Evertune
Evertune is the Generative Engine Optimization (GEO) platform that helps brands improve visibility in AI search across ChatGPT, AI Overview, AI Mode, Gemini, Claude, Perplexity, Meta, DeepSeek and Copilot.
We're building the first marketing platform for AI search as a channel. We show enterprise brands exactly where they stand when customers discover them through AI — then give them the precise playbook to show up stronger. This is Generative Engine Optimization, also known as AI SEO.
Using applied AI and data science at scale, we give brands statistical confidence in our actionable insights. We decode what gets brands mentioned more and ranked higher, provide reliable brand monitoring and competitive intelligence, then deliver actionable content strategies that move the needle. Our AI SEO and AI search engine optimization tools are built for how LLMs actually work.
Why Leading Enterprise Marketers Choose Evertune:
Data Science at Scale: We prompt across every major LLM at volumes that capture response variations and ensure statistical significance for comprehensive brand monitoring and competitive intelligence.
Actionable Strategy, Not Just Dashboards: Specific content, messaging and distribution tactics that increase your AI search visibility.
Dedicated Customer Success: Hands-on training and strategic guidance to turn insights into improved performance in AI search.
Built for AI search as a channel: Organic visibility today, paid advertising and commerce tomorrow.
Proven Leadership: Founded by The Trade Desk veterans who pioneered data-driven digital advertising. Backed by data scientists from OpenAI, Meta and other AI leaders.
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AthenaHQ
AthenaHQ is a powerful platform focused on Generative Engine Optimization (GEO), helping brands improve their AI search visibility and brand perception across AI-powered search engines. It offers tools to track brand mentions, identify gaps in AI-generated content, and enhance content to align with AI’s evolving preferences. With features like daily tracking, competitor analysis, and source intelligence, AthenaHQ provides actionable insights to help businesses stay relevant in an AI-dominated search landscape. The platform's AI-powered capabilities enable businesses to optimize content and drive more meaningful engagement through generative search.
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Devstral 2
Devstral 2 represents a cutting-edge, open-source AI model designed specifically for software engineering, going beyond mere code suggestion to comprehend and manipulate entire codebases, which allows it to perform tasks such as multi-file modifications, bug corrections, refactoring, dependency management, and generating context-aware code. The Devstral 2 suite comprises a robust 123-billion-parameter model and a more compact 24-billion-parameter version, known as “Devstral Small 2,” providing teams with the adaptability they need; the larger variant is optimized for complex coding challenges that require a thorough understanding of context, while the smaller version is suitable for operation on less powerful hardware. With an impressive context window of up to 256 K tokens, Devstral 2 can analyze large repositories, monitor project histories, and ensure a coherent grasp of extensive files, which is particularly beneficial for tackling the complexities of real-world projects. The command-line interface (CLI) enhances the model's capabilities by keeping track of project metadata, Git statuses, and the directory structure, thereby enriching the context for the AI and rendering “vibe-coding” even more effective. This combination of advanced features positions Devstral 2 as a transformative tool in the software development landscape.
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Devstral Small 2
Devstral Small 2 serves as the streamlined, 24 billion-parameter version of Mistral AI's innovative coding-centric model lineup, released under the flexible Apache 2.0 license to facilitate both local implementations and API interactions. In conjunction with its larger counterpart, Devstral 2, this model introduces "agentic coding" features suitable for environments with limited computational power, boasting a generous 256K-token context window that allows it to comprehend and modify entire codebases effectively. Achieving a score of approximately 68.0% on the standard code-generation evaluation known as SWE-Bench Verified, Devstral Small 2 stands out among open-weight models that are significantly larger. Its compact size and efficient architecture enable it to operate on a single GPU or even in CPU-only configurations, making it an ideal choice for developers, small teams, or enthusiasts lacking access to expansive data-center resources. Furthermore, despite its smaller size, Devstral Small 2 successfully maintains essential functionalities of its larger variants, such as the ability to reason through multiple files and manage dependencies effectively, ensuring that users can still benefit from robust coding assistance. This blend of efficiency and performance makes it a valuable tool in the coding community.
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