les performances passées ne préjugent pas des résultats futurs. Les performances historiques du portefeuille ne doivent pas être interprétées comme des performances générées par la nouvelle plateforme Q-Ball Intelligence ni comme une garantie de résultats d’investissement futurs.