RTX Spark, unveiled on Monday by Jensen Huang, chief executive during a keynote in Taipei ahead of the Computex technology conference, brings about a shift in how computers are used, moving away from traditional software-based workflows towards systems that can carry out tasks through AI agents.

“The PC is being reinvented,” Huang said. “For forty years, you launched apps. Click. Type. With RTX Spark and Microsoft Windows, you ask, and the PC does the work.”

RTX Spark is designed as a superchip built for what Nvidia describes as the “era of personal AI agents”. It combines a Blackwell-based GPU with a Grace CPU, delivering up to 1 petaflop of AI performance and 128GB of unified memory. 

Nvidia says this setup is intended to support complex AI tasks running directly on the device rather than in the cloud.

The company explained that the chip will allow users to run large language models locally, including systems with up to 120 billion parameters, while also handling demanding creative and gaming workloads. These include editing high-resolution video, generating AI video content, and running advanced 3D rendering tools.

Nvidia said RTX Spark systems will support Windows PCs built for what it calls “personal agents”, software that can carry out tasks across applications. The company is working with Microsoft to integrate the technology into Windows, including new security features designed to control how AI agents operate on a device.

Microsoft chairman and chief executive Satya Nadella said the collaboration aims to expand access to advanced computing tools. “Our goal is to deliver unmetered intelligence to every home and every desk with Windows,” he said.

The companies noted that the new Windows platform will include tools that allow users to manage what AI agents can access, how data is handled, and when information is processed locally instead of being sent to the cloud.

RTX Spark also targets creators and developers as Nvidia said the chip can support 90GB 3D scene rendering, 12K video editing, and AI-assisted design work. Users will be able to run high-end gaming titles at 1440p resolution with frame rates above 100 frames per second.

Adobe is among the companies adapting its software for the new system. It is reworking Photoshop and Premiere to take advantage of the hardware, with expected performance gains in AI tools such as generative editing and video expansion features.

Shantanu Narayen, Adobe’s chair and chief executive, said the changes would speed up creative work. “The best creative work in the world happens in Adobe tools from Adobe Firefly to Photoshop and Premiere, and the expansion of our partnership with NVIDIA and Microsoft will make those experiences faster and more powerful than ever,” he said.

Other software and gaming companies are also involved, including Blackmagic Design, Blender, ComfyUI, OTOY, and Xbox, all of which said they are preparing support for the new platform.

Hardware makers are preparing devices around the chip. Nvidia said laptops and compact desktops will be produced by companies including ASUS, Dell, HP, Lenovo, Microsoft Surface and MSI, with Acer and GIGABYTE also expected to join later. The first devices are scheduled for release in the autumn.

RTX Spark systems are expected to come in slim laptop designs and compact desktops aimed at both professionals and consumers. Nvidia said laptops will feature lightweight builds, OLED displays and all-day battery life.

With the launch, Nvidia is going beyond its traditional graphics chip business into full PC system design. Analysts say the move places the company in closer competition with Intel, AMD and Apple in the personal computing market.

The company is expanding AI features across its platforms, with YouTube set to introduce a new search experience called Ask YouTube.

The tool will allow users to ask more complex questions and refine their searches through follow-ups. Again, it will combine Shorts and long-form videos, as well as surface specific parts of clips that match what people are looking for.

“With Ask YouTube, you can ask more complex search queries, such as wanting tips on how to teach your kid to ride a bike, or finding creator reviews of cosy games to play before bedtime,” the company explained. “You can even ask follow-up questions to continue refining what you’re looking for.”

The feature is currently being tested with YouTube Premium users in the United States on desktop, with a wider rollout planned for later this year.

YouTube also announced a new video creation feature linked to Gemini models, including Gemini Omni. The company said the system will be used in Shorts Remix and the YouTube Create app to help users generate and edit video content with more control over output.

“Remixing with Omni delivers a fresh way for users to create and build on each other’s imagination,” YouTube wrote in a press release. “The model better understands user intent creating more consistent and meaningful storytelling while also handling complex video and audio adjustments behind the scenes.”

Alongside this, YouTube is expanding its likeness detection system for creators aged 18 and above. The tool is designed to help identify when a creator’s face is used in AI-generated content without permission. Creators can request takedowns where misuse is detected.

Separately, Google chief executive Sundar Pichai used his keynote at I/O to outline the company’s progress and the scale of its AI systems.

“It’s been an extraordinary year since our last I/O, a period of relentless shipping, technology advances and hyper progress,” he said. “We’re now in the part of the AI cycle where people want to see the value in the products they use every day.”

He said Google is still focused on building across its hardware, research and product stack, with AI now central to Search, Android, cloud services and developer tools.

He also pointed to rapid growth in usage across Google systems, with the company now processing far more data through its models than in previous years. Usage is also surging across consumer and enterprise tools.

Search is the company’s largest distribution point for its AI features. AI Overviews now has more than 2.5 billion monthly users. AI Mode has also passed 1 billion monthly users.

The Gemini app has grown as well, now having more than 900 million monthly users, up from 400 million the previous year. Google said usage frequency has increased significantly as new features are added.

The company also highlighted adoption by developers and cloud customers. Millions of developers now build with its models, while enterprise usage continues to scale across industries.

Pichai said the company is seeing strong engagement with generative tools for study, work and creative tasks, including image and video generation.

On infrastructure, Google is expanding investment in custom chips known as TPUs to support growing demand. It also outlined newer generations of its hardware designed for both training and inference workloads.

These systems are aimed at improving speed, efficiency and scale across its services, particularly Search and Gemini.

Google also introduced Gemini 3.5 Flash at the I/O conference, a faster and more cost-efficient model designed for broad use across products and developer tools. The company said it improves performance across coding and real-world tasks while reducing operating costs for large-scale usage.

In internal testing, the model is already being used in developer systems to speed up work on new tools and services. Google is also expanding its agent-focused platform, Antigravity, which allows users to manage autonomous AI agents that can carry out tasks over time.

A new version, Antigravity 2.0, is being released as a desktop application for developers and enterprises.

Google also introduced Gemini Spark, a personal agent designed to work across apps and services on behalf of users. It can run tasks in the background and update users on progress.

Spark will integrate with Google products and third-party tools. It will first roll out to trusted testers before expanding to subscribers in the United States.

On the consumer side, Google said it is moving Search towards more interactive and task-based experiences. New features will allow users to set up agents that monitor information and deliver updates automatically.

The company also announced new tools for content creation, at the I/O conference, including Google Flow for planning and editing complex projects, and Google Pics for image generation and editing using its Nano Banana model.

It shared progress on wearable devices, such as audio glasses that can deliver spoken assistance and upcoming display glasses that show information in real time.

Google is also expanding tools for science and research, linking its Gemini systems with external databases to support scientific work and experimentation.

The company said the updates are aiming for systems that can handle tasks, not just respond to queries, as it expands its products across consumer, developer and enterprise markets.

The deal values Manus at between $2 billion and $3 billion, although Meta has chosen not to disclose financial terms. Manus, now based in Singapore, did not respond to requests for comment at the time of writing.

The acquisition gives Meta full control of Manus’s technology, which it plans to operate, sell and embed across its consumer and business products, including Meta AI. 

This will help to secure what many in the industry now see as the most valuable layer in AI development, the execution layer, where software agents go beyond conversation to carry out complex tasks on their own.

Manus rose quickly into the global spotlight earlier this year after releasing what it described as a general AI agent. Unlike standard chatbots, the system was designed to make decisions and complete tasks with minimal prompting. 

The product went viral on X and was soon compared to DeepResearch, a benchmark tool in the sector. The company has claimed its agent outperforms that system, helping to drive both attention and controversy.

Behind the attention was rapid commercial growth. Manus became the fastest startup to cross $100 million in annual recurring revenue, reaching a $125 million run rate in under eight months. 

In 2025 alone, its systems processed 147 trillion tokens and powered around 80 million virtual computers, figures that point to unusual scale for a company so young. That pace made Manus one of the most talked-about AI agent startups globally.

Meta’s interest shows a change in strategy. While the company has spent years building open-source foundation models such as Llama, this deal reveals a goal to own proprietary systems that sit on top of those models and actually do the work. 

Autonomous agents that can research, write code and analyse data are now the next battleground, and competition is increasing fast.

The acquisition comes after a year of heavy spending by Meta, including its investment in Scale AI, a deal that valued the data-labelling firm at $29 billion. 

Competitors are not standing still. Microsoft is expanding Copilot, Google is pushing Gemini, and OpenAI is developing DeepResearch.

Manus’s background also adds a geopolitical edge to the deal. Founded in China and backed by its parent company, Beijing Butterfly Effect Technology, the startup was once described as China’s next DeepSeek. 

It later relocated to Singapore, joining a growing number of Chinese tech firms seeking to reduce exposure to Sino-US tensions. Singapore’s neutral stance and trade-friendly policies have made it a preferred base for global expansion.

Beijing had shown interest in supporting Manus, and the company maintains a strategic partnership with Alibaba to collaborate on AI models. 

Meta’s acquisition changes ownership firmly to a U.S. technology giant, a development that is likely to be closely watched in both Washington and Beijing.

The startup’s debut product, Chipmind Agents, is aimed at automating the repetitive, low-level work that ties up engineering teams and drags out design cycles.

Chipmind describes its agents as a new class of tools that work from a customer’s own, proprietary design data.

The agents are built to slot into existing engineering flows, learn a company’s toolchain and design hierarchy, and then carry out multi-step design and verification tasks autonomously, all while leaving final control with the human engineer. The company claims engineers can save roughly 40% of their time on routine chores.

The problem Chipmind targets is familiar to anyone who has worked in chip design: massive, customised EDA flows that simply were not built to talk to modern automation. Chipmind’s founders say they didn’t try to rip out those legacy systems.

Instead, they built a platform that prepares them for agentic automation and wraps intelligence around the existing stack.

“In the semiconductor industry, deep customisation and data protection are fundamental, but true design awareness is what separates a generic tool from an intelligent partner. Each company’s chip is a complex hierarchy with unique constraints, surrounded by a proprietary environment of tools and workflows,” said Harald Kröll, co-founder and CEO of Chipmind. 

“That is the reality we built for. Our ‘design-aware’ agents are engineered to holistically understand the entire chip context, not just the surrounding tools. We’ve found this deep awareness is the key that unlocks productivity, translating directly into significant time savings on the most complex tasks, all while integrating seamlessly into existing workflows.”

The startup’s origin is rooted in academic and industry experience. Co-founders Harald Kröll and Sandro Belfanti met at ETH Zurich during their PhDs and together have been involved in the development of more than 20 chips, from mobile modems to system-on-chip designs. Their experience, they say, exposed how much of chip engineering is precise but repetitive work.

“Anyone who’s spent time in chip development knows how much of the work is repetitive and time-consuming, demanding precision but not necessarily creativity,” said Sandro Belfanti, Co-Founder and CTO of Chipmind. 

“Throughout my career developing chips at top-tier semiconductor companies, I’ve often wished for a solution that could magically take care of those tedious tasks so I could focus on solving real engineering challenges.

“With Chipmind Agents, we’re finally bringing that solution to life: AI agents that can autonomously handle the boring parts, letting engineers focus on what truly matters: innovation.”

Chipmind’s first funding round was led by Founderful, with several semiconductor industry angels joining the table. The founders say the money will go to hiring engineers, speeding up product work and deepening relationships with strategic customers.

The launch arrives as chip design grows continually harder, demand for compute rises, design complexity increases, and simply adding headcount is not a realistic fix. Chipmind places itself as a practical bridge, a way to keep existing toolchains while automating the parts of the process that slow teams down.

Edouard Treccani, principal at Founderful, added: “In a world buzzing with AI every day, Chipmind stands out as a refreshingly real solution to a problem Harald and Sandro have spent 20 years deep in. From day one, they’ve built in close dialogue with the market, and the early feedback has been remarkably positive. Founderful is thrilled to be part of their journey!”

Chipmind is offering demos to semiconductor groups interested in testing its agents and says it will continue scaling its engineering team as it works with early customers.

Announced ahead of its flagship Dreamforce 2025 conference, set to begin on October 14, Agentforce 360 introduces new features designed to make AI agents more responsive, adaptable, and integrated into daily enterprise workflows. 

The update includes tools that allow users to build, test, and deploy AI agents with greater control and flexibility.

One of the features is Agent Script, a prompting tool that lets users instruct AI agents through conditional “if/then” logic. The tool, set for beta release in November, gives companies the ability to improve how agents respond in nuanced or unpredictable situations, such as complex customer queries. 

According to Salesforce, these agents will use “reasoning” models powered by Anthropic, OpenAI, and Google Gemini, which “think before responding” rather than relying solely on pattern-based outputs.

Another addition is Agentforce Builder, a unified workspace where users can create, test, and launch AI agents from a single interface. The platform also includes Agentforce Vibes, a customisation framework for defining the tone and “personality” of enterprise applications. Both features are expected to enter beta testing in November.

Salesforce is also expanding its integration with Slack, placing the workplace platform as a hub for enterprise intelligence. Starting this month, Salesforce’s core applications, including Sales, IT, and HR, will become accessible directly within Slack, with further expansion planned through early 2026. 

A new version of Slackbot is being piloted as well, designed to act as a personal assistant that learns user preferences and offers proactive insights and suggestions.

The company’s long-term plan is to make Slack a full-scale enterprise search and collaboration tool, connecting it with external platforms like Gmail, Outlook, and Dropbox by 2026.

Salesforce says it currently serves 12,000 Agentforce customers, including early adopters such as Lennar, Adecco, and Pearson. 

Salesforce’s innovation could be a huge one for businesses still having issues with measurable returns from their AI investments. A recent MIT study found that 95% of enterprise AI pilots fail before reaching production, emphasising the challenges companies face in scaling these technologies effectively.

Competitors are also moving quickly. Google recently launched Gemini Enterprise, with clients like Figma and Klarna, while Anthropic secured a major deal with Deloitte to roll out its Claude Enterprise chatbot to 500,000 employees and later announced a partnership with IBM. Unveiling Agentforce 360 just before Dreamforce, Salesforce aims to showcase incremental updates, but has a bigger vision for how enterprise AI should function, integrated, adaptable, and built for long-term scalability.

The platform, built on Google’s Gemini models, aims to strengthen the tech giant’s competitive edge in enterprise AI, as Microsoft, OpenAI, and Anthropic, among others, are not holding back.

Gemini Enterprise functions as a conversational system that enables employees to interact directly with company data, documents, and applications. The goal, Google says, is to make AI a core part of every workflow, not just an add-on. “We’re introducing Gemini Enterprise, designed to bring the full power of Google’s AI to every employee, for every workflow,” the company said during the launch.

The announcement comes as Google Cloud continues to expand, having surpassed a $50 billion annual revenue run rate in the second quarter of 2025. According to the company, about 65% of its cloud customers already use Google’s AI products, including nine of the world’s top ten AI labs.

Gemini Enterprise builds upon Google’s full-stack AI strategy, combining the strength of its infrastructure, foundational models, and research divisions such as Google DeepMind. The platform is powered by a multi-layer system, from the company’s purpose-built Tensor Processing Units (TPUs) and Nvidia GPUs, to its world-leading Gemini models, which have consistently topped global performance benchmarks.

Google describes Gemini Enterprise as “the new front door for AI in the workplace.” It features pre-built AI agents capable of conducting deep research, generating data insights, and automating complex workflows. Through a no-code interface, companies can also create and deploy their own agents, tailored to their specific operations.

Early adopters of the platform include Gap, Klarna, and Figma. Klarna, for instance, is using Gemini’s generative tools to produce personalised lookbooks that have boosted customer orders by 50%. 

In the healthcare sector, HCA Healthcare has deployed a “Gemini-powered Nurse Handoff solution” that simplifies patient information transfers between shifts, a move expected to save millions of hours annually.

Google is also using its own technology internally. Nearly half of all new code at the company is now generated by AI and reviewed by engineers, significantly accelerating development cycles.

The launch further reveals Google’s vision for a connected AI ecosystem. Gemini Enterprise securely links with data across multiple platforms, including Google Workspace, Microsoft 365, Salesforce, and SAP, ensuring enterprise-wide access without compromising governance or security.

To encourage adoption, Google is rolling out Google Skills, a free training platform that will teach users how to build and deploy agents within Gemini Enterprise. The company also introduced Delta, a team of expert AI engineers who will work directly with clients to deploy advanced solutions.

With Gemini Enterprise, Google is embedding AI at the very foundation of how organisations work. “AI is presenting a once-in-a-generation opportunity to transform how you work, how you run your business, and what you build for your customers,” the company stated.

With the rapid adoption of AI agents in developer workflows, organisations face a critical challenge: there is no standard way to test them. 

These agents interact with users and systems in highly dynamic ways, making it difficult to ensure reliability, accuracy, and consistency. Traditional testing methods fall short when the system under test is inherently unpredictable.

LambdaTest’s Agent-to-Agent Testing introduces a first-of-its-kind solution. The platform employs a suite of specialised AI testing agents to rigorously validate chat and voice-based AI agents across multiple dimensions including conversation flows, intent recognition, tone consistency, reasoning ability, and more.

Teams can upload requirement documents in various formats—text, images, audio, or video—and the system automatically performs multi-modal analysis to generate test scenarios that simulate real-world challenges. 

Each scenario comes with precise validation criteria and expected responses, executed within HyperExecute, LambdaTest’s next-gen test orchestration cloud that delivers up to 70% faster execution compared to traditional automation grids.

The platform also tracks key quality metrics such as bias, completeness, and hallucinations, giving teams deeper insights into the performance of their AI agents. By leveraging a multi-agent approach with multiple large language models (LLMs), LambdaTest ensures broader, more diverse, and more accurate test coverage than single-agent testing systems.

“Every AI agent you deploy is unique, and that’s both its greatest strength and its biggest risk,” said Asad Khan, CEO and co-founder at LambdaTest. “Our Agent-to-Agent Testing platform thinks like a real user, generating context-aware test scenarios that mirror real-world challenges. Each test includes clear validation checkpoints and the responses we’d expect to see.”

Enterprises adopting Agent-to-Agent Testing can expect faster test creation, reduced testing cycles, and up to a 10x increase in test coverage. By automating much of the QA process, organisations can cut costs while improving test depth and reliability.

With 15 purpose-built AI testing agents, ranging from security researchers to compliance validators, LambdaTest’s Agent-to-Agent Testing enables teams to ship AI-powered applications with confidence, ensuring they are robust, safe, and enterprise-ready.

1. Notion – Flexible All‑in‑One Workspace

• Combines docs, wikis, kanban boards, and calendars in one managed interface Reddit+13mail.mycvcreator.com+13ikonik.digital+13.

• AI-enhanced features like content summarization and smart search boost productivity across teams.

2. Slack GPT (Slack) – AI‑Driven Team Collaboration

• Real-time messaging platform now with generative AI: auto-reply drafting, meeting summarization, workflow automation (Slack GPT).

• A core part of digital workplaces, integrated deeply with Salesforce and business systems.

3. Monday.com – Visual Work Management with AI Automation

• Intuitive dashboards, task tracking, and predictive automation streamline project planning.

• Ideal for cross-functional teams and enterprise-grade coordination.

4. HubSpot AI / HubSpot CRM – Smart Sales & Marketing Stack

• Combines CRM, AI-driven lead scoring, and marketing automation in a scalable, user-friendly interface.

• Especially appealing to SMEs and startups with usage-based pricing.

5. Salesforce (Einstein GPT & Agentforce) – Enterprise CRM with AI Agents

• Vast ecosystem including Sales Cloud, Marketing Cloud, Service Cloud, Salesforce GPT, and Agentforce.

• Automates customer service and marketing tasks with autonomous AI agents.

6. SnapLogic – AI-Powered Data Integration (iPaaS)

• Offers seamless integration across cloud and on-prem systems, with AI agents (SnapGPT, AgentCreator) for workflow automation.

• Featured in Gartner’s Visionary Quadrant for iPaaS in 2024–25.

7. FuseBase – AI Agent Portal for Internal & External Collaboration

• Built on the Model Context Protocol (MCP), enabling AI agents to interact across tools, systems, and data sources.

• Ideal for deepening collaboration and automation within hybrid enterprise environments.

8. Kissflow – Low-Code/No-Code Automation Platform

• Enables non-technical users to build applications and automate workflows with AI support.

• Best suited for citizen developers and process-centric use cases.

Emerging & Niche Tools Worth Watching

From crowdsourced recommendations by founders:

• Cursor, Claude AI, Bolt, Replit, Grok, and others offer AI-assisted coding, prototyping, and operational automation without deep coding skills.

• Zapier, PostBridge, TinyLaunch, Profioilio, and Buildpad simplify marketing, product launches, growth hacking, and validation workflows for startups.

Market Trends Driving SaaS tools Innovation in 2025

• AI-Powered SaaS: Integrated AI is now standard, from workflow assistants to customer-facing agents.

• Low‑Code/No‑Code Platforms: Democratizing tech-building for non-engineers and SMEs.

• Vertical SaaS: Specialized platforms tailored to niche industries (e.g. health, legal) are rising fast.

• SaaS Security & RPA: Increasing demand for automation, Zero Trust architecture, and secure workflows (UiPath, Power Automate, CyberArk).

• AI Agents & Coding Agents: Autonomy in code review, deployment, and internal tool-building is on the rise (GitHub Copilot Reviewer, Cursor BugBot).

Summary Table

• The adoption of AI agents is shifting how businesses handle workflows, devops, and operations, breaking traditional software paradigms.

• Democratization through no-code/low-code means more people can build and benefit from SaaS solutions, especially in underserved markets.

• Vertical and niche solutions are winning because they solve industry-specific pain points, making adoption faster and more impactful.

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One of its strategic partners is Anthropic, the startup now sitting on a $13.8 billion investment from Amazon.

TechCrunch confirmed through two sources directly familiar with the development that this is a calculated strike at a fragmented and increasingly competitive sector. 

AWS’ offering will give startups a central hub to publish their AI agents while giving enterprise users a one-stop shop to search, test, and deploy the tools they need.

This marketplace will offer plug-and-play integration, allowing customers to deploy agents directly within AWS environments. Startups, in turn, will be able to charge fees based on usage or subscriptions, an approach similar to how Software-as-a-Service (SaaS) models operate. 

AWS will take a revenue cut, but insiders say it’s deliberately minimal to entice developers.

Notably, Google Cloud launched its Agent Marketplace back in April. Microsoft followed suit in May with its Agent Store inside Microsoft 365 Copilot. Salesforce and ServiceNow also offer similar platforms for enterprise workflows. Amazon’s late entry may raise eyebrows, but its execution appears bigger in scope and more commercially aggressive.

What gives AWS a potential edge is infrastructure. With its own cloud powering the backend, AWS can offer developers access to hundreds of thousands of GPUs for building, training, and running agents, something most competitors simply can’t match.

Anthropic’s involvement, meanwhile, may prove critical. The company’s Claude model, already considered one of the few credible rivals to OpenAI’s GPT-40, will form the backbone of many of its own agentic offerings. 

Anthropic also enables third-party developers to build agents using its API, and with its revenue reportedly hitting $3 billion annually as of May 2025, its model appears to be resonating with the enterprise crowd.

“The marketplace could help us reach a broader set of users, especially those who are already operating in AWS environments or looking for interoperability,” one Anthropic insider said, requesting anonymity due to lack of public authorisation.

Essentially, this launch also aims to solve what’s fast becoming one of the most annoying problems in the AI space: fragmentation. Today, most AI agents live inside walled gardens. 

A customer who uses agents built on one cloud can’t easily deploy or integrate them into another system. By unifying offerings under one marketplace, AWS hopes to simplify that chaos, and grab a major piece of what analysts expect to become a $50.3 billion market by 2030.

Even with this, everyone is wondering if these marketplaces actually bring value to smaller startups or get swallowed by dominant players. That outcome may depend less on marketplace design and more on execution, and whether AWS can place itself not just as a provider, but as the centre of gravity in the emerging agent economy.

In the meantime, AWS is betting heavily that developers want more than just raw compute, they want access, scale, and visibility. With Anthropic on board and infrastructure already in place, Amazon may be late to the party, but it didn’t come unarmed.

Today, the company announced $15 million in new funding to scale its voice AI platform, which automates high-friction insurance calls that cost provider organizations and billing companies billions of dollars every year.

The debt and equity series A round was led by SignalFire, with participation from Slow Ventures, BoxGroup, and Scrub Capital. It includes $3 million in venture debt for SuperDial to invest in R&D and go-to-market initiatives. 

In total, the company has now raised over $20 million in funding. This also marks one of the first investments from SignalFire’s new $1 billion fund focused on applied AI.

SuperDial builds AI agents that handle outbound phone calls from providers and billing companies to insurers – navigating phone trees, waiting on hold, and conducting live conversations with payer reps. 

These AI agents support tasks like benefits verification, prior authorisation, claims follow-up, and credentialing. When a call can’t be completed by an AI agent, SuperDial’s human call centre team steps in, ensuring reliable outcomes while continually improving the AI.

The platform integrates with EHRs and other systems of record to automate documentation, including writing back data gathered from calls, such as claims status updates. Customers rely on SuperDial not just to cut costs, but to unlock capacity across their revenue operations teams. Customers have reported up to 3x cost savings per call and 4x productivity gains for their existing billing teams. 

SuperDial was founded by Sam Schwager and Harrison Caruthers, who met at Stanford while studying computer science. After building a healthcare billing company that spent thousands of hours on repetitive calls to payers, they saw the opportunity to automate the problem. What started as an internal tool quickly grew into a standalone solution.

“The timing is perfect for us to tackle this problem at scale, with AI capabilities quickly maturing and the healthcare sector looking for new ways to drive efficiency by leveraging next-gen technology. Our success to date, and the incredible level of interest and excitement we’re seeing from the market, are clear signs that we’re solving a real, urgent problem,” said Sam Schwager, co-founder and CEO of SuperDial. 

Since launching at the end of 2023, the company has quickly scaled to seven figures in revenue and tens of thousands of calls per week. 

Earlier this year, SuperDial acquired MajorBoost, a voice AI company specialised in navigating complex phone trees and insurer workflows. The acquisition deepened SuperDial’s technical team and further cemented its leadership in healthcare-specific call automation.

SuperDial’s growth comes as healthcare organisations seek to cut admin costs without expanding headcount. The $150 billion U.S. RCM market still relies on manual phone calls for basic tasks – calls that can take over an hour and pull staff away from higher-impact work.

SuperDial’s customers include RCM companies and large provider organisations – including DSOs and MSOs – that manage billing in-house. Their customers rely on SuperDial to improve financial performance, reduce burnout, and unlock their teams’ capacity to focus on higher-value work. 

At West Coast Dental, SuperDial now handles over 10,000 calls per month to check claim statuses, a process that previously left nearly 70,000 claims in backlog and would have required five new hires to process. With SuperDial, the team has significantly reduced AR days and gained trustworthy, up-to-date visibility into claims.

“SuperDial isn’t just automating phone calls – they’re building the connective tissue for how the healthcare ecosystem will communicate in the future,” said Yuanling Yuan, Partner at SignalFire. 

“We believe agentic AI infrastructure is inevitable, and SuperDial is leading that shift with rapidly growing traction and a team that deeply understands the problem. This is exactly the kind of applied AI we’re excited to back.”

Looking ahead, SuperDial will deepen its EHR integrations, expand to new administrative workflows, and continue training its agents using real-world call data. 

Although healthcare never built the APIs to enable clean, system-to-system communication, SuperDial is building the next best thing: a network of AI agents that can navigate fragmented infrastructure on behalf of the organisations that rely on it. 

SuperDial believes the future of healthcare coordination will be agent-powered – where payers, providers, pharmacies, labs, and other healthcare organisations can seamlessly communicate with one another, AI-to-AI. And SuperDial will power that future.