On-Premises Business Software

DeepSeek-V3.2-Speciale is the most advanced reasoning-focused version of the DeepSeek-V3.2 family, designed to excel in mathematical, algorithmic, and logic-intensive tasks. It incorporates DeepSeek Sparse Attention (DSA), an efficient attention mechanism tailored for very long contexts, enabling scalable reasoning with minimal compute costs. The model undergoes a robust reinforcement learning pipeline that scales post-training compute to frontier levels, enabling performance that exceeds GPT-5 on internal evaluations. Its achievements include gold-medal-level solutions in IMO 2025, IOI 2025, ICPC World Finals, and CMO 2025, with final submissions publicly released for verification. Unlike the standard V3.2 model, the Speciale variant removes tool-calling capabilities to maximize focused reasoning output without external interactions. DeepSeek-V3.2-Speciale uses a revised chat template with explicit thinking blocks and system-level reasoning formatting. The repository includes encoding tools showing how to convert OpenAI-style chat messages into DeepSeek’s specialized input format. With its MIT license and 685B-parameter architecture, DeepSeek-V3.2-Speciale offers cutting-edge performance for academic research, competitive programming, and enterprise-level reasoning applications.

OpenAGI provides a modern framework for building intelligent agents that behave more like autonomous digital workers rather than simple prompt-driven LLM tools. Unlike standard AI apps that only retrieve or summarize information, OpenAGI agents can plan ahead, make decisions, reflect on their work, and perform actions independently. The system is built to support specialized agent development across domains ranging from personalized education to automated financial analysis, medical assistance, and software engineering. Its architecture is intentionally flexible, enabling developers to orchestrate multi-agent collaboration in sequential, parallel, or adaptive workflows. OpenAGI also introduces streamlined configuration processes to eliminate infinite loops and design bottlenecks commonly seen in other agent frameworks. Both auto-generated and fully manual configuration options are available, giving developers the freedom to build quickly or fine-tune every detail. As the platform evolves, OpenAGI aims to support deeper memory, improved planning skills, and stronger self-improvement abilities in agents. The vision is to empower developers everywhere to create agents that learn continuously and handle increasingly complex real-world tasks.

Lux introduces a breakthrough approach to AI by enabling models to control computers the same way humans do, interacting with interfaces visually and functionally rather than through traditional API calls. Through its three distinct modes—Tasker for procedural workflows, Actor for ultra-fast execution, and Thinker for complex problem-solving—developers can tailor how agents behave in different environments. Lux demonstrates its power through practical examples such as autonomous Amazon product scraping, automated software QA using Nuclear, and rapid financial data retrieval from Nasdaq. The platform is designed so developers can spin up real computer-use agents within minutes, supported by robust SDKs and pre-built templates. Its flexible architecture allows agents to understand ambiguous goals, strategize over long timelines, and complete multi-step tasks without manual intervention. This shift expands AI’s capabilities beyond reasoning into hands-on action, enabling automation across any digital interface. What was once a capability reserved for large tech labs is now accessible to any developer or team. Lux ultimately transforms AI from a passive assistant into an active operator capable of working directly inside software.

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.

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.

DeepCoder, an entirely open-source model for code reasoning and generation, has been developed through a partnership between Agentica Project and Together AI. Leveraging the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has undergone fine-tuning via distributed reinforcement learning, achieving a notable accuracy of 60.6% on LiveCodeBench, which marks an 8% enhancement over its predecessor. This level of performance rivals that of proprietary models like o3-mini (2025-01-031 Low) and o1, all while operating with only 14 billion parameters. The training process spanned 2.5 weeks on 32 H100 GPUs, utilizing a carefully curated dataset of approximately 24,000 coding challenges sourced from validated platforms, including TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each problem mandated a legitimate solution along with a minimum of five unit tests to guarantee reliability during reinforcement learning training. Furthermore, to effectively manage long-range context, DeepCoder incorporates strategies such as iterative context lengthening and overlong filtering, ensuring it remains adept at handling complex coding tasks. This innovative approach allows DeepCoder to maintain high standards of accuracy and reliability in its code generation capabilities.

DeepSWE is an innovative and fully open-source coding agent that utilizes the Qwen3-32B foundation model, trained solely through reinforcement learning (RL) without any supervised fine-tuning or reliance on proprietary model distillation. Created with rLLM, which is Agentica’s open-source RL framework for language-based agents, DeepSWE operates as a functional agent within a simulated development environment facilitated by the R2E-Gym framework. This allows it to leverage a variety of tools, including a file editor, search capabilities, shell execution, and submission features, enabling the agent to efficiently navigate codebases, modify multiple files, compile code, run tests, and iteratively create patches or complete complex engineering tasks. Beyond simple code generation, DeepSWE showcases advanced emergent behaviors; when faced with bugs or new feature requests, it thoughtfully reasons through edge cases, searches for existing tests within the codebase, suggests patches, develops additional tests to prevent regressions, and adapts its cognitive approach based on the task at hand. This flexibility and capability make DeepSWE a powerful tool in the realm of software development.

DeepScaleR is a sophisticated language model comprising 1.5 billion parameters, refined from DeepSeek-R1-Distilled-Qwen-1.5B through the use of distributed reinforcement learning combined with an innovative strategy that incrementally expands its context window from 8,000 to 24,000 tokens during the training process. This model was developed using approximately 40,000 meticulously selected mathematical problems sourced from high-level competition datasets, including AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. Achieving an impressive 43.1% accuracy on the AIME 2024 exam, DeepScaleR demonstrates a significant enhancement of around 14.3 percentage points compared to its base model, and it even outperforms the proprietary O1-Preview model, which is considerably larger. Additionally, it excels on a variety of mathematical benchmarks such as MATH-500, AMC 2023, Minerva Math, and OlympiadBench, indicating that smaller, optimized models fine-tuned with reinforcement learning can rival or surpass the capabilities of larger models in complex reasoning tasks. This advancement underscores the potential of efficient modeling approaches in the realm of mathematical problem-solving.

The GLM-4.6V is an advanced, open-source multimodal vision-language model that belongs to the Z.ai (GLM-V) family, specifically engineered for tasks involving reasoning, perception, and action. It is available in two configurations: a comprehensive version with 106 billion parameters suitable for cloud environments or high-performance computing clusters, and a streamlined “Flash” variant featuring 9 billion parameters, which is tailored for local implementation or scenarios requiring low latency. With a remarkable native context window that accommodates up to 128,000 tokens during its training phase, GLM-4.6V can effectively manage extensive documents or multimodal data inputs. One of its standout features is the built-in Function Calling capability, allowing the model to accept various forms of visual media — such as images, screenshots, and documents — as inputs directly, eliminating the need for manual text conversion. This functionality not only facilitates reasoning about the visual content but also enables the model to initiate tool calls, effectively merging visual perception with actionable results. The versatility of GLM-4.6V opens the door to a wide array of applications, including the generation of interleaved image-and-text content, which can seamlessly integrate document comprehension with text summarization or the creation of responses that include image annotations, thereby greatly enhancing user interaction and output quality.

GLM-4.1V is an advanced vision-language model that offers a robust and streamlined multimodal capability for reasoning and understanding across various forms of media, including images, text, and documents. The 9-billion-parameter version, known as GLM-4.1V-9B-Thinking, is developed on the foundation of GLM-4-9B and has been improved through a unique training approach that employs Reinforcement Learning with Curriculum Sampling (RLCS). This model accommodates a context window of 64k tokens and can process high-resolution inputs, supporting images up to 4K resolution with any aspect ratio, which allows it to tackle intricate tasks such as optical character recognition, image captioning, chart and document parsing, video analysis, scene comprehension, and GUI-agent workflows, including the interpretation of screenshots and recognition of UI elements. In benchmark tests conducted at the 10 B-parameter scale, GLM-4.1V-9B-Thinking demonstrated exceptional capabilities, achieving the highest performance on 23 out of 28 evaluated tasks. Its advancements signify a substantial leap forward in the integration of visual and textual data, setting a new standard for multimodal models in various applications.

GLM-4.5V-Flash is a vision-language model that is open source and specifically crafted to integrate robust multimodal functionalities into a compact and easily deployable framework. It accommodates various types of inputs including images, videos, documents, and graphical user interfaces, facilitating a range of tasks such as understanding scenes, parsing charts and documents, reading screens, and analyzing multiple images. In contrast to its larger counterparts, GLM-4.5V-Flash maintains a smaller footprint while still embodying essential visual language model features such as visual reasoning, video comprehension, handling GUI tasks, and parsing complex documents. This model can be utilized within “GUI agent” workflows, allowing it to interpret screenshots or desktop captures, identify icons or UI components, and assist with both automated desktop and web tasks. While it may not achieve the performance enhancements seen in the largest models, GLM-4.5V-Flash is highly adaptable for practical multimodal applications where efficiency, reduced resource requirements, and extensive modality support are key considerations. Its design ensures that users can harness powerful functionalities without sacrificing speed or accessibility.

GLM-4.5V is an evolution of the GLM-4.5-Air model, incorporating a Mixture-of-Experts (MoE) framework that boasts a remarkable total of 106 billion parameters, with 12 billion specifically dedicated to activation. This model stands out by delivering top-tier performance among open-source vision-language models (VLMs) of comparable scale, demonstrating exceptional capabilities across 42 public benchmarks in diverse contexts such as images, videos, documents, and GUI interactions. It offers an extensive array of multimodal functionalities, encompassing image reasoning tasks like scene understanding, spatial recognition, and multi-image analysis, alongside video comprehension tasks that include segmentation and event recognition. Furthermore, it excels in parsing complex charts and lengthy documents, facilitating GUI-agent workflows through tasks like screen reading and desktop automation, while also providing accurate visual grounding by locating objects and generating bounding boxes. Additionally, the introduction of a "Thinking Mode" switch enhances user experience by allowing the selection of either rapid responses or more thoughtful reasoning based on the situation at hand. This innovative feature makes GLM-4.5V not only versatile but also adaptable to various user needs.

Autoreach transcends the limits of a conventional scheduler or a mass messaging tool; it is the pioneering AI Sales Agent that fully automates B2B lead generation on X, effectively eliminating the need for traditional SDR tasks. Historically, the drive to expand outreach often compromised quality for sheer volume, leading to spammy interactions. Autoreach addresses this issue by employing conversational intelligence at scale, providing founders and sales teams with a consistent influx of qualified meetings. Distinctive Features Ensuring a Reliable Pipeline: AI Conversation Engine: Our innovative system moves beyond static templates, facilitating dynamic and context-sensitive dialogues. It adeptly manages common objections, responds to inquiries, and qualifies intent in a manner akin to a skilled SDR. Moreover, the system continually learns and adapts, ensuring that conversations remain relevant and effective. 24/7 Zero-Friction Booking: The AI expertly qualifies interested prospects and seamlessly integrates with your calendar for immediate meeting bookings, thereby removing all manual follow-up and minimizing lead attrition. This ensures that no potential opportunity is lost in the process.

Daten & Wissen, recognized by DPIIT and a partner of NVIDIA Inception, has developed NWarch AI, an innovative platform focused on edge-first video analytics and automation that transforms current CCTV and sensor feeds into immediate insights related to safety, crowd management, and operational effectiveness. Our solution addresses the challenges of disjointed video data, the inefficiencies of slow manual oversight, and the expenses tied to replacing existing systems by offering easy-to-integrate edge inference, AI-driven natural language agents for instant inquiries, and automation workflows that require no coding. NWarch AI caters to various sectors including construction, manufacturing, logistics, retail, and security, facilitating quicker incident responses, streamlining compliance reporting, and achieving significant efficiency improvements. By leveraging our technology, businesses can enhance their operational capabilities and make data-driven decisions more effectively.

Vitruve serves as a comprehensive platform for strength and conditioning, designed to aid in the planning, execution, and analysis of workouts, whether or not velocity-based training (VBT) is utilized, by integrating a workout builder, athlete monitoring, and performance analytics all within a single system compatible with iOS, Android, and the web. By simplifying the intricacies of coaching, it offers features such as drag-and-drop program design, reusable templates, and automated tracking, ensuring that all athlete information is centralized while delivering intelligent insights and reports that facilitate informed coaching decisions. The VBT capabilities of Vitruve enable precise tracking of bar speed, automatic load adjustments, and immediate feedback for each repetition, allowing users to assess real-time performance, monitor fatigue and readiness levels, and refine workouts based on data-driven insights. It is tailored for both team and individual management, featuring detailed performance dashboards, leaderboards, advanced metrics like peak velocity and estimated one-rep max (1RM), and effortless syncing across various devices. Additionally, the platform fosters a collaborative environment where coaches and athletes can engage meaningfully with their progress, making it an invaluable tool for enhancing athletic performance.

Match Data Pro is a sophisticated tool for managing data quality that aims to integrate, cleanse, analyze, match, eliminate duplicates, and consolidate records from various files, databases, and systems with remarkable efficiency and accuracy. It features cutting-edge AI-enabled fuzzy matching and adjustable rule-based logic to identify duplicates and inconsistencies within extensive datasets, assisting users in correcting errors, standardizing formats, and generating trustworthy golden records without the need for coding expertise. The tool also offers extensive data profiling with essential metrics to identify quality concerns prior to processing, robust data cleansing functionalities for normalizing and standardizing information, along with address verification features that enhance accuracy. Furthermore, Match Data Pro is equipped with Senzing AI entity resolution and customizable matching algorithms to accommodate minor data variations, ensuring high-performance processing capable of scaling up to millions of records. Additionally, it facilitates project job automation through scheduling, reusable rules, and seamless API integrations, making it a comprehensive solution for effective data management.

Novproxy stands out as the most affordable proxy service platform globally, offering access to over 100 million authentic IP addresses spanning more than 195 countries and regions. It excels in providing city-level precise positioning and can effortlessly navigate around regional access barriers. The platform boasts a comprehensive suite of proxy services, which includes dynamic residential IPs, static IPs, unlimited data traffic, and SOCKS5 proxies, while ensuring compatibility with the three primary protocols: HTTP, HTTPS, and SOCKS5. Key features of Novproxy include an impressive 99.9% uptime, a customizable IP rotation feature that can be adjusted between 1 and 120 minutes, and a seamless, highly anonymous browsing experience designed to effectively avoid anti-crawling measures and the risks associated with IP blocking. Additionally, with attractively priced plans, options for free trials, and reliable technical support, Novproxy has positioned itself as the preferred economical choice for both individual developers and enterprise clients alike. As a result, it has gained significant popularity among users seeking a robust and efficient proxy solution.

OpenOps is an open-source cloud automation solution designed to modernize FinOps operations with flexibility and control. It bridges the gap between buying limited automation tools and building costly in-house systems. Organizations can start quickly using ready-made workflow templates tailored to major cloud spend areas. These workflows can be fully customized or built from scratch using a no-code interface. OpenOps connects directly to cloud environments to identify cost-saving opportunities and operational risks. Human approvals and governance checks are built into every automation. The platform supports anomaly detection, allocation, forecasting, and workload optimization across multiple clouds. OpenOps integrates with dozens of tools across DevOps, analytics, ticketing, and AI ecosystems. It minimizes required permissions and avoids deploying intrusive agents. This approach delivers scalable automation without sacrificing security or control.

Qatium serves as an open and collaborative tool for water management, providing utilities of all sizes with a comprehensive, interactive, and real-time digital representation of their water systems, allowing operators and planners to efficiently manage, monitor, and optimize their networks in a secure digital framework. Users can create a virtual model of their network using GIS and other data sources, link real-time data feeds such as SCADA or metering, visualize the entire system, detect irregularities, and simulate different scenarios prior to real-world implementation, which aids in reducing leaks and losses while enhancing resilience and service quality. The platform is equipped with functionalities for visualizing networks, alerting users to potential problems, conducting scenario and simulation assessments, and offering a digital water assistant to provide guidance, along with the capability for extensions and integrations through a marketplace and developer tools. Qatium prioritizes a design that is user-friendly, secure, and scalable, empowering utilities to optimize their operations, improve decision-making processes, and foster digital innovation. Ultimately, this tool not only enhances operational efficiency but also contributes to more sustainable water management practices.

FlowFuse is an advanced industrial application software that leverages Node-RED to enable teams to seamlessly integrate machines and protocols, gather and model data, and manage applications on a large scale, all while incorporating AI-driven support to streamline both development and deployment processes. By enhancing the user-friendly low-code, visual programming capabilities of Node-RED, FlowFuse introduces enterprise-level functionalities such as secure device communication, comprehensive operational management, centralized remote deployment options, collaborative team features, and extensive security measures. The solution also boasts interactive and adaptive dashboards, AI-supported flow creation and improvement aids, and tools for converting unprocessed data into structured models using natural language inputs. Furthermore, it incorporates DevOps-style pipelines for effective management of staged environments and version control, allows for remote fleet management via a device agent, and provides sophisticated observability features to ensure performance monitoring across multiple instances. This combination of capabilities positions FlowFuse as a powerful tool for optimizing industrial operations and accelerating innovation.

amazee.ai is an AI solution designed with security and privacy at its core, empowering organizations to retain full command over their data, models, and compliance obligations while seamlessly integrating AI into their existing frameworks without risking the exposure of sensitive information. Emphasizing a privacy-centric approach and focusing on data sovereignty, this tool guarantees that AI operations and data are confined to a designated region, thereby satisfying regulatory standards such as GDPR, HIPAA, and CCPA. It operates within a certified and compliant setting that prevents vendor lock-in by relying on transparent, open-source principles and adaptable infrastructure. Additionally, its Private AI Assistant serves as a robust, region-specific chat and generative tool tailored for enterprise use, ensuring that prompts and outputs are never logged and facilitating secure processing and analysis of internal documents, CRM records, and support ticket information. This leads to valuable insights, summaries, reports, and automated replies, all while maintaining the organization's control over its data. Furthermore, the platform offers a variety of deployment options to accommodate diverse organizational needs.

Mesa is an innovative platform that leverages artificial intelligence to enhance code review processes, enabling engineering teams to elevate software quality and confidently deploy code by addressing technical debt before it impacts production. The platform's smart agents are capable of understanding the distinct elements of a team's codebase, business logic, and development standards, allowing them to provide reviews that are contextual and precise, surpassing mere linting or generic suggestions from AI. Users have the flexibility to develop custom review agents that focus on specific issues such as security vulnerabilities, performance optimization, and domain-specific logic, while also selecting from a diverse range of foundational models from notable providers like OpenAI, Anthropic, and Google, which can be optimized for various metrics such as speed, cost-efficiency, or intelligence level. Additionally, Mesa produces comprehensive and consistent descriptions for pull requests utilizing team-defined templates, seamlessly integrating into existing CI/CD workflows, and adjusting to different branching strategies to ensure that quality checks are an integral part of daily development activities. This adaptability not only streamlines the review process but also empowers teams to maintain high standards throughout their software development lifecycle.

Dafthunk is an innovative platform designed for visual workflow automation, allowing users to create, manage, and implement serverless automation workflows effortlessly with a user-friendly drag-and-drop interface, eliminating the need for any infrastructure setup or container usage. The platform enables users to build workflows by visually linking nodes that execute various tasks involving AI, browser automation, data manipulation, media creation, integrations, and development tools, which are then processed on Cloudflare’s extensive global edge network, ensuring seamless scaling and reliable execution. It features a variety of workflow triggers, such as HTTP webhooks, queues, schedules based on cron, and options for manual initiation, facilitating automation that is responsive to events, time-sensitive, or initiated by users. The platform also offers persistent storage for workflow states and execution logs through Cloudflare's D1 and R2 storage services, ensuring data integrity and accessibility. Users can enhance their workflows by integrating AI models from well-known providers like OpenAI, Anthropic, Google, and Cloudflare AI, enabling capabilities in text generation, summarization, vision processing, natural language processing, transcription, image generation, and more. This comprehensive approach empowers users to streamline their processes and harness the full potential of automation technology.

Ease Commerce is India’s leading e-commerce CRM software built to streamline and unify end-to-end commerce operations. It brings together order management, returns, warehouse operations, inventory tracking, payments, and customer support in one centralized platform. Designed for multi-channel sellers, Ease Commerce integrates seamlessly with marketplaces, payment gateways, ERP systems, and social commerce platforms. Smart automation handles repetitive tasks such as order processing, inventory updates, and reconciliation, saving time and reducing operational friction. The platform provides real-time analytics and reports to help teams monitor KPIs and make data-driven decisions. Integrated task management keeps teams aligned and productive across departments. Ease Commerce also enables faster, more consistent customer support through unified communication channels. With flexible modules and deep integrations, it adapts to businesses at every growth stage. Ease Commerce empowers sellers to focus on scaling rather than managing operational chaos. It delivers a smarter, more efficient way to run modern e-commerce operations.

iCloudEMS is an innovative, cloud-based ERP solution designed specifically for higher education entities, including private universities, deemed universities, and large colleges. This platform oversees the entire academic and administrative process through a cohesive system that encompasses admissions, student data management, academic activities, examinations, learning management systems, financial operations, human resources, payroll, accreditation, library services, housing, transportation, career services, and mobile applications. Hosted on the secure AWS cloud framework, iCloudEMS provides institutions with the scalability, reliability, and high-level data protection necessary for managing large student populations. With its AI-enhanced automation, the platform offers features such as real-time notifications, insights into attendance and fee structures, performance evaluation, grievance resolution, and data-driven recommendations for leadership teams. iCloudEMS is crafted to support administrators, faculty, students, and management alike, facilitating streamlined operations, enhancing transparency, minimizing manual tasks, and establishing a robust digital foundation for progressive educational environments. By integrating all these functionalities, iCloudEMS ensures that institutions are well-equipped to meet the demands of modern education.