The company, which was founded in 2016, is pitching itself as a privacy-focused alternative to both legacy badge systems and more controversial forms of facial recognition. According to Alcatraz, its flagship product, the Rock, uses facial authentication rather than surveillance-style identification, allowing employees to enter buildings without badges or passcodes while avoiding the storage of photographs or other personal data in the cloud. The company said the platform was designed to meet compliance requirements including GDPR, CCPA and BIPA. 

A Security Pitch Built for the A.I. Era

Alcatraz said demand has risen sharply as the AI boom turns data centers into some of the world’s most sensitive physical infrastructure. In its announcement, the company said its customer base already includes major AI data centers, U.S. airports, energy companies, NFL teams, universities and Fortune 100 companies. It also reported more than 300% year-over-year growth in data center adoption in 2025, along with 200% growth in new enterprise customers and a fivefold expansion across Fortune 500 deployments. 

Chief Executive Tina D’Agostin said the company sees itself as bringing smartphone-style identity verification into the workplace. “We are the Face ID of securing physical spaces,” she said in the announcement, arguing that badges and passcodes now create too much risk for modern workplaces. Founder Vince Gaydarzhiev, who Alcatraz said worked on hardware prototyping for iPhone and iPad during the development of Face ID at Apple, said he wanted to bring a privacy-centered approach to the buildings where people work. 

The timing of the funding reflects a larger shift in the market: as companies pour billions into AI infrastructure, the business of protecting the physical spaces behind that technology is becoming more strategically important. Alcatraz said it plans to use the new capital to expand into new industries, enter international markets and grow its team, betting that the next phase of AI growth will require not just more computing power, but tighter control over who can access it. 

The pitch to investors is straightforward: today’s defence systems are often too expensive, too rigid, and too slow to adapt to a battlefield increasingly shaped by low-cost, mass-produced drones. EGIDE is betting that militaries and infrastructure operators need something different — a system that is scalable, cost-efficient, and flexible enough to work across air, ground, and maritime missions.

A product built for the new economics of warfare

At the center of EGIDE’s offering are two core products: an electrically propelled interceptor and Mystique, a hardware-agnostic defence platform. Together, they are designed to help detect, track, and stop evolving threats without relying on the kind of high-cost interceptor model that has dominated traditional air defence.

That usability point matters. Instead of building a product tied to a single platform or mission, EGIDE says Mystique is meant to work across different systems by combining distributed sensors, AI-driven detection, and layered interception capabilities. In practical terms, that means faster integration, broader deployment options, and more adaptability as threats change.

The company argues that this architecture can reduce both the cost and the complexity of legacy defence tools. That is one of its clearest competitive advantages. In an environment where attackers can launch large numbers of inexpensive drones, the side relying on costly interception systems can quickly face a losing economic equation.

Why this matters now

The urgency behind EGIDE’s product is rooted in recent conflicts. The company explicitly points to the wars in Ukraine and Iran as evidence that cheap drones can overwhelm older defence systems built to counter a smaller number of more expensive threats.

As co-founder Simon Calonne put it: “Low-cost drones are fundamentally transforming modern warfare. Systems designed to intercept a small number of high-value threats are now being confronted with large volumes of inexpensive and highly adaptable aerial systems.”

That shift is exactly why investors are paying attention. Defence buyers in Europe and across NATO are under pressure to strengthen their arsenals, but they also need systems that can be deployed at scale without becoming financially unsustainable. EGIDE’s value proposition is that it is building “a new generation of scalable and affordable defence capabilities” for that reality.

What investors are really funding

Investors are not just backing a startup with a prototype. They are backing a broader thesis: that Europe needs a new defence stack built for modern conflict, and that software-defined, lower-cost interception systems could become a critical layer of that stack.

Expeditions co-founder and general partner Dr. Mikołaj Firlej framed it in strategic terms, saying: “Europe is entering a decisive moment in the rebuilding of its security architecture.” He added that the spread of cheap drones is exposing “critical vulnerabilities and unsustainable economics associated with legacy defence systems.”

That line gets to the heart of the investment case. EGIDE is attractive because it sits at the intersection of several high-priority trends: European defence sovereignty, drone warfare, AI-enabled systems, and affordability at scale. For investors, that combination creates a potentially large market if the company can deliver systems that are both effective and economically viable.

Heartcore Capital partner Jimmy Fussing Nielsen made the same case more directly: “Cheap attack drones have significantly changed the economics of warfare, overwhelming legacy defence systems and pushing Europe to find a new answer.”

Competitive advantages: cost, flexibility, and integration

EGIDE’s competitive edge appears to rest on three main pillars.

First is cost efficiency. The company is focused on mass-affordable interceptors, a phrase that directly addresses a central problem in modern defence procurement: you cannot sustainably stop cheap threats with extremely expensive countermeasures.

Second is cross-domain usability. Eurazeo highlighted that EGIDE’s systems are designed to work across air, sea, and ground missions, making them more versatile than tools built for one narrow operational setting.

Third is software and integration. Mystique is described as hardware-agnostic, which is important because defence customers rarely operate in clean, standardized environments. A platform that can connect with different sensors, systems, and mission profiles has a much better chance of fitting real-world procurement and deployment needs.

This is also where EGIDE could stand out from more traditional defence manufacturers: not just by building munitions, but by building a more adaptable operating layer around them.

Why the founders matter

EGIDE was founded in 2025 by former MBDA engineers Simon Calonne and Florian Audigier, who bring relevant technical expertise from one of Europe’s biggest missile makers. Calonne specializes in Guidance, Navigation and Control, while Audigier has experience in warhead design.

That background helps explain why investors were willing to write checks at the seed stage. In defence tech, teams matter enormously because technical complexity, regulatory barriers, and procurement cycles are all high. Backers are often looking for founders who understand not only engineering, but how military systems are actually designed and fielded.

What the funding will be used for

EGIDE says the new capital will go toward accelerating the design and production of its electrically propelled interceptors, advancing the Mystique platform, and expanding its engineering team across Europe. The hiring focus will include expertise in electric propulsion, aerodynamics, warhead design, and software engineering.

Calonne said the company’s ambition is to build “a European leader in mass-affordable interceptors capable of protecting forces and critical infrastructure against evolving aerial, sea and ground threats.”

That ambition helps explain why the round matters beyond one startup. Investors are betting that next-generation defence will not be defined only by bigger budgets, but by better economics, faster iteration, and systems that can adapt as quickly as the threats they are designed to stop.

Image: EGIDE

SonarSource unveiled three new open beta products — Sonar Context Augmentation, SonarQube Agentic Analysis and SonarQube Remediation Agent — designed to help teams guide, verify and fix AI-generated code throughout the development cycle. The company’s message is clear: as coding agents produce more software at much higher volume, the next battleground will not be generation alone, but whether organizations can trust, control and maintain what those agents create. 

Why This Matters for Users

For users, the benefit is practical rather than theoretical. AI coding tools can already generate large amounts of code quickly, but Sonar argues that speed often comes with more issues, more complexity and more technical debt. Its new tools are meant to reduce that burden by giving agents better context before they write code, checking their work while they are generating it, and fixing issues automatically before developers have to spend time cleaning them up by hand. 

The Competitive Difference: Sonar Is Selling a Control Layer

That is what separates SonarSource from many competitors chasing the AI coding boom. Plenty of vendors focus on helping agents generate code faster. Sonar is focused on what happens after that moment — whether the output aligns with architecture, passes quality and security checks, and can be repaired systematically without dragging down engineering teams. In other words, Sonar is not trying to be the coding agent itself. It wants to be the trust and verification layer around agent-driven development. 

The AC/DC Framework Behind the Launch

Sonar is packaging the launch around what it calls the Agent Centric Development Cycle, or AC/DC, a four-stage framework for AI-generated software: Guide, Generate, Verify and Solve. The idea is that AI agents should not operate as black boxes. They should first receive project-specific rules and architectural constraints, then generate code in a sandboxed flow, then have that code verified through deterministic analysis, and finally feed identified issues into a repair loop. That cycle, Sonar argues, is what turns AI coding from a novelty into an enterprise-ready process. 

Context Before Code

The first new product, Sonar Context Augmentation, is aimed at one of the most common weaknesses in AI coding: agents often lack awareness of the standards, structures and boundaries of the codebase they are working in. Sonar says the product injects relevant, real-time project context from SonarQube directly into the agent workflow, so the model understands what rules apply before it writes code. For customers, the value is not just cleaner output. Sonar says early benchmarks showed better build pass rates, better test pass rates, less code duplication, lower cognitive complexity and fewer tool calls and tokens, which could also mean lower operating costs. 

Catching Problems Earlier

The second product, SonarQube Agentic Analysis, moves code analysis into the agent’s generation loop instead of waiting for a failed pull request or human review. That could be meaningful for users because it shifts error detection upstream. If the code introduces a security risk, logic flaw or maintainability issue, the agent can see it and correct it in real time. The promise is that developers spend less time acting as cleanup crews for AI mistakes and more time on architecture and higher-value work. 

Fixing Technical Debt at Scale

The third product, SonarQube Remediation Agent, takes aim at both new issues and old backlog problems. For fresh pull requests, it can generate fixes as soon as SonarQube flags an issue. For older codebases, Sonar says it can work systematically through accumulated vulnerabilities, reliability issues and maintainability problems by opening one pull request per issue. That gives developers reviewed, ready-to-merge fixes without forcing automatic changes into production. The important distinction is that Sonar says every generated fix is re-scanned by its analysis engine before it reaches the developer, which strengthens its position as a verification-first platform rather than a blind automation tool. 

A Timely Message as AI Code Quality Comes Under Scrutiny

Sonar is also leaning on research to support its case. In the post, the company cites peer-reviewed Carnegie Mellon research covering 807 open-source projects that had adopted Cursor. Sonar says the study found a temporary productivity boost from agent usage, but by the third month that boost had faded, while code analysis warnings rose 30 percent and code complexity climbed 41 percent. For technology buyers, that is the core tension Sonar is trying to monetize: AI may increase output, but without stronger quality controls it can also increase long-term drag on development. 

Why Enterprises May Find This More Useful Than Another Coding Copilot

That framing could resonate especially with larger organizations that are already experimenting with Cursor, Claude Code, Codex, Gemini and GitHub Copilot but are concerned about compliance, maintainability and architectural drift. Sonar’s advantage is that it already has a long-standing position in code analysis and quality gates. Rather than asking customers to adopt yet another standalone AI coding product, it is extending that existing authority into the agentic era. For customers already using SonarQube, the transition may feel less like buying a brand-new category and more like upgrading an existing control point to meet AI-era demands. 

Image: Sonar 

Coder has raised a $90 million Series C led by one of its largest customers, KKR, with participation from another customer, QRT, in a signal that some enterprise buyers are increasingly willing to back the infrastructure vendors they see as critical to their AI strategy (Coder blog, April 1, 2026). The company is using that moment to make a broader point: as AI coding agents spread inside large organizations, the winners may not be the loudest developer apps, but the platforms that help enterprises govern, secure and operationalize them.

Why the User Benefit Is Really About Control

For users, the pitch is less about novelty than control. Coder says large enterprises need persistent and reproducible development environments, curated tools and repositories, audit trails, token tracking, prompt observability, isolation from internet and production systems, and strict access boundaries for autonomous agents. That is the kind of infrastructure that matters when companies want to use tools such as Claude Code, Cursor or other coding agents without exposing themselves to compliance, security or operational risks.

What Makes Coder Different From Competitors

What sets Coder apart from many competitors is that it is not selling an AI assistant alone. It is positioning itself as the governed workspace layer underneath AI development, especially for enterprises that want self-hosted deployments, infrastructure flexibility and tighter compliance controls. In a market crowded with direct-to-developer AI tools, Coder is arguing that enterprise customers care more about what breaks when agents run freely than about which tool looks hottest this quarter.

Early Customer Signals Are Strong

That argument appears to be resonating with customers already using the product at scale. Coder says KKR’s engineering organization uses the platform across more than 500 engineers and is looking to extend coding agents to thousands of employees, including analysts, developers and operators. The company also said bookings are up 300 percent from a year earlier and that it posted 184 percent trailing 12-month net dollar retention, suggesting customers are not just adopting the platform but expanding their use over time.

Centralized Guardrails Could Matter More Than New Features

The user benefit here is straightforward: instead of asking every developer, analyst or employee to configure and manage their own agentic coding environment, Coder offers a centralized and governed setup that is easier to scale across teams. That matters even more as the definition of “developer” expands beyond software engineers to include non-technical users, citizen developers and human-agent workflows. In that world, enterprise-grade guardrails are not a nice-to-have. They are the product.

Coder’s CEO Is Making a Long-Term Infrastructure Bet

Coder’s chief executive, Rob Whiteley, frames the trend as a market signal many investors are still underestimating. He writes that “the interesting signal in enterprise AI right now isn’t coming from IDEs or vibe coding tools,” but from engineering organizations trying to understand how to maintain compliance and control as they deploy AI more broadly. He adds that “infrastructure doesn’t 10x in a year” and instead “compounds over decades,” underscoring Coder’s attempt to separate itself from faster-moving but potentially less durable AI application plays.

Why Regulated Industries May Pay Attention

The company also leans heavily into a message likely to resonate with regulated industries. Whiteley writes that “data sovereignty, control, and repatriation are the new norm,” while describing how QRT, operating under strict financial-services requirements, needed to move fast on AI without sacrificing guardrails. That gives Coder a differentiated position against cloud-first or lightweight agent platforms that may be easier to start with, but harder to justify inside security-sensitive or air-gapped enterprise environments.

“The Safe Mode for AI”

One of the sharpest lines in the post comes from KKR’s VP of AI, Cloud and Data, who described the company as “the safe mode for AI.” It is a strong encapsulation of Coder’s competitive angle: not that AI coding agents should be blocked, but that they need a secure, observable and policy-controlled environment to become usable at enterprise scale. For technology buyers, that may be the more compelling promise than raw code generation alone.

Image: Coder, YouTube video (screenshot)

Coro is pushing security operations closer to where users already work, launching new Model Context Protocol, or MCP, capabilities that allow customers to access, analyze and act on security data directly from tools like ChatGPT, Claude and other AI environments (Source: Coro, Business Wire announcement). The move is aimed squarely at small and midsize businesses and lean IT teams that often lack the time, staff and budget to manage sprawling security tools, and it reflects a broader shift in enterprise software toward conversational interfaces that can turn questions into actions without forcing users through another dashboard.

For customers, the clearest benefit is speed. Instead of logging into a dedicated security platform, hunting through menus and stitching together findings manually, teams can query live security data, investigate events, generate reports, visualize trends and execute actions from within the AI tools they already use. That could dramatically reduce friction for IT administrators who are increasingly relying on AI assistants as part of their daily workflow and want security operations to live in the same environment.

What makes Coro’s pitch different from many security competitors is not just that it uses AI, but where it puts it. Many cybersecurity platforms still treat AI as an add-on inside their own interface. Coro is extending its platform outward, using MCP to make its security layer interoperable with external AI tools rather than requiring users to stay inside Coro’s native environment. For resource-constrained organizations, that matters: the product becomes less about learning a new security system and more about bringing security context into tools employees already understand.

Coro says its AI-driven platform is built across three layers. The first is AI-driven insights that automatically analyze security events, identify threats and prioritize actions across users, devices and environments. The second is an AI copilot that lets users interact with the security environment in natural language, producing summaries, answering questions and guiding response steps. The third, and newest, layer is MCP integration, which pushes those capabilities into outside tools so customers can work with Coro data without logging into Coro itself.

The company is positioning that structure as a practical answer to a longstanding industry problem: cybersecurity tools have often been built for large enterprises with specialized teams, leaving smaller organizations to cope with complexity they are not staffed to handle. Coro’s argument is that conversational access, plain-language guidance and workflow interoperability can shrink that burden while still giving users meaningful control over response and reporting.

“Cybersecurity has forced teams to adapt to complex tools and workflows for years,”

said Joe Sykora, CEO of Coro.

“With MCP, Coro is flipping that model, meeting users where they already are and bringing security into the tools they already use every day, making it possible to go from question to action instantly.”

That message is likely to resonate with managed service providers and channel partners as well, another audience Coro explicitly called out. These partners often manage multiple customer environments and have strong incentives to reduce swivel-chair work, accelerate analysis and standardize actions across familiar interfaces. By pairing its unified security data with whichever AI platform a user prefers, Coro is also offering a more flexible model than platforms that lock customers into a single assistant or a closed workflow.

The company says MCP can cut work that once took hours or days, such as investigating security incidents or preparing executive reports, down to seconds or minutes. It also says the integration can support higher-level outputs like visualizations and executive-ready reporting built from large volumes of security data. That emphasis on both actionability and presentation suggests Coro is not only trying to help analysts respond faster, but also helping IT leaders communicate risk more clearly to the rest of the business.

For technology buyers, the bigger takeaway is that Coro is betting the next competitive battleground in cybersecurity will not be just detection quality, but usability. As AI assistants become part of everyday enterprise workflows, security vendors may increasingly be judged by how easily they can plug into those environments. Coro’s MCP launch is an early attempt to claim that ground, especially among organizations that want enterprise-grade protection without enterprise-grade complexity.

Image: Coro page screenshot

Information from startup ecosystem updates suggests the studio is focusing on interactive gaming experiences that blend competitive mechanics with strong community engagement features.

The global mobile gaming market continues to expand as developers explore new formats that combine social elements, live services, and scalable mobile platforms. Emerging studios such as Cheer Games are experimenting with innovative gameplay models designed to increase player engagement and retention.

The newly raised funding will support early game development and team growth as the company prepares to launch its first titles.

Image: Cheer 

Reports circulating in startup funding databases suggest the company is developing advanced lithium-ion battery cell manufacturing systems aimed at improving scalability and efficiency in production.

Demand for battery technologies continues to rise as industries invest heavily in electric mobility, renewable energy, and large-scale energy storage systems. Technologies that improve manufacturing speed and reliability are increasingly important for strengthening global battery supply chains.

The funding is expected to help the company scale its production capabilities and accelerate collaborations with industrial manufacturers.

Image: UniverCell

Startup funding trackers and ecosystem reports point out that the company is working on software tools that help logistics providers optimize route planning, fleet operations, and data-driven decision-making.

The logistics industry is undergoing rapid digital transformation as companies adopt new platforms to improve supply-chain visibility and reduce operational costs. Solutions like the one being developed by Vectrix aim to help operators streamline transportation management and enhance delivery efficiency.

The seed investment will be directed toward technology development and expanding the company’s engineering team.

Image: Vectrix 

Hollywood Investors Move Into AI and Tech

Reese Witherspoon

Reese Witherspoon has built a strong reputation as a tech-forward investor, backing female-led technology and media startups through her investment activities. Her focus on data-driven storytelling and creator platforms reflects how AI and analytics are reshaping content production and distribution.

Ashton Kutcher

Ashton Kutcher remains one of the most influential celebrity investors in Silicon Valley. Through A-Grade Investments, he was an early backer of companies such as Airbnb and Uber and has continued to invest in AI-adjacent and data-centric startups, earning credibility as more than a passive investor.

Will.i.am

Musician and entrepreneur Will.i.am has consistently promoted AI as a creative and consumer-facing technology. He has invested in AI-powered consumer devices and collaborates with major technology firms on AI-first products, often emphasizing inclusivity and accessibility.

More Celebrities Investing in Tech and AI

Beyond Hollywood’s most visible investors, several other high-profile figures are quietly shaping the technology ecosystem:

Leonardo DiCaprio

Leonardo DiCaprio has invested in technology startups focused on sustainability, data analytics, and climate tech, many of which rely heavily on AI for modeling, forecasting, and optimization.

Serena Williams

Serena Williams, through her venture fund Serena Ventures, actively backs AI-driven, data-centric startups, with a focus on diversity, fintech, and enterprise technology.

Jay-Z

Jay-Z has invested in technology platforms and data-driven businesses, including startups leveraging AI for content distribution, creator monetization, and consumer insights.

Karlie Kloss

Karlie Kloss has combined direct investment with education through Kode With Klossy, helping expand access to coding, data science, and AI fundamentals for young women—supporting the talent pipeline behind future innovation.

Mark Wahlberg

Mark Wahlberg has participated in funding rounds for AI-powered consumer and fitness technology platforms, reflecting growing interest in personalized, data-driven products.

Why Celebrity Investment Matters

Celebrity-backed capital often acts as a catalyst rather than a substitute for traditional venture funding. High-profile investors can accelerate consumer trust, attract strategic partners, and help startups cut through crowded markets. In AI especially—where adoption often depends on public perception and ethical trust—well-known backers can play a critical role in shaping narratives.

At the same time, this trend reflects a broader shift: AI is no longer a niche enterprise technology. As it moves into media, entertainment, sustainability, and everyday consumer products, it naturally attracts investors who operate at the intersection of culture and commerce.

As artificial intelligence continues to influence how content is created, distributed, and consumed, the involvement of movie stars, musicians, athletes, and models is likely to grow—turning celebrity investors into unexpected but influential stakeholders in the future of technology.

Material by Irina Kalaydjieva

Image: Leonardo DiCaprio – Wikimedia / Flickr by Raph_PH

Image: Serena Williams – Wikimedia / NickRewind

Image: Jay-Z – Wikimedia / Flickr by Alex Johnson

Image: Karlie Kloss – Wikimedia / Flickr by Erik Drost

Image: Mark Wahlberg – Wikimedia / Flickr by Eva Rinaldi

Image: Ashton Kutcher  – Wikimedia / European Union, 2026

Image: Reese Witherspoon – Wikimedia / Flickr by dtstuff9

Image: Will.i.am – Wikimedia / Xuthoria

Lace AI, a startup using artificial intelligence to supercharge customer service for home service companies, has raised $19 million in total funding, according to company’s website referring a TechCrunch article. The Mountain View-based company disclosed a previously unannounced $5 million pre-seed round led by Canvas Ventures, and a new $14 million seed round led by Bek Ventures. Other investors include Horizon VC, Launchub, Snowflake co-founder Marcin Zukowski, and Vivino’s Heini Zachariassen.

Founded in early 2022, Lace is the brainchild of Boris Valkov, a former AI engineer at Meta who helped develop PyTorch. Drawing from his early experiences in his family’s grocery store and later engineering roles at VMware and Meta, Valkov teamed up with Stan Stoyanov to launch a platform that blends AI with customer service.

Lace’s software analyzes 100% of incoming customer calls for home services companies—like HVAC, plumbing, and roofing—to detect missed sales opportunities and improve booking rates. The company says some clients have reported double-digit revenue growth and claims a 1,000% increase in annual recurring revenue (ARR) in 2024, after beginning sales at the end of 2023.

Currently working with over 100 businesses, including A1 Garage Door Service and Sage Home, Lace operates on a SaaS model, charging per agent. With 20 employees today, the company plans to triple its headcount using the new capital.

Bek Ventures’ managing partner Mehmet Atici told TechCrunch the firm was drawn to Lace’s strong team and its focus on applying AI to traditionally underserved industries—an approach he believes represents a major opportunity.

Photo: Lace AI