Wispr raises $280 million to develop voice AI technologies beyond dictation
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Wispr raises $280 million to develop voice AI technologies beyond dictation

Significant capital is required to build next-generation artificial intelligence infrastructure due to rising costs for computing power and hardware. To address this issue and move beyond traditional dictation, startup Wispr has raised $280 million in a Series B funding round at a $2 billion valuation.

This large influx of capital will allow the company to train its own voice models that easily process complex real-world audio material. By proactively solving expensive computational needs, Wispr aims to create a universal voice interface that will replace text input on everyday devices and digital platforms.

Most people view voice control as a simple tool for setting timers or writing short messages. However, Wispr envisions a future where natural human speech becomes the primary method of data entry across all daily-use software applications.

Wispr's flagship application, Flow, allows users to speak seamlessly into any text field on both desktop and mobile operating systems. Flow processes human speech in real time, eliminating the need for clumsy raw transcriptions. It removes filler words like 'um' and 'uh', corrects grammatical errors, and subtly fixes stumbles on the fly. Thus, it transforms the user's hasty, vague ideas into clear, professional text without tedious manual editing and typing.

The new capital directly accelerates the development and deployment of Canto—Wispr's proprietary voice model designed specifically for noisy real-world environments. Standard speech-to-text engines work acceptably in quiet rooms but fail completely in typical conditions. Background conversations, barking animals, heavy traffic, and coffee shop noise usually lead to significant transcription errors. Canto overcomes this major hurdle by training directly on unfiltered, chaotic audio data.

Thanks to this, Canto reduces the frequency of speech recognition errors in noisy environments from 30% to less than 10%. Users can easily dictate long emails or complex documents while traveling or in open-plan offices. Consequently, Canto lowers the percentage of speech recognition errors in loud places from 30% to less than 10%, allowing users to comfortably dictate detailed letters or complex strategic documents while commuting, walking through busy city streets, or being in noisy offices.

Major venture capital funds clearly see enormous long-term value in Wispr's technical approach and ambitious product vision. The Series B funding round was led by Menlo Ventures alongside Notable Capital, NEA, Neo Ventures, 8VC, and MVP Ventures. With this strategic injection of funds, Wispr has raised a total of $361 million. The technology is already gaining significant popularity among corporate teams, creative professionals, and even notable users. To date, users have generated over 60 billion words on the Flow platform.

Employees from almost all Fortune 500 companies and over 10,000 enterprises use Flow daily. Multilingual specialists and professional athletes highly value Flow for its ability to smoothly switch between languages without losing words. In the future, with the advancement of artificial intelligence, traditional physical keyboards and touchscreens may become obsolete. With a substantial reserve of funds, Wispr is using this money to create a persistent voice layer situated directly beneath every desktop and mobile application. Instead of switching between different apps or manually typing long messages, users will simply be able to speak naturally to their screens. Combined with the optimization of specialized voice models, voice input becomes significantly faster and more accurate than manual typing.

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CodeRabbit raises $143 million in Series C round to manage AI-powered software development
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CodeRabbit has successfully raised $143 million in a Series C funding round, valuing the company at $1.5 billion. Atomico and Smash Capital participated as co-leads in this round. New investors include BMW i Ventures, Datadog, Hirtle Callaghan, and SineWave Ventures. Existing investors, such as CRV, Scale Venture Partners, and Flex Capital, also supported the deal.

This funding was secured less than a year after CodeRabbit raised $60 million. The company reports that its revenue has increased more than fivefold compared to the previous year. Currently, CodeRabbit performs over two million code checks weekly for 17,000 clients, including companies like Adyen, BMW, Indeed, JFrog, NVIDIA, and Trivago.

The capital raised will be used to accelerate the company's international expansion and increase research and product development expenses. CodeRabbit plans to invest over $10 million to support open-source projects, which will allow it to maintain free access to AI review and agent features for accompanying projects.

AI coding tools enable engineering teams to create software at unprecedented speeds, as developers can generate code, create pull requests, and troubleshoot much faster. Nevertheless, every change introduced still requires testing, verification, and proper management before going into operation.

CodeRabbit asserts that this shift is transforming software development management practices in organizations. Traditional task trackers were designed to work with planned work and developer-driven execution. However, AI-based development allows code to appear before teams achieve full consensus.

This makes the pull request an increasingly critical control point. CodeRabbit CEO, Hardjot Gill, noted: 'Writing code has become dramatically easier. Verifying its quality remains complex.' He emphasized that changes created by AI still require independent verification before being released into the production environment.

CodeRabbit's goal is to provide this level of control for software created by both humans and agents. The platform analyzes changes using repository context, organizational standards, and testing evidence. The system also indicates the scope of impact and architectural dependencies that require further attention.

Along with the latest funding announcement, CodeRabbit introduced Agentic Change Management. This system expands AI review capabilities, including prioritization, explanation, and continuous code security monitoring.

Its purpose is to help engineering teams understand exactly which changes require attention. The CodeRabbit Triage tool ranks incoming pull requests based on value, urgency, and risk. It also considers reviewer dependencies, readiness, and suitability before routing the task. Lower-risk changes can pass through automated workflows, while complex changes are directed to human reviewers. CodeRabbit Change Stack provides deeper explanations of software changes and their potential impact by analyzing contracts, domain behavior, integrations, tests, and migrations between repositories.

CodeRabbit is also expanding its presence in security with an AI-powered product. This system continuously scans production code for vulnerabilities and emerging risks. It verifies detected issues against code evidence before recommending a fix via pull requests. This approach targets vulnerabilities that traditional rule-based tools might miss, including authorization bypasses, insecure object references, and business logic errors. The company believes that continuous monitoring will become increasingly significant as AI generates more software.

The latest funding will also support CodeRabbit's international expansion strategy. The company recently opened an office in London to serve corporate clients in Europe and plans further expansion in Europe, Japan, and other Asian markets. Luca Eisensteken, a partner at Atomico, will join the CodeRabbit board of directors. He stated that the infrastructure for independent governance will become increasingly important for AI-generated software. CodeRabbit believes that its model-agnostic approach will help organizations maintain software quality as AI adoption accelerates.

Convex raises $57 million in Series B round to develop a reliable AI-based backend platform
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Convex raises $57 million in Series B round to develop a reliable AI-based backend platform

Convex has raised $57 million in a Series B funding round. These funds are planned to be used to scale the development of its backend platform, which is specifically designed for artificial intelligence. The financing was led by Insight Partners, with participation from Etna Labs and all previous investors, including a16z and Spark Capital.

Thanks to this Series B round, Convex's total raised capital has reached $110.5 million. The company stated that it intends to use this money to improve its core platform, enhance tools for agentic development, and increase its staff in its San Francisco office.

Founded in 2021 by former Dropbox infrastructure engineers, Convex offers developers a fully integrated backend platform. This platform combines databases, functions, workflows, search, synchronization, authentication, and file storage into a single solution built for software development.

The fundraising comes amid the trend towards AI-powered coding and autonomous development tools. Convex believes that developers need infrastructure capable of handling the increasing complexity of applications while maintaining a high level of reliability.

The company posits that traditional backend infrastructure struggles to support the growing use of AI coding agents. According to the company, AI-generated applications based on standard databases can subtly introduce data errors that only manifest after deployment.

Internal testing revealed that 90% of AI-built applications running on traditional databases encountered data corruption in real-world operating conditions. In comparison, applications developed on the Convex platform passed the same tests without any failures.

Convex's backend includes built-in ACID transactions, end-to-end type checking, real-time subscriptions, and TypeScript support. These features reduce the need for manual integration of multiple services for developers and increase application reliability. Furthermore, Convex packages search capabilities, workflow automation, authentication, file storage, and retrieval-augmented generation functions, allowing AI applications to be enriched with organization-specific knowledge.

The platform has gained significant traction among developers building AI-based software. Convex currently supports nearly two million applications created by about 500,000 developers worldwide. The company also reported over 1.2 million weekly downloads from npm.

Convex's clients include OpenAI, Tripadvisor, Solana, Zapier, and Reducto. These organizations use Convex to simplify backend development while ensuring scalable, reliable performance for their applications.

Insight Partners noted that the growing adoption of AI coding assistants has exposed weaknesses in traditional software infrastructure. The investment firm believes that Convex provides developers with a more robust foundation where backend components function cohesively without the need for complex configuration.

Convex plans to use the new funding to accelerate product innovations amidst the transformation of software engineering influenced by AI. The company will direct funds toward creating new tools that simplify application development for both developers and AI agents that build software.

The company will continue to provide highly reliable backend products supporting increasingly complex AI-based applications globally. Convex is convinced that stable backend infrastructure is becoming critically important as artificial intelligence generates large volumes of application code.

Instead of combining disparate components, Convex offers a single unified platform, which effectively reduces complexity and prevents software bugs. The raised funds will strengthen Convex's engineering team and significantly expand its global developer ecosystem and platform capabilities. With fresh capital and a growing customer base, Convex aims to solidify its position in the evolving AI development infrastructure market, making the creation of reliable software faster and easier for both human developers and AI agents.

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