OpenAI Integrations

Tabbit Browser is an innovative web browser that incorporates AI capabilities seamlessly into the online experience, merging browsing, searching, automation, and AI support all in one place. Rather than isolating AI as a standalone chatbot, this browser employs AI tools that are attuned to the context of the webpages, files, and tabs the user is engaging with, enabling a more sophisticated interaction with the content while navigating the internet. Users can enhance the AI's understanding by providing references such as text snippets, screenshots, web pages, or files, which allows the AI to produce targeted answers and insights that are pertinent to what they are currently studying. Additionally, the browser offers the versatility of switching between various advanced AI models like GPT, Gemini, and Claude, empowering users to select the most appropriate model for their specific tasks or workflows. A standout feature of Tabbit Browser is its interactive chat capability with web content; users can highlight text, take screenshots, or refer to pages, prompting the browser to summarize, clarify, or analyze the details without needing to navigate away from the page. This integration of AI not only enhances productivity but also enriches the user’s overall browsing experience.

GPT-5.4 Pro is a high-performance AI model introduced by OpenAI for users who require maximum capability when solving complex problems. It builds on earlier GPT models by integrating advanced reasoning, coding, and workflow automation into a single system. The model is designed to assist professionals with demanding tasks such as data analysis, financial modeling, document generation, and software development. GPT-5.4 Pro can interact directly with computers and applications, allowing AI agents to perform multi-step workflows across different tools and environments. Its extended context window supports up to one million tokens, enabling it to analyze large amounts of information while maintaining accuracy. The model also improves deep web research and long-form reasoning tasks. Developers benefit from improved tool usage and search capabilities that help agents select and operate external tools efficiently. GPT-5.4 Pro delivers stronger coding performance and faster iteration cycles for developers working on complex software projects. It also reduces token usage compared with earlier models, improving cost efficiency and speed. Overall, GPT-5.4 Pro is designed to support advanced professional workflows and AI-powered automation at scale.

GPT-5.4 Thinking is a specialized version of OpenAI’s GPT-5.4 model designed to deliver enhanced reasoning and structured problem-solving in ChatGPT. It integrates improvements in coding, professional knowledge work, and agent-based workflows into a single AI system. One of its key features is the ability to present a plan for its reasoning before generating a final answer. This allows users to review the direction of the response and make adjustments while the model is still working. By enabling this interactive process, GPT-5.4 Thinking helps produce more precise and relevant results. The model is particularly effective for tasks that require deep research or multi-step reasoning. It also maintains context across longer prompts and conversations, reducing confusion in complex discussions. GPT-5.4 Thinking improves how AI interacts with tools and software environments during problem-solving workflows. Its advanced reasoning capabilities allow it to handle analytical tasks with higher consistency and clarity. As a result, GPT-5.4 Thinking is designed to support professionals who need reliable AI assistance for complex work.

GPT-5.4 mini is an advanced AI model designed to provide a balance between high performance, speed, and cost efficiency. It is built to handle a wide range of tasks, including coding, reasoning, tool usage, and multimodal understanding. Compared to earlier versions, GPT-5.4 mini delivers significantly improved performance while operating at faster speeds. The model is particularly effective in environments where low latency is essential, such as real-time coding assistants and interactive applications. It supports capabilities like function calling, tool integration, and image-based reasoning, making it highly versatile. GPT-5.4 mini is also well-suited for subagent architectures, where it can efficiently process smaller tasks within larger AI systems. Developers can use it to automate workflows, analyze data, and build responsive AI-driven applications. Its strong performance across benchmarks shows that it approaches the capabilities of larger models in many scenarios. At the same time, it maintains a lower cost, making it ideal for high-volume usage. Overall, GPT-5.4 mini provides a powerful and scalable solution for modern AI development.

GPT-5.4 nano is a compact and cost-efficient AI model designed for handling lightweight, high-frequency tasks at scale. It is optimized for operations such as classification, data extraction, ranking, and simple coding assistance. The model delivers fast response times, making it suitable for applications where low latency is critical. Compared to earlier nano models, GPT-5.4 nano offers improved performance while maintaining minimal computational cost. It supports key features such as tool usage and structured output generation, allowing it to integrate easily into automated systems. The model is often used as a subagent within larger AI workflows, handling repetitive or supporting tasks efficiently. This approach allows more complex models to focus on higher-level reasoning and decision-making. GPT-5.4 nano is particularly useful in environments that require processing large volumes of requests quickly. Its efficiency makes it ideal for cost-sensitive applications and scalable deployments. Overall, it provides a reliable and fast solution for simple AI-driven tasks.

Manifold Security is an innovative AI Detection and Response platform designed to protect autonomous AI agents functioning on enterprise endpoints, effectively filling a significant void left by conventional cybersecurity methods that predominantly concentrate on user interactions or the inputs and outputs of models. This platform delivers real-time insights into the actual activities of AI agents, capturing their behaviors while they engage with systems, perform commands, access files, and utilize APIs throughout both development environments and production infrastructures. By monitoring agent activity directly on endpoints without the need for extra infrastructure like proxies or gateways, organizations can oversee genuine actions instead of merely analyzing prompts or replies. Furthermore, it establishes connections among agents, tools, and associated services, providing a comprehensive perspective on which agents are operational, the permissions they possess, and their interactions with both internal and external resources. This holistic approach not only enhances security protocols but also empowers organizations to better understand and manage their AI systems effectively.

Inagent is a cutting-edge conversational AI solution tailored for contact centers, featuring autonomous AI agents that excel in comprehending, reasoning, and managing intricate customer interactions across both voice and text channels within a comprehensive omnichannel framework. Utilizing generative AI and contextual awareness, these virtual agents are adept at deciphering messages, making informed decisions, and carrying out tasks linked to business systems like CRM and ERP platforms to fulfill defined operational objectives. This platform empowers organizations to assemble specialized teams of AI agents trained in various business sectors, ensuring precise and effective management of responsibilities such as customer support, sales, appointment coordination, and claims processing, while also facilitating smooth transitions of conversations between AI agents and human representatives when necessary. One of the standout features of Inagent is its rapid deployment capability, allowing activation within minutes through an intuitive no-code interface that only necessitates basic training with existing business knowledge. Furthermore, this innovative technology not only improves operational efficiency but also enhances customer satisfaction by providing timely and accurate responses.

Snapper serves as a comprehensive security platform for AI agents, aimed at ensuring thorough governance and protection for organizations that utilize AI across various applications, networks, and systems. It implements runtime enforcement by scrutinizing every action an agent takes, such as tool interactions, API calls, and data access requests, prior to execution, utilizing a multi-layered policy-driven rule engine. Additionally, Snapper provides a holistic view of AI activity by analyzing network traffic, browser usage, DNS queries, and running processes to uncover unauthorized tools and hidden AI applications. It also proactively intercepts outgoing large language model requests via SDK wrappers and a network proxy, allowing it to assess, redact, and document sensitive information in real time. Enhancing its security features, Snapper possesses sophisticated threat detection mechanisms that can recognize prompt injection tactics, exploit chains, unusual behaviors, and complex attack patterns, leveraging behavioral baselines, kill chain analysis, and a composite trust scoring system for robust protection. Ultimately, Snapper represents a critical asset for organizations seeking to navigate the risks associated with AI deployment while maintaining operational integrity.

Silmaril is an innovative defense mechanism against prompt injection that autonomously heals itself, aiming to safeguard AI systems from sophisticated, multi-layered threats that conventional barriers cannot mitigate. Unlike traditional methods that merely filter inputs, it envelops inference calls, assessing whether the sequence of actions is steering towards a detrimental result. By employing a multihead classifier, it evaluates user intentions, application contexts, and execution states simultaneously, which allows it to identify indirect injections, multi-turn attack sequences, context manipulation, and tool exploitation before any harm can occur. To enhance its protective capabilities, Silmaril incorporates autonomous threat-hunting agents that explore systems, identify weaknesses, and produce synthetic training data based on actual attack incidents. These findings facilitate automatic model retraining, allowing for the deployment of updated defenses in less than an hour, while simultaneously disseminating anonymized protective measures across all instances. Moreover, this proactive approach ensures that the system remains resilient against emerging threats, adapting continuously to the evolving landscape of cybersecurity challenges.

Rings AI offers an innovative Extended CRM (XRM) platform that redefines conventional customer relationship management by creating an intelligent, relationship-focused system that enhances deal flow. Instead of functioning merely as a traditional database, it integrates relationship mapping, market intelligence, and AI into a cohesive operating layer that perpetually evaluates the interconnections among individuals, organizations, and opportunities. The platform effortlessly gathers and synchronizes information from emails, calendars, meetings, and various external sources, ensuring that records are constantly updated in real time without requiring manual intervention. By effectively mapping networks and revealing the most promising routes to reach target companies or individuals, Rings empowers users to discover warm introductions, reveal concealed connections, and prioritize their outreach efforts with improved accuracy. Additionally, its AI-driven search functionality allows users to query their entire dataset using natural language, providing timely contextual insights, while its automated intelligence features illuminate past deal activities, enhancing the overall user experience. This combination of features makes Rings AI a powerful tool for optimizing customer interactions and driving business success.

Mosaic is an advanced, AI-powered platform for deal modeling that caters to the needs of private equity firms, investment banks, and deal-making teams by automating and standardizing their financial analysis processes. By moving away from conventional spreadsheet-based methods, it implements a structured and rules-driven approach to swiftly produce accurate deal calculations, enabling users to transform basic deal materials like CIMs and financial statements into comprehensive LBO and operating models in just a matter of minutes. The platform prioritizes speed, precision, insight, and transparency, allowing teams to minimize the time spent on formula linking and instead concentrate on assessing investment opportunities and making informed decisions. Its robust modeling engine guarantees consistency by removing typical spreadsheet pitfalls such as broken links or overlooked calculations, while also providing complete traceability of assumptions and results. Additionally, this innovative solution fosters collaboration among team members, ensuring that everyone is aligned and informed throughout the financial analysis process.

JetStream Security serves as a governance platform focused on security, enabling enterprises to gain comprehensive visibility, control, and responsibility over their AI systems by transforming them from unclear, disjointed applications into managed and traceable infrastructures. Functioning as a unified control center, it integrates identity management, operational governance, monitoring, and financial management into one cohesive system, empowering organizations to “monitor every AI action, associate actions with accountable individuals, and ensure workflows stay within authorized limits” while applying policies during runtime. Furthermore, it incorporates agentic identity, linking human, agentic, and non-human identities to specific actions and access rights, thereby ensuring that each invocation, tool usage, or workflow can be tracked and governed according to least-privilege access standards. By maintaining ongoing runtime governance, JetStream continuously evaluates actual AI behavior against pre-approved frameworks, utilizing immutable logging and real-time monitoring to identify deviations, thereby reinforcing security and compliance. This robust approach not only enhances accountability but also supports organizations in navigating the complexities of AI governance effectively.

CyberTide is an innovative data security platform that leverages AI to provide organizations with comprehensive visibility, control, and safeguarding of sensitive information across various environments, including cloud services, SaaS applications, collaborative tools, and generative AI settings. By integrating several security features into a cohesive framework, such as Data Loss Prevention (DLP), Data Security Posture Management (DSPM), insider risk management, and AI security posture management, it empowers teams to identify, categorize, and protect data in real time. The platform employs context-aware artificial intelligence to thoroughly analyze the meanings and interconnections of data, rather than depending solely on keywords, which greatly minimizes false positives while ensuring precise identification of sensitive content. It actively monitors data both at rest and in transit, encompassing communication channels like emails, chats, and files, as well as AI-generated prompts, all while enforcing stringent policies aimed at preventing unauthorized access, leakage, or misuse of confidential information, including personal, financial, and proprietary data. This proactive approach not only enhances security but also fosters a culture of data protection within organizations.

Straiker is an innovative security platform designed exclusively for safeguarding enterprise AI applications and autonomous agents, particularly addressing the emerging hazards posed by “agentic AI” systems that engage with various tools, APIs, and sensitive data. By offering comprehensive visibility and control throughout the entire AI stack, it analyzes behavioral signals from models, prompts, tools, identities, and infrastructure, which facilitates the immediate detection and prevention of AI-specific threats, including prompt injection, privilege escalation, data exfiltration, and the misuse of tools. The platform integrates continuous discovery, adversarial testing, and runtime protection through essential components such as Discover AI, Ascend AI, and Defend AI, working in harmony to identify all active agents, simulate potential attacks to reveal weaknesses, and implement real-time protective measures during operation. Its intricate, multi-layered architecture captures profound contextual signals from user interactions, network activities, and agent workflows, ensuring a robust defense against evolving threats. As AI technologies continue to advance, the necessity for such tailored security solutions will become increasingly critical for enterprises navigating this complex landscape.

FireTail serves as a comprehensive AI security and governance solution that empowers organizations with thorough oversight, management, and safeguarding of AI applications within their ecosystems. The platform actively identifies AI utilization across various domains, including codebases, cloud services, APIs, software-as-a-service tools, and web browsers, creating a live inventory of both authorized and unregulated AI systems to ensure adherence to governance protocols. It meticulously records and evaluates every interaction with AI, encompassing prompts, responses, metadata, and user identities, thereby offering profound insights into the access patterns of AI models and the pathways through which data navigates. With FireTail, organizations can implement adaptable, context-sensitive policies via a unified governance framework, leveraging established guidelines like OWASP or tailored regulations to uphold compliance while fostering innovation. Furthermore, it consistently tracks activities to identify potential threats such as prompt injection, data breaches, improper model usage, and unusual behaviors, ensuring a proactive approach to security. This ongoing vigilance not only enhances organizational resilience but also promotes a culture of responsible AI usage.

SubQ is an advanced large language model created by Subquadratic to handle complex long-context reasoning tasks. It supports up to 12 million tokens in a single input, making it capable of analyzing entire repositories, extended conversation histories, and large datasets without losing context. The model is built on a sub-quadratic sparse-attention architecture that focuses computational resources on the most relevant data relationships. This design significantly reduces processing requirements compared to traditional transformer models while maintaining strong performance. SubQ is particularly useful for software engineering, coding workflows, and long-context retrieval tasks. It enables developers and teams to process large amounts of information in a single operation instead of splitting tasks into smaller parts. The model offers fast processing speeds and operates at a fraction of the cost of many competing solutions. It is available through API access, allowing integration into enterprise systems and developer tools. SubQ can also be used as a layer within coding agents to improve code exploration and analysis. Its compatibility with existing development environments makes it easier to adopt. With its efficient architecture and large context window, it helps teams work with complex data more effectively.

Notenic serves as a runtime orchestration and governance platform aimed at managing and securing autonomous AI agents, also known as "digital labor," in real-time scenarios where failures could lead to significant regulatory, legal, or operational repercussions. Functioning as an infrastructure layer, it integrates directly into the execution path of AI systems to enforce strict governance protocols prior to any interaction with systems of record, thus avoiding the limitations of post-output filters or controls applied at the prompt level. The platform incorporates a zero-trust runtime architecture characterized by foundational principles such as zero-persistence, which ensures no data is retained after each session, and execution-path control that enforces policies right at the moment actions are taken. This design also emphasizes independence from model context, effectively preventing any adversarial inputs from compromising governed behavior. In addition, Notenic offers a comprehensive control plane that encompasses the management of AI agents, treating them as operational units with clearly defined roles and appropriate oversight, which enhances organizational efficiency and accountability. This robust framework ultimately ensures that AI operations are conducted within a secure and compliant environment.

MS0 Reverse transforms outdated code from a burden into a valuable asset by utilizing AI-driven analysis and intelligence that uncovers business logic, safeguards institutional knowledge, and clarifies intricate systems prior to teams making informed decisions regarding maintenance, refactoring, or modernization. Prioritizing intelligence, the methodology emphasizes a thorough understanding of the system before determining the next steps with certainty. It offers a structured knowledge infrastructure through a comprehensive knowledge graph that illustrates the entire operational framework, encompassing data flows, business logic, dependencies, and inter-program relationships. Various stakeholders, including developers, analysts, architects, executives, compliance teams, product owners, and portfolio managers, can engage with the same systems using natural language, obtaining insights derived from a unified governed knowledge layer. Furthermore, MS0 Reverse supports an open infrastructure through APIs and MCP connectors, facilitating the creation of customized tools, partner extensions, and additional functionalities. This versatility not only enhances collaboration among teams but also empowers organizations to leverage their existing systems more effectively.

Firstwork delivers advanced AI solutions for workforce operations, utilizing enterprise-grade AI agents that enhance candidate fill rates, decrease drop-offs, and facilitate a seamless transition from offer acceptance to the first work shift. Designed specifically for those who shape our world, Firstwork streamlines the various operational tasks that often hinder hiring, onboarding, compliance, and workforce management. The Document Verification agent efficiently authenticates IDs, permits, and other necessary documents in real time, promptly flagging any errors, poor image quality, or missing information to expedite the onboarding process. Additionally, the Browser Automation feature takes care of web-based tasks across multiple external platforms and systems, ensuring checks and updates are completed concurrently with comprehensive audit trails. Furthermore, the Workflow Engine transforms standard operating procedures into automated, flexible workflows powered by AI, empowering teams to create intricate, multi-step processes that operate seamlessly from start to finish without the need for manual oversight, ultimately enhancing overall efficiency. This innovative approach not only improves productivity but also supports a more agile workforce management strategy.

Cherry Studio serves as a comprehensive AI assistant and cross-platform desktop application that integrates numerous AI models into one cohesive workspace compatible with Windows, macOS, and Linux. By connecting with leading model providers, it enables users to seamlessly transition between various AI services without the hassle of managing multiple applications, browser tabs, or disjointed workflows. This tool is crafted to function as a robust local AI productivity center, facilitating tasks like everyday chatting, writing, translation, research, coding assistance, document comprehension, image analysis, and multimodal AI workflows all through a single interface. Users have the capability to customize model providers, oversee assistants, organize discussions, and select different models according to their specific tasks, which makes Cherry Studio valuable for both casual users and those engaged in more intricate experimentation. Additionally, its assistant system empowers users to create, subscribe to, and oversee role-based assistants equipped with tailored prompts for various scenarios, including product management, community operations, technical support, and strategic planning, enhancing the overall user experience and efficiency. This flexibility allows individuals and teams to harness AI effectively, adapting to their unique workflows and requirements.

PromptUnit serves as an AI inference intermediary that automatically minimizes AI expenses by acting as a bridge between an application and its AI service providers, requiring no modifications to existing code. Teams simply replace the base URL while maintaining the same SDK, endpoints, response parsing, and error management, allowing PromptUnit to take care of routing, failover, cost monitoring, and quality assessment. It meticulously logs every API interaction, detailing aspects such as model, feature, user segment, token count, latency, and cost, thereby providing immediate insights into AI expenditures before any routing adjustments are implemented. In its observation mode, PromptUnit meticulously monitors traffic, shadow-classifies incoming requests, predicts potential savings, and clarifies routing choices, enabling teams to visualize exact savings prior to activating live routing. After activation, Smart Routing intelligently classifies tasks to direct each request to the most cost-effective model that meets the established quality standards. Additionally, PromptUnit incorporates features like prompt compression, token inflation protection, efficiency scoring for prompts, semantic request caching, and multi-model consensus for enhanced performance. Its comprehensive approach ensures that organizations can optimize their AI usage and manage budgets effectively.

ASI:One is a versatile personal AI that adapts to individual needs by learning to interact, socialize, and assist with daily tasks. This innovative tool transcends traditional chatbots by tuning into users’ unique voices, senses of humor, preferences, and daily routines as it operates seamlessly within their digital landscape. Users have the opportunity to customize their AI through specific instructions, rules, and skills, along with settings for voice, memory, projects, and ongoing responsibilities, enabling the AI to evolve its functionalities over time. ASI:One is capable of engaging in conversations, utilizing voice interactions, managing extended tasks, browsing online, and controlling compatible tools, as well as organizing plans and delegating tasks to specialized AIs when necessary. Additionally, it can link up with other personal AIs to enhance users' social and professional connections, facilitating encounters with like-minded individuals, orchestrating social outings, coordinating group events, and nurturing relationships, even when the user is offline. This level of integration and personalization makes ASI:One an invaluable companion in everyday life.

Saris AI serves as a specialized platform for banks and credit unions, designed to streamline workflows in lending, compliance, and operations through automation, while ensuring that teams maintain control with secure, compliant, and auditable processes. Its advanced AI agents excel in managing complex back-office tasks that are often labor-intensive and repetitive, which can be challenging for conventional robotic process automation (RPA) tools to handle effectively. By optimizing backend functions related to lending, deposit services, collections, risk assessment, and compliance, Saris enables financial institutions to reallocate skilled personnel from monotonous operational duties to more impactful roles that benefit members, customers, and the broader community. Furthermore, Saris seamlessly integrates with existing core banking systems and loan origination platforms, allowing institutions to enhance their workflows without the need for disruptive overhauls of their existing technological frameworks. This capability not only preserves their current infrastructure but also empowers them to adapt to evolving market demands.

GuardionAI serves as an Agent and MCP Security Gateway, delivering comprehensive security for AI agents and Model Context Protocol tools that interact with enterprise data. Positioned within the execution path, it effectively identifies and redacts sensitive information, implements protective measures, and offers enhanced visibility into activities that conventional SIEM, DLP, and identity frameworks typically miss. Every action performed by agents is meticulously scrutinized, enforced, and logged at the protocol level, encompassing AI agents, LLM applications, RAG systems, chatbots, coding assistants, MCP servers, internal applications, databases, operating systems, and cloud infrastructures. GuardionAI is designed to counteract critical AI vulnerabilities including prompt injection, system overrides, web-based assaults, MCP tool tampering, malicious code execution, exposure of NSFW content, leakage of PII and credentials, unauthorized access to confidential data, off-topic drift, and breaches of access control, all aligned with the OWASP LLM Top 10 and agentic AI threat frameworks. Notably, the gateway offers a robust four-layer protection system, ensuring that organizations can safeguard their AI assets more effectively than ever before. This multifaceted approach not only enhances security but also empowers teams with the insights needed to navigate the complexities of modern AI environments.

General Analysis serves as a cutting-edge AI security platform designed to aid security teams in adversarially testing, monitoring, and safeguarding AI agents and systems that are actively deployed. Its primary objective is to enable organizations to grasp AI-related risks, avert potential incidents, and secure various real-world AI applications, which include employee copilots, coding agents, customer support tools, healthcare assistants, legal aids, financial copilots, and creative workflows. By mapping out AI applications and agents through an extensive range of parameters such as prompts, retrieval methods, tools, MCP servers, browser activities, permissions, repositories, cloud accounts, SaaS workflows, and business processes, it effectively identifies context-aware attacks that highlight vulnerabilities within the system. The platform's automated red teaming employs adaptable attacker models that respond to target behaviors and generate complex multi-step exploit chains, providing security teams with the ability to discover vulnerabilities that traditional static prompt sets or endpoint-only testing might overlook. Ultimately, General Analysis empowers organizations to enhance their AI security posture while ensuring that their deployments remain resilient against evolving threats.

XecGuard, developed by CyCraft, serves as a firewall for trustworthy and agentic AI, specifically engineered to safeguard enterprise AI systems against various threats such as prompt injection, data leakage, and unsafe outputs. Leveraging CyCraft's extensive experience in red and blue teaming within sectors like government, finance, and high-tech manufacturing, XecGuard enhances security measures by integrating AI guardrails with cybersecurity protocols, compliance safeguards, and risk management tactics, ultimately facilitating the safe adoption of enterprise AI. This innovative solution functions as a plug-and-play LoRA security module, allowing organizations to bolster their LLM defenses seamlessly without necessitating modifications to the underlying model architecture, thus ensuring rapid implementation while maintaining optimal performance. By utilizing proprietary security datasets and advanced multi-stage fine-tuning methods, XecGuard significantly improves the resilience of LLMs against adversarial attacks, malicious interventions, and unauthorized extraction of sensitive information, making it an essential component for any enterprise aiming to fortify its AI systems effectively. Furthermore, its ability to adapt quickly to emerging threats underscores its value in today’s fast-evolving technological landscape.

ZOOOP is an innovative creative platform tailored for creators and film production teams, seamlessly integrating advanced AI video, image, and audio technologies into a single streamlined workflow. Designed for those who wish to harness AI in their creative endeavors without the hassle of managing multiple tabs, subscriptions, and disjointed tools for various media assets, ZOOOP simplifies the process. It elevates content generation to a core aspect of creativity, ensuring that each AI-generated image, video clip, and audio track is managed within a unified Generative Canvas. This cohesive workspace allows for a fluid transition between tasks, enabling creators to progress from scripting to storyboarding and shot refinement without the need for repetitive exporting and re-uploading. The platform's AI video toolkit is comprehensive, offering features such as text-to-video conversion, image-to-video transformation, first and last-frame interpolation, video extension, section editing, camera motion management, and AI-driven lip sync capabilities. With ZOOOP, the creative process becomes not only more efficient but also more enjoyable, empowering creators to focus on their artistry.

Puter.js serves as the backend for applications powered by AI, enabling you to leverage your current AI coding tool to develop fully functional apps while reducing AI token usage by as much as 90%. This comprehensive JavaScript library integrates features such as authentication, cloud storage, databases, and support for various AI models including OpenAI, Claude, Gemini, Grok, Kimi, and DeepSeek, all without the need for API keys or complex setup processes. With its streamlined approach, you can focus on creating innovative applications more efficiently.

Avante brings AI to the ongoing work of running an employee benefits program. HR and benefits teams use it to work through plan changes, create communications, analyze program performance, and give employees personalized support. The platform combines two AI agents. Ava supports the benefits team, helping turn program documents and data into updated FAQs, employee communications, leadership reports, and analysis of costs, utilization, and vendor performance. Carly supports employees with 24/7 guidance on coverage, plan comparisons, and benefits relevant to their circumstances. Both agents work from the employer's plans, programs, and eligibility rules. When new plan information is added, Ava helps identify gaps and conflicting documents and prepare updated materials for review. That shared program context also informs Carly's employee guidance. Company strategy and priorities guide the agents' work. Source citations, ongoing evaluations, guardrails, and escalation to human support provide controls around responses and outputs. Avante works alongside existing benefits systems, with access through Slack, Microsoft Teams, and email. It serves mid-to-large employers and the brokers and consultants who support them.

ZeroGPU serves as a compute efficiency layer tailored for AI inference, enabling AI applications to minimize their inference costs by shifting high-volume tasks to dedicated models within an edge-powered inference network. This solution is founded on the principle that many production-level AI tasks do not necessitate advanced reasoning capabilities; instead, activities like document analysis, content summarization, page classification, signal extraction, PII detection, web content processing, query routing, and message moderation can generally be handled effectively by smaller, task-oriented models rather than costly frontier models. By utilizing ZeroGPU, developers can pinpoint workloads that lack the need for deep reasoning and efficiently direct them to specialized small language models and nano models. This process involves executing these tasks across optimized servers, leveraging approved edge capacity and cloud fallback, while also providing a framework to assess cost savings, improvements in latency, reduction in reliance on frontier-model calls, and overall model performance. In doing so, ZeroGPU not only enhances operational efficiency but also contributes to the broader accessibility of AI technologies.

Arato.ai serves as a comprehensive platform for the development of structured, dependable, and production-ready large language models (LLMs), aimed at empowering teams to confidently create, assess, and expand generative AI applications. While it is designed to handle intricate systems, Arato simplifies the process by seamlessly integrating with any LLM stack and connecting to existing AI applications without the need for rewrites, extensive setup, or intricate integrations. This platform allows teams to simulate multi-modal user experiences through text, voice, data, or images, enabling them to evaluate AI behavior prior to customer interaction and ensure alignment with AI regulatory standards such as the EU AI Act and ISO/IEC 42001. One of Arato's standout features, Arato Simulate, functions as a black-box simulation tool that emulates realistic user traffic to rigorously test AI applications for accuracy, security, compliance, costs, and user experience, all assessed based on their business impact. By identifying issues that traditional testing methods often overlook—such as multi-turn conversations, edge cases, adversarial situations, persona-specific shortcomings, and large-scale challenges—Arato enhances the reliability and effectiveness of AI applications. Ultimately, this innovative platform not only streamlines the development process but also ensures that AI solutions are robust and ready for real-world deployment.

SubQ 1.1 Small is the second iteration of Subquadratic’s long-context AI model, built to help enterprises solve problems that require reasoning across entire artifacts rather than isolated chunks. The model is designed for use cases involving large code repositories, document libraries, legal agreements, financial reports, contracts, and other complex information sets. Its Subquadratic Sparse Attention architecture reduces the compute burden of traditional dense attention, making it more practical to process multi-million-token contexts. SubQ 1.1 Small achieves near-perfect performance on needle-in-a-haystack retrieval tests up to 12M tokens, despite being trained primarily at 1M tokens. It also performs strongly on RULER, GPQA Diamond, LiveCodeBench, and AutomationBench Finance, showing a balance between long-context retrieval and general reasoning ability. At 1M tokens, the model uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. This efficiency makes long-context training and inference more scalable for enterprise AI applications. SubQ 1.1 Small is especially valuable for teams that need to analyze relationships across full documents, trace logic across codebases, or connect information across extensive collections. The model is intended to help organizations reduce dependence on complex retrieval workarounds and reason more directly over large-scale data.

UnoRouter serves as a versatile gateway for accessing various OpenAI-compatible language models. With a single API key, users can unleash over 200 models from multiple providers including OpenAI, Anthropic, Google, and others, seamlessly integrating coding agents like Claude Code, Cline, Codex, and Kilo Code. By simply directing any OpenAI SDK to the designated base URL, users can effortlessly switch between models without needing to modify their existing code. Additionally, UnoRouter features an integrated chat and character client, which supports personas, lorebooks, and the import of SillyTavern cards, all accessible with the same API key. The platform operates on a usage-based pricing model that includes a free tier, ensuring users have access to live updates on model availability and pricing. This innovative approach simplifies the process of utilizing multiple AI models for various applications.

Constellation Gate AI serves as an auxiliary defense mechanism for AI agents, positioned strategically between the agent and the model to filter all requests for potential threats and data leaks. This solution functions as an inline gateway for coding agents and model APIs, ensuring protection of workflows while eliminating the need for significant code modifications. Users can direct existing tools such as Claude Code, Cursor, OpenClaw, Codex, or OpenCode to utilize Gate, thereby gaining access to defenses against prompt injection, secret detection, PII redaction, token optimization, and a reliable audit trail. The platform specifically addresses three critical vulnerabilities: prompt injection attacks, leakage of credentials and PII, and unauthorized tool calls. Rather than depending on the model's self-defense mechanisms, Gate preemptively intercepts attacks before they penetrate the model, removes sensitive information prior to the return of responses, and prevents outputs from compromised tools before an agent can act on them. Gate is compatible with the existing calls made by agents, relaying them to the model while meticulously scanning each request and response in both directions, ensuring comprehensive protection against emerging threats. This proactive approach not only enhances security but also instills confidence in users about the integrity and safety of their AI workflows.

The Parity Layer serves as a straightforward addition to the SDKs provided by OpenAI, Anthropic, and Google. This innovative layer enhances a more cost-effective model to either match or exceed the performance of your existing model on the production prompts you use, ensuring validation before any transition occurs, and allows for immediate reversion to your original model if there are any quality concerns. Teams can achieve a reduction of 30-60% in AI API expenses without sacrificing quality. Users can obtain the first proof in just one day, and up to ten prompts can be tested for free without the need for a credit card. It is important to note that this solution is not designed for use with coding agents and focuses primarily on optimizing existing models.

Pioneer serves as an inference API designed for developers who prioritize deployment over managing a GPU cluster. This tool allows teams to connect an existing client, such as OpenAI or Anthropic, to Pioneer, enabling them to maintain their API and code while performing inference seamlessly, all while Pioneer identifies areas where the current model may be lacking. It intelligently groups production traffic based on use cases, highlights opportunities for enhancement in accuracy, latency, or cost, and automatically creates and directs requests to specialized models. Through its continuous improvement mechanism known as Adaptive Inference, Pioneer analyzes real-time production failures to extract valuable examples, retrains a tailored model, assesses the updated checkpoint, and implements enhancements without necessitating any redeployment, all while maintaining access through the same endpoint. Additionally, Pioneer accommodates encoder models for tasks that require structured extraction, including named entity recognition, text classification, structured JSON extraction, privacy filtering, and safety classification, as well as decoder models that facilitate text generation, classification, and open-ended prompting. As a result, developers can optimize their workflows and enhance model performance with minimal hassle.

Concentrate AI serves as a centralized gateway for rapidly evolving teams, offering a single API that connects to all major LLM providers while consolidating routing, spending, logging, and controls. This platform empowers teams to securely leverage and manage artificial intelligence through a unified API, ensuring that each request is directed towards the most efficient, cost-effective, and high-performing model for specific tasks or workflows. With access to over 130 models, teams can evaluate speed, quality, and expense, seamlessly directing workloads to the most suitable options without having to integrate multiple provider APIs into their environments. Concentrate recognizes that different applications such as support bots, coding agents, internal tools, chat functions, and batch jobs have varying needs, allowing teams to choose model slugs, restrict authorized providers, prioritize based on real-time latency, and implement fallback strategies to redirect traffic when a provider encounters slowdowns, errors, or limitations. Additionally, it offers a comprehensive view of AI utilization for engineering, finance, security, and leadership teams, featuring detailed logs at the request level that include models used, provider information, duration, token usage, expenditure, error rates, alerts, and data export capabilities, thereby enhancing oversight and decision-making in AI deployment. This level of transparency and control allows organizations to optimize their AI strategies effectively.

Condense.chat is an innovative API designed for compressing input for language models, functioning as a drop-in proxy that effectively reduces the size of prompts, retrieved documents, tool outputs, and recurring agent contexts prior to reaching the main models. By minimizing context while maintaining the integrity of Claude Code, it intercepts an agent's expanding session history and processes it through compression models, enabling long-running coding agents to operate with fewer tokens at the start of each new turn. Acting as an intermediary between applications and upstream LLM providers, Condense meticulously tracks conversations as a content-addressed chain, seamlessly compressing any repeated context along the way. Developers can easily integrate this system by directing their SDK to the Condense provider route, adding a Condense key, and retaining their existing provider key without needing to make any additional changes. Compatibly, it supports routes for both Anthropic and OpenAI, and also offers pass-through functionalities for other provider pathways, including model lists and embeddings, ensuring a versatile integration. This makes it an invaluable tool for optimizing interactions with language models while enhancing overall efficiency in processing and managing session data.

GPT-Live represents an advanced iteration of voice models designed to enhance the natural interaction between humans and AI, currently utilized in ChatGPT Voice. This innovative system is engineered to create a conversational experience that closely resembles real dialogue, utilizing a full-duplex architecture that enables simultaneous listening and speaking. Throughout interactions, GPT-Live demonstrates its attentiveness with brief affirmations such as "mhmm" or "yeah," facilitates rapid exchanges, and allows for moments of silence when the user needs time to gather their thoughts. Unlike traditional systems that process each turn sequentially, GPT-Live continuously analyzes incoming audio while producing responses, making real-time decisions about when to speak, listen, pause, or even interject. Furthermore, for inquiries that necessitate web searches, intricate reasoning, or advanced tasks, GPT-Live can seamlessly refer to a more sophisticated model working in the background, retrieving and integrating the results into the ongoing dialogue without disrupting the natural flow of conversation. This capability not only enhances the interaction but also ensures a more engaging and dynamic user experience.

GPT-Live-1 is among the two innovative voice models being introduced to ChatGPT users worldwide, designed to enhance conversational interactions with AI and make them feel more authentic. Utilizing a full-duplex architecture, this model can simultaneously listen and respond, eliminating the need for a rigid turn-taking approach. Throughout dialogues, GPT-Live-1 demonstrates attentiveness by providing brief acknowledgments, facilitating a rapid exchange of ideas, pausing for users to gather their thoughts, or remaining silent when it’s time to listen. It is capable of processing input in real-time while generating responses, allowing it to make quick decisions multiple times each second regarding whether to communicate, keep listening, take a break, interrupt, or use additional tools. Additionally, GPT-Live-1 distinguishes between casual interactions and more complex tasks; when faced with a question that necessitates web searching, reasoning, or advanced capabilities, it can seamlessly pass the task to a more advanced frontier model behind the scenes and present the findings once available. This innovative approach not only enhances user experience but also expands the scope of what can be accomplished during AI conversations.

The GPT-Live-1 mini is one of the two voice models being introduced to ChatGPT users worldwide, aimed at enhancing natural, intelligent, and engaging voice interactions in daily dialogues. Utilizing a full-duplex system similar to GPT-Live, this model can simultaneously listen and speak, eliminating the constraints of traditional turn-taking communication. It is designed to continuously analyze input while producing responses, enabling it to make real-time decisions about when to speak, listen, pause, or even interrupt, allowing for a more dynamic conversational flow. As a result, interactions feel quicker and more fluid, with improved timing and reduced chances of awkward pauses, making conversations feel more seamless. Additionally, GPT-Live-1 mini takes advantage of the updated ChatGPT Voice experience, granting users the ability to interject with questions, request the model to slow its pace, or instruct it to remain silent and listen attentively. This multifaceted approach aims to create a richer and more interactive user experience overall.

Stallari transcends the role of a personal assistant, serving as your reliable Chief of Staff specifically designed for macOS and Apple Silicon. It assigns specialized agents to interact with your existing tools whenever desired—be it on a set schedule, triggered by an event, or instantly upon request—while ensuring that your information and activities remain securely stored and every choice is transparent and auditable. What sets Stallari apart is its functionality without relying on cloud services; its Autonomous Dispatch feature enables agent workflows to operate automatically, allowing your inbox to be managed at 6 a.m. and your daily brief to arrive before you even have your morning coffee, all without the need for keeping a browser tab open. Moreover, Stallari Memory empowers agents to learn progressively from observations, decisions, corrections, and commitments, with the memory securely encrypted on your Mac and exportable in a signed, portable format. Additionally, the Fleet Coordination feature enables users to integrate multiple Macs into a cohesive Fabric, facilitating the distribution of dispatches, reducing redundant tasks, and sharing state seamlessly via the user’s vault. With such a comprehensive suite of features, Stallari not only enhances productivity but also fosters collaboration across devices effortlessly.

Vailor is an entirely AI-driven platform designed for governance, risk management, and compliance in the cybersecurity sector, which consolidates risk evaluations, adherence to regulations, audits, asset management, organizational structures, and oversight of third parties into a single, cohesive source of truth. The organizational component of Vailor models multi-tenant entities, outlining perimeters and assets while incorporating unique configurations, data isolation, and governance tailored to each entity. Business teams can initiate projects using an AI-supported pre-assessment questionnaire that gathers crucial information, conducts an initial evaluation, and directs it through a streamlined validation workflow. When it comes to risk assessments, Vailor provides support at every stage, offering contextual scenario recommendations, a variety of methodologies, and automated generation of deliverables. Additionally, the compliance module aligns organizations with regulatory mandates, continually identifies and monitors gaps, suggests remediation strategies, and automatically produces necessary evidence and documentation. With such comprehensive features, Vailor empowers organizations to enhance their cybersecurity posture effectively.

OpenAI Presence is an enterprise AI agent deployment product designed to help companies put voice and chat agents into real production workflows. The platform is built for high-value use cases where agents need to answer questions, resolve issues, access company systems, follow policies, take approved actions, and escalate to people when required. Presence starts each deployment around a defined job, such as resolving billing problems, supporting insurance claims, handling customer support, assisting outbound sales, or managing internal IT service requests. Companies control what knowledge the agent receives, which systems it can access, what actions it can take, and when human approval or escalation is required. Presence includes policies, standard operating procedures, guardrails, simulations, evaluation tools, approved actions, and escalation rules to help verify accuracy and performance. Before launch, teams can test agents against common requests, edge cases, higher-risk scenarios, and company-specific policies. After launch, production sessions and escalations show where the agent is working well and where it needs improvement. Codex can investigate those signals and propose updates that teams test, compare against production behavior, and approve before rollout. By combining OpenAI models, enterprise deployment support, workflow-specific controls, evaluations, guardrails, and continuous improvement, OpenAI Presence helps organizations build AI agents they can trust in customer and internal operations.

Scribe is an automated knowledge base that operates on a self-hosted platform, utilizing your tools to generate content. It analyzes the Git history, Claude Code and Codex sessions, as well as self-sent URLs and drop files, transforming this information into a well-organized, cross-project wiki formatted in Markdown and stored within Git repositories. By eliminating the need for developers to maintain an additional cognitive framework or to reconstruct context each time an agent session begins anew, Scribe effectively captures critical decisions, fixes, evaluations, and the rationale behind them, enabling agents to reference this accumulated knowledge prior to taking action. Its operational pipeline is scheduled via cron jobs, allowing it to identify projects and sift through low-value data using FTS5 before engaging with an LLM, thus extracting verified facts through structured bounded workflows that run in two passes. The final output is organized into entity-centric pages complete with YAML frontmatter, wikilinks, backlinks, retrieval context, and various typed relationships, including supersedes, contradicts, derived_from, specializes, and extends, ensuring comprehensive documentation and clarity across projects. This systematic approach significantly enhances collaboration and knowledge sharing among teams, streamlining the development process.

Calven is a cutting-edge AI marketing platform designed to empower teams with nine independent agents focused on Research and Intelligence, Target Audience, and Positioning and Messaging. Instead of functioning as isolated tools, these agents collaborate through a unified database known as the Universe, where critical elements such as market signals, customer feedback, competitor insights, win/loss data, ideal customer profiles, personas, positioning, battle cards, and messaging are seamlessly integrated and consistently updated. Four dedicated research agents monitor competitors, markets, customers, and deals in real time; the ICP and Persona agents utilize these insights to create a validated understanding of the target audience; while the Positioning, Messaging, and Product Intelligence agents refine this context into compelling and distinct language. Additionally, Calven interfaces with various platforms such as sales calls, CRM systems, Slack conversations, documents, and more, efficiently processing information that teams typically lack the time to analyze, ensuring that all marketing materials are current and relevant. This holistic approach not only streamlines the marketing process but also enhances strategic decision-making across the organization.

BaseRT offers a robust inference runtime for LLMs specifically optimized for Apple Silicon, allowing developers to seamlessly access models from Hugging Face, engage in local conversations, or utilize an API compatible with OpenAI through a single command-line interface. Enhanced by meticulously crafted Metal kernels, BaseRT aims to provide exceptional prefill and decoding efficiency on M-series Macs, with benchmark results indicating it performs up to 6.4 times faster in prefill tasks compared to llama.cpp, 3.9 times faster than MLX, and achieves a decoding speed that is 1.33 times quicker. The basert CLI is equipped to manage tasks such as model downloading, conversion, interactive chat, serving capabilities, completion generation, benchmarking, inspection, and bundle signing. Its server functionalities are extensive, encompassing chat interactions, text completions, embeddings, transcription services, tool calls, continuous batching, paged key-value caching, and prefix caching, with support for models that can handle text, vision, and audio data. BaseRT employs a proprietary .base model format that incorporates Q2–Q8 affine quantization, optional AWQ calibration, and signed bundles, and it is capable of converting GGUF, Hugging Face, and MLX checkpoints. Furthermore, this innovative runtime is tailored to maximize the capabilities of Apple Silicon, making it an essential tool for developers in the AI space.

Mavvrik operates as a sophisticated platform for managing costs associated with AI and hybrid infrastructure, providing a centralized hub for finance, FinOps, IT, and engineering teams to oversee GenAI, autonomous agents, GPUs, cloud systems, on-premises resources, Kubernetes, data platforms, and SaaS solutions. By consolidating cost, usage, and telemetry data from major providers such as AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, it establishes a comprehensive source of truth for the entire technology ecosystem. Teams can meticulously monitor each model interaction, agent engagement, GPU utilization, and resource workload, allowing for precise spending allocation across various dimensions, including customer, product, feature, project, application, environment, team, or cost center. Through in-depth analysis of cost-to-serve and unit economics, Mavvrik uncovers margin losses, identifies costly workloads, and clarifies the actual expenses involved in delivering each service. Additionally, its capability for real-time anomaly detection and alerts serves to flag unusual usage patterns before they escalate into unexpected budget overruns, while its predictive forecasting tools assist organizations in effectively modeling their cloud, GPU, and AI-related expenditures. This holistic approach empowers teams to make informed financial decisions and optimize resource utilization for sustained growth.

OpenAI Ads Manager serves as a self-service platform that enables users to create, launch, manage, and refine advertising campaigns that are featured within ChatGPT. This platform is tailored to assist businesses in reaching potential customers as they navigate various choices, assess different options, consider tradeoffs, and ultimately make informed decisions, utilizing the deeper context provided by conversations instead of relying solely on keyword matching. Advertisers have the ability to establish an account, define campaign objectives and budgets, create ad groups and advertisements, or import campaign data in bulk through CSV files for convenience. Once campaigns are active, Ads Manager offers performance analytics, allowing teams to track outcomes, modify campaigns, and enhance delivery from a single interface. ChatGPT Ads leverage conversational intent, and when ad personalization is activated, they can use signals from a user's broader interactions within ChatGPT to deliver more relevant and beneficial placements. Additionally, the platform accommodates cost-per-click bidding and offers extensive measurement tools, thereby granting advertisers greater flexibility in how they purchase and assess their campaigns. By providing these features, OpenAI Ads Manager empowers businesses to engage effectively with their target audience in a meaningful way.

GPT-5.6-Cyber is a cybersecurity-focused OpenAI model introduced for trusted defenders through Daybreak Red access. Built on GPT-5.6 Sol, the model is trained to improve performance on advanced security research, vulnerability discovery, exploit validation, and specialized defensive workflows. GPT-5.6-Cyber is intended for authorized users who need deeper support across vulnerability research, security testing, exploit-chain analysis, incident response, malware analysis, secure code review, patch validation, and technical documentation. OpenAI created Daybreak to give approved individuals and organizations access to frontier cyber capabilities while managing the risks of reduced safeguards. Daybreak Blue provides access to frontier general-purpose models with safeguards tailored for defensive security work, while Daybreak Red provides access to cybersecurity-specific models such as GPT-5.6-Cyber. The model is designed to reduce unnecessary refusals that can interrupt legitimate security research while still operating inside a trusted access program. OpenAI reports that GPT-5.6-Cyber improves performance on certain specialized cybersecurity evaluations and has been used to support real-world vulnerability research and coordinated disclosure. Access controls include identity verification, account security, monitoring, legal attestations, approved-use restrictions, and additional requirements such as hardware security keys for individual Daybreak accounts. By combining cyber-specific training, trusted access controls, reduced refusal behavior, vulnerability research capabilities, and safety practices, GPT-5.6-Cyber helps approved defenders conduct advanced security work more effectively.