Business Software for OpenAI

DeepInfra is a cloud-based AI inference platform designed to effortlessly execute a wide range of the latest machine learning models at scale, such as large language models, vision models, embeddings, and various forms of media generation including images and videos. The platform offers serverless inference via straightforward APIs, enabling developers to seamlessly incorporate production-ready AI models into their applications without the burden of managing GPU resources, auto-scaling, complex deployments, or model hosting logistics. Supporting OpenAI-compatible APIs allows for an easier transition from existing OpenAI-style integrations, while also providing access to an extensive library of both open-source and commercial models. With its Native API, users can access every type of model available on the platform, covering tasks such as image generation, speech recognition, object detection, token classification, fill-mask, image classification, zero-shot image classification, and text classification. DeepInfra is designed for optimal performance, ensuring scalable, low-latency inference powered by state-of-the-art GPU infrastructure, which ultimately enhances the efficiency of AI-driven applications. This focus on performance makes it an ideal choice for businesses looking to leverage advanced AI technologies.

Zefort is an advanced platform for contract management that utilizes AI to ensure consistent oversight of contracts in intricate scenarios. It provides valuable assistance to legal, compliance, procurement, finance, and business teams by maintaining contracts in a manner that is clear, searchable, organized, and manageable long after they have been signed. The AI capabilities of Zefort enhance the handling of contracts within extensive and complicated archives, where precision, dependability, oversight, and governance are crucial. This platform enables teams to pinpoint essential contract details, maintain visibility of obligations, swiftly locate information, and handle follow-ups efficiently as the volume of contract data increases. Rather than viewing contract management merely as a means of storing documents, Zefort leverages AI throughout the entire lifecycle of contracts to enhance structure, visibility, monitoring, compliance, and daily contract-related tasks. Users benefit from the ability to centralize all executed contracts, conduct searches across various agreements, automate monotonous metadata tasks, and easily identify key aspects such as suppliers, important dates, contract values, obligations, and significant terms, ultimately streamlining their workflows. Additionally, Zefort's comprehensive approach allows for improved collaboration among teams, as they can easily access and share contract information, ensuring that everyone stays aligned and informed.

Argorant is a comprehensive B2B contact database designed specifically for revenue teams that have grown weary of dealing with bounce-back emails and steep enterprise pricing. With the ability to search through an impressive 614 million contacts spanning 184 countries, users can easily find relevant companies by entering straightforward industry terms like ‘fintech’ or ‘dental practices.’ Each email address is meticulously verified through a live SMTP probe at the time of export, with catch-all servers being distinctly marked, ensuring that users receive only deliverable addresses rather than unreliable guesses based on patterns. Argorant operates on a fully self-service model, offering a free trial without the need for a credit card, along with paid plans starting at less than $100 monthly, flexible monthly billing, and the option to cancel with just one click. Additionally, it comes equipped with a REST API, webhooks, and an MCP server, allowing AI tools like Claude and GPT to seamlessly search for, verify, and export leads in real-time, making it an indispensable tool for modern sales strategies. This combination of features positions Argorant as a leading solution for businesses looking to optimize their lead generation efforts.

JackHamr functions as an AI-driven software solution that manages the entire development process from start to finish. Rather than relying on a singular AI assistant, it coordinates a team of specialized agents: a specification agent formulates requirements, a planning agent divides them into tasks, a coding agent develops the software, a testing agent performs evaluations, a quality reviewer ensures standards are met, and a deployment agent launches the product. These agents operate within hosted cloud development environments utilizing tools such as VS Code, Docker, SSH, and WireGuard-encrypted networking, allowing them to continue functioning even when your laptop is closed. Communication with these agents can be conducted through push-to-talk voice chat or natural typing, making interactions seamless. GitHub integration is built-in, offering features like one-click cloning, automatic branch creation for each task, real-time commit synchronization, and the ability to create pull requests effortlessly. Users have the flexibility to utilize their own LLM keys from providers like OpenAI, Anthropic, Google, or opt for the cost-effective options available through JackHamr, with the ability to switch models during the workflow. The pricing model is pay-as-you-go with detailed billing, charging only for infrastructure costs and LLM tokens without any additional fees. To encourage new users, there is a $10 credit available at the outset, and no credit card is required for registration, allowing anyone to experiment with the platform without financial commitment. This innovative approach not only streamlines software development but also enhances collaboration among various specialists in the field.

Ling 2.6 represents an independently developed and open-source series of large language models created by Ant Group, utilizing a Mixture of Experts (MoE) architecture to enhance inference efficiency, long context modeling, training methodologies, and collaborative reasoning for AI agents. By employing this MoE architecture, Ling effectively directs each token to engage only the most pertinent expert subnetworks, significantly reducing the computational load while preserving the extensive capabilities of the model. This series makes strides in long-sequence modeling, exemplified by Ling-2.6-1T, which accommodates a native context window of up to 1 million tokens and offers a 256K context window through its official API; additionally, Ling-2.6-flash features a native 256K context window, enabling it to handle around 200,000 characters in lengthy inputs. These models are meticulously crafted to ensure dependable retrieval of long-range information without any discernible loss of quality, regardless of whether the data is located at the start, middle, or end of the context. This innovative approach to long-context processing sets a new benchmark for efficiency and reliability in language model performance.

Plugsky is an AI infrastructure platform that gives developers and businesses access to models, agents, RAG, tools, and deployment options through a single API. Its OpenAI-compatible interface allows teams to migrate existing applications by changing the base URL while continuing to use familiar SDKs and workflows. The platform supports more than 31 first-party and partner models, including chat, reasoning, coding, vision, and embedding models. Plugsky offers flat-rate pricing with unlimited usage under fair-use limits, helping teams avoid unpredictable per-token costs and rate-limit surprises. Businesses can deploy on Plugsky’s cloud, their own cloud, private regional infrastructure, or on-premises environments depending on compliance and data residency requirements. Agent Cloud enables teams to build AI agents with function calling, memory, orchestration, tools, and private knowledge retrieval. Plugsky also includes Model Fusion, a marketplace for agents and prompt packs, white-label model options, and integrations for developers and SaaS teams. Enterprise controls such as SSO, RBAC, audit logs, SLAs, GDPR alignment, PDPL support, and private endpoints make it suitable for regulated industries. Plugsky gives organizations a flexible way to build, scale, and control AI applications without being locked into a single model provider or deployment environment.

OpenParser AI serves as a cutting-edge truth hub for organizations that handle extensive documentation, designed to consolidate information from documents, databases, and web content into a secure and unified knowledge layer. By posing a question, users can allow the AI to retrieve precise answers from their documents, databases, or websites, with each response directly linked to the specific document page, database entry, or web segment from which it originated. OpenParser enhances document management through features like straightforward document Q&A, seamless database querying without the need for SQL, comprehensive website content searches, and cross-document insights, all while ensuring role-based access and implementing document approval workflows. Additionally, it offers instant validation and generates structured outputs that teams can efficiently act upon. Users have the flexibility to upload documents or link to platforms like Google Drive, SharePoint, SQL databases, and website URLs, with an auto-sync feature that keeps all information up to date. The OpenParser AI Engine is capable of comprehensively analyzing and reasoning through all available data, utilizing Llama by default or allowing integration with the user's preferred LLM, which can include options such as OpenAI, Anthropic, Gemini, and others. This powerful combination ensures that teams have access to the most relevant information in real-time, ultimately improving decision-making processes and operational efficiency.

The Meta Model API is an innovative developer interface designed for utilizing Muse Spark 1.1, Meta's advanced multimodal reasoning model tailored for agentic tasks such as coding, tool utilization, and comprehensive computer interactions. Currently available in public preview, this API enables developers to seamlessly integrate Muse Spark 1.1 via an OpenAI-compatible package, simplifying the transition for existing clients while maintaining the same code framework and allowing for easy configuration to the muse-spark-1.1 model. This model excels in personal agentic functions, facilitating planning and coordination across various external applications and services, while also adapting to new native tools, MCP servers, and bespoke skills. Functioning as a primary agent, it can collect contextual information, devise plans, and oversee execution across multiple subagents; conversely, as a subagent, it adheres to its designated role, comprehends available tools, and recognizes when to escalate issues. Additionally, the model is capable of managing a context window of 1 million tokens, allowing it to remember past actions, retrieve information from significantly earlier tasks, and effectively condense context for optimal performance. With these capabilities, the Meta Model API represents a significant advancement in the development of intelligent, responsive applications.

ForthWrite is an innovative AI email assistant that composes responses in your unique style directly within Gmail and Outlook. Simply open any conversation, and you'll find your reply already prepared, becoming more tailored to your voice with each email interaction. Rather than generating a standard "professional tone" response, ForthWrite adapts by learning from your actual sent emails, capturing your distinctive tone, sentence structure, preferred sign-offs, terminology, and the adjustments you make over time. With a click on Generate Draft, you receive a refined reply that reflects your voice, which you can tweak as necessary before sending; every accepted draft and dispatched reply contributes to a personalized writing profile that is exclusively yours. ForthWrite is designed for those who value the nuances of their email communication, particularly when messages need to convey a sense of individuality, accuracy, and authenticity rather than sounding formulaic. It integrates seamlessly within both Gmail and Outlook Web, accommodating both professional and personal email accounts, enabling users to continue crafting their messages without the hassle of switching between tabs or transferring prompts to a different AI tool. This streamlined functionality ensures that your email experience remains efficient and user-friendly.

RunInfra effortlessly transforms natural language into fully operational AI inference endpoints. By simply describing your requirements, the AI agent autonomously constructs, refines, deploys, and scales your project without the need for YAML configurations, DevOps expertise, or GPU setup—just a conversation. Designed specifically for delivering open-source AI models as production-ready APIs, it intelligently chooses suitable models, benchmarks actual GPU performance, implements kernel enhancements, and establishes HTTP endpoints compatible with OpenAI. RunInfra is capable of creating diverse applications including language models, speech recognition, text-to-speech, embeddings, vision-language tasks, image generation, retrieval-augmented generation (RAG) searches, document analysis, transcription services, AI assistants, and complex multi-model reasoning frameworks, contingent on the runtime and model capabilities. Its streamlined workflow progresses seamlessly from your initial description to optimization, deployment, and integration; simply inform RunInfra of your needs, and it will evaluate real GPU options from L4 to B200, explore model variants like AWQ, GPTQ, and FP8, fine-tune kernels using Forge, and deliver a fully functional endpoint compatible with OpenAI’s Python and JavaScript SDKs. The efficiency and simplicity of RunInfra make it a valuable asset for developers aiming to leverage advanced AI technologies without the typical complexities involved.

Flo2 serves as a gateway and router that connects users to leading AI model providers such as OpenAI, Anthropic, Groq, Cerebras, and DeepInfra via a single, unified API that is compatible with OpenAI. It intelligently selects the most cost-effective or quickest model for each request through smart routing capabilities. To ensure reliability, automatic fallback mechanisms maintain application functionality even if one provider experiences downtime. Additionally, racing mode allows for simultaneous processing of requests across multiple providers, enhancing efficiency. Comprehensive cost tracking is available, detailing expenses for each request, model, and project. Developers are able to utilize their own provider keys on flo2.com, and RapidAPI's testing tier offers free tokens for preliminary evaluations. This seamless integration is aimed at simplifying the development process while maximizing performance and minimizing costs.

Mercury 2 represents a groundbreaking advancement in reasoning models, specifically designed for real-time voice interaction as it can quickly answer phone calls. Unlike traditional autoregressive models that leave callers in silence while generating responses one token at a time, Mercury 2 employs a diffusion large language model architecture capable of producing over 1000 tokens per second with standard NVIDIA GPUs. This remarkable speed allows it to complete a full reasoning process and begin speaking within a timeframe that aligns with natural conversational flow, effectively shortening the typical wait time from several seconds to approximately 300 milliseconds. The operational mechanism of Mercury models involves transforming clear text into noise, after which a conventional Transformer is trained to reverse this transformation and predict the original text across all positions at once. By utilizing a denoising approach that engages multiple tokens simultaneously, generation becomes more efficient, enabling speeds akin to custom silicon on NVIDIA H100s while improving responsiveness in voice applications. As a result, Mercury 2 not only enhances user experience but also sets a new standard for interactive voice technologies.

Command Code is an advanced coding assistant that operates within the terminal, enabling the creation of comprehensive full-stack applications, deploying new features, troubleshooting issues, writing test cases, and optimizing code, all while adapting to the unique workflows of individual developers. It harnesses the power of the meta neuro-symbolic taste-1 model alongside continuous reinforcement learning, interpreting every suggestion, rejection, and modification as valuable feedback, which allows it to identify and cultivate recurring preferences, structures, patterns, and tools into enduring skills and memories for each project. Rather than simply adhering to standard best practices, it assimilates developers' code review techniques, stylistic inclinations, architectural choices, as well as their preferred package managers and libraries, even those minor conventions that often go undocumented, thereby applying this contextual understanding in future interactions. Command Code is equipped with features that facilitate interactive command-line interface operations, headless prompts, automated task execution, planning capabilities, background sandboxes, customizable agents, checkpoints, and memory retention across different sessions, providing a truly personalized coding experience. This innovative tool not only streamlines the development process but also empowers developers to enhance their productivity and maintain consistency in their coding practices over time.

Cloudgov.ai serves as an intelligent AI-driven FinOps platform designed for ongoing management of costs and policy adherence across various environments, including cloud, multicloud, data systems, containers, and artificial intelligence. By integrating major platforms such as AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into a unified control panel, it enables teams to monitor expenses, allocation, policies, and associated risks in real time. Its Continuous Multicloud Observability feature links accounts, reviews past expenditures, categorizes costs based on region, account, and service, and projects future spending based on historical data. With AI-generated insights, the platform uncovers areas of waste and potential optimization, and its anomaly detection functionality alerts users to unexpected spikes in spending along with their financial implications. Furthermore, it provides ready-to-use Infrastructure as Code snippets for remediation, which allows engineering teams to implement suggested adjustments seamlessly, and integrates with Jira to convert insights and anomalies into actionable tasks for team members, thereby streamlining the workflow for cost management. Overall, Cloudgov.ai empowers organizations to maintain financial control while enhancing efficiency across their cloud operations.

Waterfall serves as a credit infrastructure tailored for platforms that leverage large language models, enabling the transformation of AI applications into profitable business ventures without the need for teams to develop a proprietary billing system. It offers each user, agent, or team a credit wallet secured by stablecoins, meticulously tracking every model interaction based on provider, model, token count, and associated costs. Users can either route their requests through the Waterfall Gateway or utilize TypeScript and Python SDKs for integration, ensuring that usage is accurately attributed to the appropriate wallet in real time. Each API request is settled instantly against the wallet, leading to a decrease in credits while allowing for immediate revenue recognition for every request, eliminating the delays associated with traditional invoicing and manual accounting processes. With support for over 300 models from various providers, including OpenAI, Anthropic, DeepSeek, and xAI, Waterfall enables products to seamlessly deploy multiple AI services while managing a unified accounting framework. This innovative approach simplifies financial management for AI-driven applications, making it easier for businesses to scale their operations efficiently.

FinOps LLM serves as an advanced platform for AI cost management and observability, specifically designed for engineering teams utilizing production GenAI. It enables transparency in token expenditures across a variety of providers such as OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, and Groq, while also aligning internal usage data with invoices from these providers. Users can filter token-level expenses based on provider, model, feature, team, customer, environment, and other custom metrics, ensuring that each dollar spent has a designated owner. Additionally, the platform includes attribution and chargeback functionalities that correlate usage with product interfaces and customer demographics, facilitating showback processes and allowing for data exports to systems like NetSuite, QuickBooks, CSV, or through APIs. Furthermore, real-time anomaly detection features track spending, latency, and quality, comparing them against dynamic feature baselines, and issue alerts via Slack, PagerDuty, email, or webhooks whenever notable changes occur. To further enhance cost control, optional budget enforcement and auto-throttling measures can prevent excessive spending due to runaway agents, excessive retries, or unexpected model shifts. This comprehensive approach ensures that engineering teams can manage their AI resources effectively while maintaining financial oversight.

Celeris-1 stands out as a swift and versatile language model platform, complemented by a diffusion model that achieves cutting-edge intelligence at unprecedented speeds. Unlike conventional autoregressive models that generate tokens sequentially, Celeris employs a diffusion-based inference architecture that allows for simultaneous generation, resulting in response times that can be measured in mere milliseconds. On the MMLU-Pro benchmark, Celeris-1 boasts an impressive accuracy of 75.9% while achieving a median response time of 158 milliseconds and producing an astonishing 1,664 output tokens per second, positioning it closely to leading models but operating over ten times faster. This powerful model is accessible through an API that is compatible with OpenAI, enabling developers to seamlessly integrate it into existing SDKs and applications with minimal modifications. Additionally, it supports streaming capabilities for real-time applications, allowing for response times as low as 24 milliseconds without any buffering or delays, making it an ideal choice for interactive use cases. Overall, Celeris-1 represents a significant advancement in the efficiency and performance of language models.

Mongrel serves as a comprehensive desktop database systems workbench that integrates over 30 database engines, terminals, file transfers, container management, Kubernetes functionalities, and an API client into a single native application compatible with Windows, macOS, and Linux platforms. You can execute queries on various databases such as PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, Redis, ClickHouse, Snowflake, BigQuery, and others using native SQL or respective engine dialects. The application allows for inline data editing, navigation through foreign keys, running EXPLAIN plans, designing schemas using a visual ER canvas, synchronizing data across different databases, and scheduling encrypted backups with ease. It replaces a multitude of tools by offering built-in SSH, Mosh, Telnet, Serial terminals, as well as SFTP and SCP file transfers, management for Docker and Podman, Kubernetes functionalities, and support for HTTP, GraphQL, WebSocket, and gRPC clients. An optional AI assistant is available, compatible with OpenAI, Anthropic, and Gemini providers, and it securely stores keys in your operating system's keychain while ensuring that all writes are subject to approval. Mongrel is designed to prioritize user privacy, ensuring that your queries, data, and logs remain exclusively on your local machine. All subscription plans provide access to every feature, and you can initiate a 7-day trial without needing to enter any payment information. Additionally, the user-friendly interface and rich functionality make it an essential tool for database management.

DeskFerry serves as an intuitive platform designed for assembling an AI team simply by articulating your needs in everyday language. It generates AI agents that can seamlessly integrate with the tools your team is already familiar with, facilitating automated tasks across over 1,500 applications without the necessity of coding. These agents possess the capability to read, write, and interact with various services, including Gmail, Slack, Notion, Salesforce, HubSpot, Jira, and Stripe, among others, while users have the option to select from a variety of AI models provided by OpenAI, Anthropic, Google, xAI, and more. Teams can customize agent skills to align with their brand voice, create branded materials, and organize outputs in appropriate tools and formats. Additionally, the platform features scheduling and trigger functions that support automations running on an hourly, daily, or weekly basis, or in response to events within connected applications. To enhance security and oversight, human-in-the-loop mechanisms can be implemented, requiring approval prior to the execution of significant actions, such as sending emails or updating records. Users can enrich the platform's knowledge base by incorporating documents, databases, and URLs, while a memory feature enables automations to remember previous interactions and preferences over time, ensuring a more personalized user experience. This combination of functionality and ease of use makes DeskFerry a valuable asset for teams looking to leverage AI in their workflows.

BusyLamp is a specialized legal spend management and eBilling solution designed to address the complexities of European legal operations. It enables organizations to manage outside counsel billing with automation, compliance, and detailed financial insights. The platform includes features like automated invoice conversion, which transforms billing data into standardized formats while reducing manual entry and errors. BusyLamp provides real-time dashboards and reporting tools that offer visibility into legal spend, helping teams make informed resourcing decisions. It is built to handle cross-border invoicing, multi-currency transactions, and VAT compliance requirements. The system enhances efficiency by reducing administrative workload and simplifying invoice review processes. BusyLamp also ensures data security with enterprise-grade encryption and compliance certifications. Integration capabilities allow it to connect seamlessly with existing enterprise systems. It offers multilingual support and tailored features for European legal environments. Overall, BusyLamp helps organizations improve cost control, compliance, and operational efficiency in legal spend management.

Multichannel communication platform Clienk allows companies to communicate effectively with their customers. Clienk provides solutions for online conversion, mail and telephony, as well as web chat, proactive chat, social media, and web self-service. It improves customer satisfaction, sales, and trust in your business by allowing you to use one web application. Managers strive to achieve more, even if they are forced to do it with fewer employees. The modern consumer does not accept a lower level of service. How can you solve this problem? Clienk solutions will allow your team to work more efficiently. Our all-in-one app allows you to quickly serve multiple customer contact channels, including chat, Facebook, Wechat and WhatsApp. Sales is all about leveraging momentum. Chat is a great way to reach potential customers when they need you.

Build, test and optimize Generative AI apps that unlock the value in your data. Our industry-leading ML expertise, our state-of-the art test and evaluation platform and advanced retrieval augmented-generation (RAG) pipelines will help you optimize LLM performance to meet your domain-specific needs. We provide an end-toend solution that manages the entire ML Lifecycle. We combine cutting-edge technology with operational excellence to help teams develop high-quality datasets, because better data leads better AI.

Promote authentic student work while tackling even the most advanced forms of misconduct effectively. Equip instructors with efficient tools that simplify the grading process, allowing them to dedicate more time to teaching. Foster the development of students' creative thinking abilities through constructive, high-quality feedback that integrates smoothly into teachers' current workflows. Address the integrity of student submissions and keep pace with new trends in academic dishonesty through this all-encompassing solution. Utilize a contemporary assessment platform that enables the delivery and evaluation of various types of assessments from any location. This powerful plagiarism detection tool is designed to seamlessly integrate into existing academic practices. Recognized as the benchmark for plagiarism checking among scholars and publishers, this tool ensures the highest standards are met. Collaborating with educators around the globe, Turnitin shares innovative and effective instructional strategies. In addition, explore an array of resources, including our blog, teaching tools, comprehensive guides, and white papers, to further enhance educational practices. This holistic approach not only safeguards academic integrity but also enriches the learning experience for students and teachers alike.

Galileo is an AI observability and eval engineering platform designed to help teams evaluate, monitor, guardrail, and improve AI agents and applications. The platform connects the offline testing process with production governance, turning evals into guardrails that can control agent actions, tool access, escalation paths, and safety behavior. Galileo helps teams capture ground truth from synthetic data, development data, live production data, and subject matter expert annotations. Its evaluation capabilities include RAG evals, agent evals, safety evals, security evals, and custom evals that can be tuned to specific environments. Galileo’s Luna models distill optimized LLM-as-judge evaluators into compact models that run at lower cost and latency for production-scale monitoring. The insights engine analyzes traces, prompts, functions, context, datasets, models, and agent behavior to identify failure modes and recommend fixes. Teams can use Galileo to detect hallucinations, tool-selection failures, drift, bias, unsafe outputs, and other reliability issues before they harm production experiences. Deployment options include SaaS, virtual private cloud, and on-premises environments. By combining observability, eval engineering, production guardrails, ground-truth datasets, Luna models, insights, and enterprise deployment options, Galileo helps organizations build more reliable AI systems.

GLTR is designed to utilize the same models that generate counterfeit text as a means for detection. It incorporates the GPT-2 117M language model from OpenAI, which stands out as one of the most substantial models accessible to the public. By taking any given textual input, GLTR evaluates the predictions made by GPT-2 at each position in the text. The output showcases a ranking of all words recognized by the model, allowing us to determine how the actual following word ranks in comparison. Utilizing this positional data, a color-coded mask is applied to the text, reflecting the ranking position of each word. Words that rank among the most probable are shaded in green (for the top 10), yellow (for the top 100), red (for the top 1,000), while the remaining words appear in purple. Consequently, this method provides a clear visual representation of how probable each word is according to the model's predictions, ultimately enhancing our ability to identify potentially fake text. Additionally, this visual tool can help users quickly gauge the authenticity of a given passage.