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CompeteIQ brings Battlecard intelligence directly into Glean. CompeteIQ's new Glean integration makes current Battlecard intelligence discoverable within the enterprise knowledge environment where employees already search for answers. CompeteIQ Team September 19, 2026 Competitive intelligence is most valuable when it reaches teams at the moment they need it. That is why CompeteIQ is introducing a new integration with Glean, bringing trusted Battlecard intelligence into the enterprise knowledge environment where employees already search for information. Competitive intelligence where sellers already work Competitive intelligence teams invest significant time researching competitors, monitoring market changes, and translating those findings into actionable guidance. The challenge is often distribution: ensuring that the right intelligence reaches the right employee at the right time. CompeteIQ's integration with Glean helps solve that last-mile problem. Once the integration is enabled, eligible CompeteIQ Battlecards can be indexed and synchronized with Glean. Newly created Battlecards are queued for indexing, changes to existing Battlecards are synchronized, and Battlecards removed from active use can also be removed from Glean. This allows CompeteIQ to remain the system where competitive intelligence is created, managed, and maintained while Glean provides another way for employees to discover and use it. From CompeteIQ Battlecards to enterprise knowledge The first release focuses specifically on CompeteIQ Battlecards. Battlecards provide sellers with actionable competitive guidance during active opportunities, including competitor positioning, differentiation, product capabilities, competitive developments, and guidance from the competitive intelligence team. By indexing this content in Glean, organizations can make CompeteIQ Battlecard intelligence discoverable alongside their broader enterprise knowledge, subject to their Glean configuration and permissions. Administrators can also synchronize existing eligible Battlecards when establishing the integration, allowing organizations to bring their current Battlecard library into Glean. Intelligence that stays current A meaningful competitive intelligence integration cannot rely on a one-time content export. CompeteIQ maintains the intelligence and its synchronization lifecycle. New Battlecards can be indexed, changes generate updates, and Battlecards that are no longer active can be removed. The result is a connected model: CompeteIQ creates and maintains the competitive intelligence, while Glean helps make it discoverable where employees already look for answers. Starting with Battlecards Battlecards are the first CompeteIQ content type supported by the integration. CompeteIQ's broader intelligence repository also includes Differentiation Analyzers, newsletters, daily intelligence, source evidence, and other competitive deliverables. These additional content types are not included in the initial Glean release. Beginning with Battlecards gives organizations access to one of the most actionable forms of competitive intelligence while establishing a foundation for broader integration over time. Bringing competitive intelligence closer to the moment of decision Competitive intelligence should not require employees to remember where to look for it. CompeteIQ's integration with Glean is designed around a simple idea: create and maintain trusted competitive intelligence in CompeteIQ, then make that intelligence available in the environments where employees already look for answers. For organizations using both CompeteIQ and Glean, trusted Battlecard intelligence can become part of the enterprise knowledge experience - closer to sellers, closer to customer conversations, and closer to the decisions it was created to support. Ready to transform your CI program? See how CompeteIQ can help you win more competitive deals with unified competitive intelligence.

Wonderful hits $5B valuation with $550M enterprise AI OS round. Wonderful raised $550M at a $5B valuation for its enterprise AI operating system. Inside the Series C, the forward-deployed model, and the competition. Key takeaways. * 1Wonderful closed a $550M Series C at a $5B valuation on September 2, 2026 - roughly 2.5x the $2B valuation it carried in March 2026, and more than $800M raised in about 20 months of existence. * 2The pitch is consolidation: a single model-agnostic 'operating layer' for agents, workflows, integrations, and governance, positioned against the risk of enterprises rebuilding SaaS sprawl with AI tools. * 3Salesforce joining as a new investor is the round's most interesting signal - the company is simultaneously building its own agent platform, making this both a bet and a hedge. * 4Wonderful is selling services as hard as software: forward-deployed engineering pods that push a first use case into production, then hand the capability back to the customer. * 5No ARR figure has been disclosed, which separates Wonderful from peers like Sierra and Glean that have publicly anchored valuations to revenue milestones. Twenty months after it started, a company that did not exist in 2024 is worth $5 billion. On September 2, Amsterdam-headquartered Wonderful announced a $550 million Series C led by Insight Partners, with Salesforce joining as a new investor alongside returning backers Index Ventures, IVP, Vine Ventures, 9Yards, and Bessemer Venture Partners. The round values the company at $5 billion - up from roughly $2 billion in March 2026, when it raised $150 million. Total funding since its founding in early 2025 now exceeds $800 million. The number is eye-catching. The category claim is more interesting. Wonderful is not selling an AI agent, a chatbot, or a copilot. It is selling what it calls an AI operating system - a shared layer that sits underneath every agent, workflow, and AI-native application inside a large organization. That framing is a direct bet on where enterprise AI spending consolidates next, and it puts Wonderful in a fight with better-capitalized specialists on one side and the incumbent platform vendors on the other. Including, awkwardly, one of the investors in this very round. The round by the numbers. All figures from Wonderful's announcement and contemporaneous reporting on the September 2, 2026 Series C. Series C round size Wonderful / Business Wire, Sept 2026 Post-money valuation Wonderful, Sept 2026 Total raised since early 2025 CTech / TechFundingNews, Sept 2026 Employees across 35+ markets Wonderful, Sept 2026 What an "AI operating system" Actually means here. The phrase is doing a lot of work, so it is worth unpacking what Wonderful says is in the box. The platform bundles four product lines that can be bought separately or combined: managed workflows that automate end-to-end business processes, productivity agents aimed at employees and decision support, AI-native applications intended to complement or replace legacy software outright, and conversational agents for customer-facing work. Underneath sits the shared layer that gives the product its name - enterprise context, integrations, security, and governed execution, applied consistently across everything built on top. Two architectural decisions matter more than the product taxonomy. First, the platform is model-agnostic. Wonderful's AI Gateway routes each request to whichever model suits the task, sending complex prompts to frontier models and cheap ones to smaller models, with per-team rules governing which groups can use which models and monitoring that flags cost spikes and unusual access patterns. Second, it is deployment-agnostic, running on any cloud environment including on-premise - a requirement, not a nicety, for regulated buyers in finance and healthcare. The developer-facing tooling is more conventional than the marketing suggests: team-scoped project folders, version control with rollback, and A/B testing so customers can compare agent variants before promoting one to production. That is deliberate. The company's argument is that enterprises already know how to run software; what they lack is a governed place to put AI. The thesis, in the founder's words. CEO and co-founder Bar Winkler frames the opportunity as a repeat of the cloud transition - with a specific warning attached about what happens if enterprises skip the platform layer. " Just as cloud platforms became the foundation of the modern enterprise, AI operating systems will become the foundation of every enterprise. Its customers are already proving that once AI reaches production in one part of the business, it quickly expands across the enterprise. Without a shared operating system, AI risks recreating the sprawl of traditional SaaS. - Bar Winkler, CEO and Co-founder, Wonderful Where Wonderful sits in a crowded, well-funded field. Wonderful is competing for the same enterprise budget as several companies that raised earlier and, in some cases, larger. The distinguishing variable is scope: most peers own one layer of the stack, while Wonderful is claiming all of them. Note that valuations below are private and reported, and ARR figures are company-disclosed rather than audited. | Company | Reported valuation | Latest round | Primary claim | | Wonderful | $5B (Sept 2026) | $550M Series C | Full-stack enterprise AI OS; agents, workflows, apps, governance | | Sierra | $15B+ (May 2026) | $950M, led by GV and Tiger Global | Customer-facing agents; outcome-based pricing, ~$200M ARR reported | | Glean | $7.2B (Dec 2025) | $150M Series F | Enterprise search and knowledge; $300M ARR reported May 2026 | | Decagon | $4.5B (Jan 2026) | $250M Series D | AI customer support automation; per-conversation pricing | The real product May be the engineers. The least software-like part of Wonderful's model is arguably the most important one. The company deploys forward-deployed engineering pods - teams that sit inside the customer's organization, connect the platform to real systems, and drive a first use case into production. Then, per the company's own description, they transfer the capability so the enterprise can build and operate on the platform independently. Insight Partners describes the compounding logic explicitly: reusable integrations and accumulated enterprise context are supposed to make each deployment cheaper than the last. This is the Palantir playbook, and in 2026 it has become close to standard among enterprise AI vendors. The reason is uncomfortable but well-documented. Gartner has forecast that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing unclear business value, escalating costs, and inadequate risk controls. Multiple 2026 surveys put the share of enterprises that have genuinely scaled agents across the organization at roughly a quarter, even as the share running some kind of pilot approaches universality. The gap between a working demo and a governed production system is where enterprise AI budgets go to die. High-touch services close that gap. They also compress gross margins and make growth a function of headcount, which is precisely why Wonderful says a large share of this round goes toward expanding international FDE teams. Investors are underwriting a services-heavy business at a software multiple - a bet that the accumulated context and reusable integrations eventually flip the ratio. One more detail worth sitting with: Salesforce is now an investor. Salesforce sells its own agent platform into the same buyer. A strategic check from a competitor is usually a distribution signal, a hedge, or an early look at an acquisition target. Sometimes all three. What the round does not tell you. Wonderful has disclosed valuation, headcount, market count, and total capital raised. It has not disclosed annual recurring revenue, customer count, retention, or the software-versus-services split of its revenue - all of which peers at similar valuations have made public. Reported customer traction is described qualitatively ("hundreds of agents, systems, and workflows across a dozen verticals") rather than in contracted terms. Treat the $5 billion figure as a statement of investor conviction about the category, not as a proxy for realized revenue. Questions to ask before buying an "AI operating system" * What exactly happens to the agents and integrations you build if you leave the platform - do you retain the artifacts, or just the outputs? * Which models can you route to today, and how quickly are new frontier models added to the gateway? * How is the forward-deployed engagement priced, and at what point does it end rather than become a permanent line item? * Can the platform run in your existing security perimeter - including fully on-premise - without a feature downgrade? * What does governed execution actually enforce: approval gates, audit logs, data residency, per-team model permissions, or all four? * How does the vendor measure whether a deployed workflow is working, and will they contract to that metric? * Given that a large share of agentic projects are forecast to be cancelled, what is your own kill criterion and review date before you sign? Frequently asked questions. #Wonderful #enterprise AI #AI agents #Series C funding #AI operating system #Insight Partners #agentic AI #Salesforce

Runtime: the Tau of Glean. + Nvidia starts to Groq inference, and the quote of the week. Welcome to Runtime! Today on Product Saturday: Glean launches a new desktop app for searching across corporate documents and data, Harness wants to be the home for your AI-generated code, and Thomson Reuters makes its own model. Please forward this email to a friend or colleague! If it was forwarded to you, sign up here to get Runtime for free every week, or level up here. Ship it. See the light: Agent harnesses haven't caught on with the general business user to the same extent that coding agent harnesses have altered the trajectory of software development, but that's starting to change. Still, a lot of companies that are concerned about data security and runaway token costs need assurances that hooking employees up with autonomous business tools won't result in sensitive data flying out the window in the most expensive way possible. Glean has raised $768 million to build out its enterprise search platform, which uses agents and coworking tools to allow IT departments to trust their users (to the extent any IT department ever does, anyway) with autonomous workflows. This week it announced a new version of the desktop experience of that platform called Glean Tau, which it compared to Anthropic's Claude Cowork. Glean Tau "can plan, execute, review, recover, and act on a user's behalf across tasks like organizing files, analyzing documents, creating spreadsheets, and working across connected apps," the company said in a press release. It hooks into Glean's broader enterprise platform, which makes it easier for companies to put restrictions on data access without preventing employees from finding the right data for the right task. But Glean's main message behind the introduction of Glean Tau was to appeal to the tokenomics crowd, claiming that Glean is about 80% cheaper to use than Claude Cowork based on the results of its own benchmark, which is a little funny. Your mileage may vary, but if AI-powered business assistants are going to make a real dent in the enterprise, cost - as always - will be a big factor. Delivery, continued. The year of the GitHub-killer: GitHub's ongoing inability to provide a stable home for enterprise code (you can read about this week's incidents here) will force some companies to make hard decisions as agents redefine almost every part of software development, and the sharks are starting to circle. Last week The Stack highlighted the launch of Cursor's Origins service, and this week Harness rolled out its own take on a modern repository. "Harness Code Repository is what source control looks like when agents are part of the team," Harness said in a press release. It was designed to "handle thousands of pull requests and commits opened at once," the company said, and comes with strict access-control features that users can apply to AI agents to keep them in their lane. Moar tokens: This week's Hot Chips conference featured several interesting launches, including SiFive's first RISC-V server, but Nvidia continues to rule over the AI chip landscape. The fruits of its kinda-sorta acquisition of Groq last year were released this week, with the Groq 3 LPX inference chip reaching full production. Designed as a co-processor with the Vera Rubin rack-scale server, the new chip's mission is to increase "the rate at which tokens are generated for an individual user, [which determines] how quickly an agent can complete each step of its work," it said in a press release. The Groq licensing deal was an interesting admission from Nvidia that while its flagship GPUs remain the state-of-the-art for training, AI inference is going to require some different approaches. DIY AI: The rise of open-weight models has been one of the more interesting developments in enterprise tech this year, helping companies control costs, setting up a market for AI gateways that route traffic between models, and allowing some businesses to take their AI strategy in-house. Thomson Reuters published a paper this week detailing how it trained a new model called Thomson (of course) using open-weight models such as Aliababa's Qwen. The new model, which is also open weight, "performs competitively with recent frontier models on a wide range of domains and capabilities, ranging from agentic tasks to safety, legal, tax & multilingualism, to comprehensive large-scale Deep Research," the company said in the paper. Thomson Reuters also said that it spent just $450,000 on the final training run for the large version of the model, while estimating the total cost required to develop the model at around $40 million. Quote of the week. "When it breaks, what's going to happen is your leadership is going to try to hold you accountable for the system that you built. And if you don't understand what you built, you're going to start panicking." - Microsoft principal engineer Raki Rahman, who told The Stack that even in the AI coding agent era, software developers need to own their code. The Stack is also reading: One of East Coast's largest data centers accused of "violating federal law": Floodlight sent a drone over a prominent data-center facility in New Jersey run by Nebius, and just like Elon Musk's disregard for the health of the citizens who live around SpaceXAI's massive data center in Memphis, found the facility is operating "dozens of unpermitted gas-powered generators." Thanks for reading - see you Tuesday!