On-Premises Business Software

SmartClient is an enterprise-grade framework for building high-performance, data-driven web applications. It provides a comprehensive set of customizable UI components, advanced data management, automatic data binding, and client-side caching. Developers can integrate SmartClient applications with any back-end technology, including Java, .NET, and PHP, allowing teams to build complex enterprise systems that fit seamlessly into existing infrastructure. Built on a mature and extensible architecture, SmartClient simplifies development without limiting control. Developers can rapidly assemble responsive, high-productivity interfaces while maintaining access to low-level APIs and advanced configuration options. Features like automatic data synchronization, offline support, and modular loading ensure scalability and performance across large, distributed deployments. SmartClient is also AI native, integrating artificial intelligence across the stack to accelerate development and deliver intelligent user experiences. Reify AI enables teams to generate complete applications from natural language prompts, while Instant UI can turn APIs or databases into fully functional interfaces automatically. The Answer Engine allows users to query data conversationally and see instant dashboards, and AI Report Builder creates shareable, production-ready reports in seconds. AI-enhanced components support meaning-based filtering, data summarization, and contextual insights—all with the same level of reliability and security that SmartClient is known for. With its combination of declarative power, deep customization, and AI-enabled productivity, SmartClient gives developers a unified environment for building modern, intelligent enterprise applications at scale.

Mistral AI Studio serves as a comprehensive platform for organizations and development teams to create, tailor, deploy, and oversee sophisticated AI agents, models, and workflows, guiding them from initial concepts to full-scale production. This platform includes a variety of reusable components such as agents, tools, connectors, guardrails, datasets, workflows, and evaluation mechanisms, all enhanced by observability and telemetry features that allow users to monitor agent performance, identify root causes, and ensure transparency in AI operations. With capabilities like Agent Runtime for facilitating the repetition and sharing of multi-step AI behaviors, AI Registry for organizing and managing model assets, and Data & Tool Connections that ensure smooth integration with existing enterprise systems, Mistral AI Studio accommodates a wide range of tasks, from refining open-source models to integrating them seamlessly into infrastructure and deploying robust AI solutions at an enterprise level. Furthermore, the platform's modular design promotes flexibility, enabling teams to adapt and scale their AI initiatives as needed.

Ekinox serves as a visual AI automation platform that allows users to create, implement, and oversee AI-driven workflows without the need for coding; its user-friendly drag-and-drop interface facilitates the design of intelligent agents that can link to over 100 pre-existing integrations, triggering actions across numerous productivity, data, and communication applications. The platform is designed for real-time processing and encourages collaboration by offering team workspaces, version control, and immediate deployment capabilities. In addition, it boasts enterprise-level security that adheres to SOC 2 standards, features bank-level encryption, supports custom API connectors, and includes sophisticated access controls. Users benefit from the ability to monitor their workflows through comprehensive analytics dashboards, enabling them to assess costs and performance across various models and integrations while utilizing predictive auto-scaling and log retention for enhanced functionality. With setup times cut down to mere minutes, Ekinox optimizes processes ranging from straightforward task automation to more complex workflows, making it an invaluable tool. This efficiency not only improves productivity but also enhances the overall user experience.

Webix Grid operates as an independent JavaScript DataGrid component, serving as a versatile table/grid UI widget that excels in high-performance environments, particularly when dealing with extensive datasets, and is specifically crafted for seamless integration into web applications requiring the display, editing, filtering, and sorting of tabular data. Notable advantages include its lightweight nature, allowing users to utilize only the grid without needing the entire Webix UI library; its emphasis on catering to the "just the grid" scenario rather than a comprehensive UI framework; and its abundance of features, providing an extensive array of functionalities suitable for enterprise-grade data applications. Furthermore, its design ensures that developers can implement it effortlessly while maintaining optimal performance.

Vectense is a comprehensive AI workflow platform that empowers companies to develop automations without any coding requirements. Its Story Editor feature allows users to outline workflows using natural language, and the platform takes care of constructing them automatically. With compatibility for various AI models such as OpenAI, Anthropic, Mistral, and local solutions, users can easily switch between providers without needing to reconstruct their workflows. Developed in Germany, Vectense adheres to GDPR standards and provides options for German cloud hosting or complete on-premise deployment. The platform seamlessly integrates with Outlook, Gmail, and CRM/ERP systems through native connectors and REST APIs, enhancing connectivity across tools. Additionally, it offers functionalities like inline testing, version control, and clear analytics to ensure robust performance. Vectense is tailored specifically for small to medium-sized businesses with employee counts ranging from 20 to 500, making it an ideal solution for organizations looking to streamline their operations. This focus on SMBs allows Vectense to address the unique challenges and needs of this segment effectively.

DicksonOne provides the capability for remote and immediate surveillance of temperature, humidity, and differential pressure across various locations and devices; it integrates information from Dickson's sensors, loggers, and chart-recorders into a centralized dashboard that is accessible from any device, featuring alert notifications, data visualizations, and scalable implementation suitable for regulated settings. On the other hand, DicksonWare caters to PC users who need to manage individual USB loggers that involve local data transfers, along with graph and table visualizations, and compliance features that adhere to 21 CFR Part 11 for industries under regulation. Both solutions offered by Dickson are tailored for highly regulated applications, including pharmaceuticals, cold-chain logistics, labs, and manufacturing, by providing customizable monitoring, secure data management, device integration, and various deployment options that can accommodate both remote and on-site environments. Furthermore, the versatility of these systems ensures they can adapt to the specific needs of different sectors, enhancing operational efficiency and regulatory compliance across the board.

The PICO MES platform empowers manufacturers to modernize and enhance their production processes by consolidating personnel, equipment, tools, and data into a singular cohesive system. This innovative solution provides digital work instructions that include detailed step-by-step guidance and engaging visual aids, along with real-time insights into throughput, yield, and operator efficiency, ensuring comprehensive traceability of every part's manufacturing history. It seamlessly integrates with various tools, including barcode scanners, torque tools, PLCs, vision systems, and ERP/PLM/QMS platforms, among others. Remarkably, PICO can be deployed in just minutes instead of months, connecting effortlessly with existing machinery and business applications without necessitating any major overhauls. This capability makes it suitable for manufacturers of all scales, from small enterprises moving away from paper-based processes to larger corporations optimizing operations across multiple sites. Moreover, its plug-and-play compatibility with over 200 devices and its modular design facilitate the enforcement of standardized work processes in environments characterized by high variability and low volume, allowing for automatic data collection and proactive responses to shop floor challenges as they arise. Ultimately, the PICO MES platform serves as a critical tool for manufacturers aiming to increase efficiency and streamline their operations.

The Tosi Platform is an innovative Cyber-Physical Systems solution specifically crafted to safeguard, link, and manage Operational Technology (OT) networks as well as essential infrastructure. In contrast to conventional IT tools that have been modified for OT usage, Tosi is built from the ground up to cater to industrial settings, offering support for native industrial protocols and resilience against extreme temperature variations, all while eliminating the need for complex configurations. Its deployment process is exceptionally swift, with sites becoming operational in less than five minutes through a straightforward “plug-and-go” approach, which empowers organizations to securely and efficiently connect their distributed infrastructures without the necessity for specialized IT knowledge. The platform employs a robust zero-trust security framework that includes enterprise-level protections such as end-to-end 256-bit AES encryption, hardware-based authentication using RSA keys, the absence of open inbound ports, and compliance with ISO/IEC 27001:2022 standards. Additionally, Tosi provides an integrated management experience through a single interface known as TosiControl, which allows users to visualize network topology for better oversight and control, enhancing operational efficiency and security across the board. This comprehensive design not only streamlines management but also strengthens the overall security posture of industrial environments.

SurePassID is a sophisticated multi-factor authentication solution that can be deployed in various environments to protect both information technology and operational technology sectors, encompassing essential infrastructure, older systems, on-site operations, air-gapped setups, hybrid clouds, and fully cloud-based systems. The platform accommodates numerous authentication strategies, including passwordless and phishing-resistant methods like FIDO2/WebAuthn (utilizing FIDO2 PINs, biometrics, or push notifications), alongside one-time passwords (OTP through OATH HOTP/TOTP), mobile push notifications, SMS, voice calls, and conventional techniques. SurePassID seamlessly integrates with popular operating systems, facilitating domain and local logins, RDP/SSH remote access, and even older or embedded Windows systems typically found in OT/ICS/SCADA settings, thus allowing for offline two-factor authentication when necessary. Additionally, it offers protection for VPNs, network devices, appliances, legacy applications, and web applications through SAML 2.0 or OIDC identity provider capabilities, as well as access protocols for network devices. This flexibility makes SurePassID an essential tool for organizations aiming to enhance their security posture in diverse operational landscapes.

Cospace serves as a comprehensive private digital workspace that consolidates documents, tasks, communication, and data analysis into one cohesive platform. It enables users to efficiently store and manage files with real-time editing capabilities and metadata functionalities, while also allowing project management through customizable workspaces featuring both Kanban and table views. Users can generate tasks with personalized fields and monitor their progress using intuitive workflow tools. Additionally, Cospace incorporates built-in chat and video calling options, facilitating seamless team communication that is directly linked to relevant tasks or documents for enhanced context. On the data front, Cospace supports the integration of various sources, including APIs, databases, spreadsheets, and cloud storage, which can be cleaned, structured, transformed, and visualized using real-time dashboards. Furthermore, it automates workflows, generates reports, and provides predictive analytics, empowering teams to identify trends, streamline processes, and make informed, data-driven decisions while enhancing overall productivity. In this way, Cospace not only simplifies collaboration but also fosters a more informed decision-making environment for teams.

DeepSeek-V3.2 is a highly optimized large language model engineered to balance top-tier reasoning performance with significant computational efficiency. It builds on DeepSeek's innovations by introducing DeepSeek Sparse Attention (DSA), a custom attention algorithm that reduces complexity and excels in long-context environments. The model is trained using a sophisticated reinforcement learning approach that scales post-training compute, enabling it to perform on par with GPT-5 and match the reasoning skill of Gemini-3.0-Pro. Its Speciale variant overachieves in demanding reasoning benchmarks and does not include tool-calling capabilities, making it ideal for deep problem-solving tasks. DeepSeek-V3.2 is also trained using an agentic synthesis pipeline that creates high-quality, multi-step interactive data to improve decision-making, compliance, and tool-integration skills. It introduces a new chat template design featuring explicit thinking sections, improved tool-calling syntax, and a dedicated developer role used strictly for search-agent workflows. Users can encode messages using provided Python utilities that convert OpenAI-style chat messages into the expected DeepSeek format. Fully open-source under the MIT license, DeepSeek-V3.2 is a flexible, cutting-edge model for researchers, developers, and enterprise AI teams.

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.