Business Software for OpenAI

Kastra serves as the crucial authorization framework for AI systems, determining the permissions of agents, models, and tools prior to their execution. Positioned along the execution path of all interactions such as prompts, tool calls, shell commands, database operations, and API requests, it evaluates each action based on deterministic, attribute-driven policies, rendering decisions to allow, deny, redact, or escalate in less than a millisecond. In contrast to monitoring solutions that only track AI actions post-execution, Kastra proactively prevents unauthorized activities before they can impact any tool, API, database, or production environment. Its comprehensive control plane integrates a policy engine, edge decision-making capabilities, various integrations, and a tamper-proof evidence vault that securely signs each decision for auditing and replay purposes. Furthermore, with Kastra Edge, local enforcement is extended to developer environments, safeguarding coding agents such as Claude Code, Cursor, and Codex CLI from harmful commands, unauthorized data extraction, unsafe file modifications, and improper tool usage. This proactive approach to authorization not only enhances security but also ensures compliance and accountability in AI-driven processes.

Flint AI serves as a local-first and framework-agnostic AgentOps command-line interface designed to assist developers in assessing the reliability of AI agents prior to their deployment in production environments. By executing the command flintai scan, users can evaluate Python source code for various issues such as security flaws, misconfigurations, and inadequate safety measures, while also employing AI reasoning to filter out potential false positives. Additionally, the command flintai eval tests a running agent by sending both functional and adversarial prompts, grading its responses against over 35 established criteria, which encompass aspects like factual accuracy, adherence to instructions, and resilience against prompt injections and jailbreak attempts. Each evaluated agent is assigned a reliability score, with the results linked to the OWASP Agentic Security Initiative risk categories ASI01 through ASI10 and severity assessed via CVSS v4.0 metrics. Flint AI is compatible with several agent frameworks and SDKs, including Claude Agents SDK, LangChain, CrewAI, Anthropic SDK, OpenAI SDK, MCP servers, and AutoGen, ensuring a broad range of applications in the development ecosystem. Furthermore, this versatile tool not only enhances the security and quality of AI agents but also streamlines the evaluation process, ultimately fostering greater confidence in AI deployment.

Prefactor is a cutting-edge platform designed for real-time assessment, monitoring, and reliability of production AI agents. It evaluates each execution instantly based on metrics such as quality, drift, cost, and data risk, seamlessly integrating these assessments into actionable responses to ensure that any failing agent is detected in real time rather than merely reflected on a post-execution dashboard. Teams are equipped to monitor every model invocation, tool usage, and decision-making process through structured traces and spans, allowing them to conduct evaluations using LLM-as-judge, technical assessments, qualitative analyses, and custom metrics at every phase of the process. Additionally, context can be incorporated from various sources, including GitHub, Linear, Jira, databases, and internal APIs, serving as ground truth for evaluations. When a run exceeds predefined limits, Prefactor is capable of blocking or throttling it, pausing sensitive actions, or routing the decision to a person for approval, modification, or rejection prior to execution, with meticulous logging of each choice made. The command-line interface allows for the discovery of agents without the need for platform migration, while the TypeScript and Python SDKs ensure seamless integration with LangChain, Claude, Vercel AI, OpenClaw, and LiveKit, enhancing the overall functionality and adaptability of the platform. This comprehensive approach not only optimizes agent performance but also fosters collaboration among teams by providing clear visibility and control over the AI processes.

GPT-Realtime-2.1 is an OpenAI realtime model designed for advanced voice-agent and speech-to-speech AI applications. It improves on GPT-Realtime-2 with stronger alphanumeric recognition, better silence and noise handling, and more natural interruption behavior. The model supports text, audio, and image inputs, while producing text and audio outputs for interactive realtime experiences. Developers can use GPT-Realtime-2.1 across endpoints such as Chat Completions, Responses, Realtime, realtime translation, realtime transcription sessions, and related OpenAI API workflows. The model supports function calling, configurable reasoning effort, instruction following, and reasoning token support for complex voice-agent tasks. Its 128,000-token context window and 32,000-token maximum output make it suitable for longer conversations and more detailed realtime workflows. GPT-Realtime-2.1 does not support video, structured outputs, fine-tuning, or predicted outputs according to OpenAI’s current documentation. Pricing starts at $4 per 1 million text input tokens and $24 per 1 million text output tokens, with separate pricing for audio and image tokens. By combining realtime audio interaction, reasoning, tool use, and multimodal input, GPT-Realtime-2.1 helps developers build responsive AI agents for support, sales, operations, translation, transcription, and interactive voice applications.

Spokenly is an innovative dictation application powered by AI, available for Mac, iPhone, Windows, and Linux, designed to convert spoken words into clear, punctuated text in any working environment. By simply holding a shortcut, users can speak naturally and then release to insert the transcription directly at the cursor across various platforms including browsers, email, chat applications, word processors, IDEs, terminals, and more. This versatile app accommodates over 100 languages, supporting mixed-language dictation, and provides both local and cloud-based speech-to-text models. Users can utilize on-device models like Whisper and Parakeet for offline operation, while cloud services from companies such as OpenAI, Deepgram, Groq, Soniox, and ElevenLabs can be accessed for enhanced accuracy or real-time transcription needs. Additionally, the Local Only Mode ensures that voice data remains solely on the device, preventing any network interactions. The application features modes that allow users to save different transcription models, select AI providers, set prompts, and choose output styles tailored for specific tasks. Furthermore, the AI Instructions feature enables users to eliminate filler words, correct grammar and punctuation, summarize, rewrite, translate, or reformat the dictated text, enhancing the overall functionality and user experience of the app. With its extensive capabilities, Spokenly stands out as a comprehensive solution for anyone looking to streamline their dictation process.

FluidVoice is a free and open-source dictation application for macOS that combines local speech recognition with an on-device AI model known as Fluid-1, which enhances the quality of dictation. By using a single hotkey, users can dictate text into virtually any input field across various applications such as email, documents, chat interfaces, terminals, code editors, and more, with the text being displayed almost instantaneously. The application relies on local speech models that function offline, allowing for secure dictation without needing an internet connection, while optional AI post-processing can utilize Fluid Intelligence, OpenAI, Groq, or other custom providers. Fluid-1 improves initial dictation by refining rough entries, correcting formatting, capitalization, dates, names, and numbers, and it adjusts the tone according to the currently active application, all while preserving the speaker's intended meaning. Users have the flexibility to develop personalized prompts tailored to different applications, and with modes like Write Mode, Command Mode, and Direct Dictation, transitioning between tasks is seamless. Furthermore, FluidVoice is capable of supporting over 40 languages, leveraging various models such as Nemotron Speech 3.5, Parakeet Flash, Parakeet TDT versions 2 and 3, Cohere Transcribe, Apple Speech, and Whisper, thus catering to a diverse user base and enhancing accessibility in dictation across different linguistic backgrounds. This versatility makes FluidVoice an essential tool for those seeking effective and efficient dictation solutions.

AICostGuardian serves as a comprehensive platform for managing AI expenses, enabling organizations to monitor, enhance, and regulate their spending on over 25 different AI service providers through a centralized interface. It meticulously tracks every API interaction with millisecond accuracy, providing instantaneous cost calculations and integrating provider data into comprehensive analytics, automated reporting, forecasting, and visual dashboards. Teams can evaluate spending patterns, benchmark usage against peers, pinpoint areas for cost savings, and leverage machine-learning insights alongside intelligent recommendations to minimize avoidable AI costs. With predictive alerts and anomaly detection features, users receive timely notifications about unusual spikes in usage and potential budget exceedances, while customizable spending thresholds ensure that consumption remains manageable. Additionally, department-specific cost tracking, team performance analytics, detailed permission settings, and role-based access facilitate clearer ownership accountability and regulation of AI resource utilization throughout the organization, ensuring informed decision-making and strategic oversight. As organizations increasingly adopt AI technologies, AICostGuardian stands out as a vital tool for fostering financial prudence and operational efficiency.

SatGate functions as a governance and accountability layer for AI agents, regulating their access, expenditure, delegation, and execution capabilities prior to any interaction with APIs, models, MCP tools, or external paid services. Operating as an HTTP reverse proxy and MCP proxy, it implements scoped authority, individual agent budgets, routing policies, and next-request revocation directly within the request workflow. Agents initiate their access by authenticating through established systems like Kubernetes, AWS, or OIDC, after which SatGate Mint converts that identity into a cryptographically signed Macaroon that delineates limits regarding scope, budget, expiration, and delegation depth. The architecture ensures that permissions can only tighten as requests traverse through agent chains, effectively stopping sub-agents from exceeding their authorized capabilities. In addition, the Observe mode tracks requests and analyzes resource usage categorized by agent, team, tool, route, and cost center while preserving existing workflows, whereas the Control mode imposes strict budgetary limits to prevent unauthorized or costly actions from being executed. This dual functionality allows organizations to maintain oversight while granting necessary freedoms to their AI agents.

Cloptima is an innovative platform that integrates AI and cloud FinOps, offering governance for LLM expenditures, insights into multicloud costs, optimization for Kubernetes, analysis of queries, and controls on engineering costs within a unified framework. Through its AI gateway, teams can securely utilize their own credentials from OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock, applying a range of protections like encrypted controls, virtual keys, model policies, token limits, budgets, guardrails, and attribution before any requests are sent to the providers. The platform's spend analytics provide a comprehensive breakdown of usage categorized by provider, model, team, application, environment, user, agent session, tool, workflow, and other dimensions, while the agent controls monitor retries, loops, tool interactions, and the potential for runaway costs. Additionally, exact and semantic response caching can help minimize redundant usage, whereas intelligent routing capabilities allow for the redirection of eligible traffic to more cost-effective or faster models, with the option for canary rollout and rollback if there are regressions in quality, latency, or error rates. This holistic approach ensures that organizations can effectively manage their AI-related expenditures while maximizing efficiency and performance across their operations.

AICosts.ai serves as a comprehensive platform for managing AI-related expenses, consolidating billing and usage information from over 50 different providers into a single dashboard. Users can easily upload invoices and data exports in various formats such as PDF, CSV, or JSON, or they can utilize the developer API to send usage events, with the platform efficiently parsing this information into a standardized format without needing any proxy setups or alterations to production requests. It accommodates a wide array of services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily insights break down expenditures by platform, model, and billed units, which encompass tokens, operations, characters, and other specific metrics from providers, enabling users to compare different services and understand the origins of their charges. Additionally, users can set budgets that may either encompass the entire AI landscape or focus on specific platforms or features, while also receiving email notifications whenever their rolling 30-day expenses surpass predetermined thresholds, ensuring they stay informed and within their financial limits. This level of detail and control empowers teams to manage their AI costs more effectively.

Burnwise serves as a financial assistant powered by AI, providing insights into an organization's AI expenditure, the reasons behind spending fluctuations, and strategies for cost reduction without compromising on product quality. It monitors usage metrics across large language models, image generation, video, and audio services from leading providers through a consolidated SDK and a cohesive dashboard. Rather than merely presenting aggregate token statistics, Burnwise breaks down costs by specific product features, users, sessions, teams, and agent workflows, allowing teams to gain a clearer understanding of the actual expenses associated with functions such as chat support, document assessment, summaries, or translation services. The platform's usage intelligence uncovers discrepancies between cost and value, while real-time anomaly alerts detect unexpected surges and excessive prompt usage. Additionally, Burnwise provides a concise set of prioritized decision cards that outline potential savings, risk factors, and quality implications, suggesting actions such as changing models, activating semantic caching, imposing limits, or altering feature operations. By offering these insights, Burnwise empowers organizations to make informed decisions that enhance efficiency and optimize resource allocation.

TokenAtlas is an innovative platform focused on AI FinOps and cost intelligence, designed to assist teams in comprehending, predicting, and managing AI expenses before they escalate. Users can define their workload by providing details such as model specifications, token input and output volumes, request frequencies, and anticipated growth, allowing TokenAtlas to evaluate the scenario against a curated list of API pricing. The cost modeling dashboard consolidates all configured workloads into a single interface, while the model comparison feature juxtaposes various provider and model options using clear and transparent assumptions. Additionally, the what-if scenario planning tool assesses the potential financial impact of introducing a new prompt, switching models, modifying retrieval pipelines, or increasing traffic prior to actual implementation. Moreover, cost risk analysis pinpoints the workloads that are particularly vulnerable to fluctuations in volume, prompt size, or model selection, while benchmark comparisons reveal how the configured model mix stands in relation to standard AI product and infrastructure profiles. This comprehensive approach empowers teams to make informed financial decisions, enhancing overall efficiency and cost-effectiveness in AI operations.

ZenLLM serves as an AI-driven platform focused on optimizing costs for engineering teams that deploy LLM applications in live environments. By linking provider invoices to the underlying application activities, it identifies which specific prompts, workflows, models, customers, retries, and request paths contribute to financial expenditures. Teams can utilize the ZenLLM SDK to transmit request-level telemetry, allowing them to incorporate relevant business context—such as workflow, owner, customer, team, or product feature—without having to store the content of prompts or responses. In addition, it keeps track of token consumption, model selection, latency, errors, retries, and overall costs, revealing wasteful patterns that provider dashboards often obscure. The platform is capable of recognizing instances of context accumulation when conversations or agents repeatedly send extended histories, excessive use of premium models for low-risk tasks, retry loops that lead to unnecessary expenses, outdated system prompts, routing errors, anomalies, and a lack of accountability regarding costs. Furthermore, ZenLLM empowers teams to make informed decisions that can significantly enhance cost efficiency in their LLM application operations.

LLMeter is a comprehensive open-source platform designed for monitoring AI costs, allowing developers to manage their expenditures across various providers like OpenAI, Anthropic, DeepSeek, OpenRouter, Mistral, and Azure OpenAI from a single dashboard. By simply connecting read-only provider keys, teams can instantly access detailed insights into actual costs, daily usage trends, model-specific analytics, and potential areas for optimization, all within approximately 30 seconds and without any need for SDK installation, endpoint modifications, or rerouting production traffic through a proxy. Since it facilitates direct communication with model providers, LLMeter introduces no additional latency, avoids becoming a single point of failure, and does not access or store any user prompts or completions. Additionally, budget alerts notify teams prior to exceeding their daily or monthly spending thresholds, while anomaly detection features help catch unexpected usage surges before they escalate. The platform's dashboard provides a clear overview of the costs associated with various providers, models, endpoints, customers, and environments, and its integration with OpenRouter enhances transparency by covering over 500 models, ensuring users have a robust tool for managing their AI-related expenditures efficiently. Ultimately, LLmeter empowers teams to make informed financial decisions regarding their AI usage.

LLMetrics serves as a comprehensive cost tracking solution for teams involved in the development of AI products, integrating model expenses, token consumption, feature attribution, and usage notifications into a single, interactive dashboard. This powerful tool accommodates over 100 models from various providers, including OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, and Groq, with pricing information updated on a daily basis. Teams can label each model interaction with details such as feature name, provider, model type, input tokens, and output tokens, enabling them to pinpoint which specific functionalities—be it a chatbot, summarizer, search tool, or lesson creator—are contributing to their expenditures. The platform offers real-time updates and daily trend visualizations, illustrating how costs fluctuate in response to software releases, modifications to prompts, increases in traffic, or transitions between models. Additionally, it includes spend thresholds and spike-detection features that can alert teams via email or Slack when unusual usage patterns are identified, aiding them in preventing runaway loops and unforeseen cost surges prior to receiving the provider invoice. By leveraging these insights, teams can make informed decisions regarding their AI product strategies and budget management.

AI Cost Board serves as a comprehensive platform for monitoring AI API usage and managing associated costs, consolidating important metrics like expenses, requests, tokens, latency, errors, and overall usage from various model providers into a unified real-time dashboard. By directing LLM traffic through a single proxy endpoint, applications can efficiently forward requests to the designated provider while capturing detailed logs that include model information, token usage, status, timing, costs, input, output, and raw JSON context. Typically, teams only need to adjust the base URL of the provider and utilize an AI Cost Board project key, thereby maintaining the integrity of the original request structure. This platform accommodates a variety of providers such as OpenAI, Anthropic, and Google Gemini, offering a standardized setup that harmonizes usage data across different integrations. Cost analytics provide a breakdown of spending categorized by project, provider, model, and timeframe, enabling users to identify trends, calculate cost per request, assess success rates, and evaluate operational performance. Moreover, the searchable request logs empower developers to analyze payloads, address failures, compare various models, and probe into instances of slow or costly API calls. Overall, AI Cost Board enhances transparency and control over AI API expenditures, facilitating informed decision-making for teams utilizing AI technology.

StackSpend is an advanced cost management platform leveraging cloud and AI technologies, designed to offer engineering, finance, and FinOps teams a consolidated daily overview of their contemporary AI infrastructure. By establishing read-only connections to a variety of providers such as AWS, Google Cloud, Azure, Snowflake, and others, it seamlessly imports historical billing information and standardizes expenditure across different services. The platform features comprehensive dashboards and exploration tools that dissect costs by various dimensions, including provider, service, model, project, user, team, feature, and customer, thereby aiding teams in analyzing AI COGS, cost per request, and profit margins at the product level. Additionally, it provides insights into budgets and projected spending trends, while its same-day anomaly detection feature identifies unexpected cost spikes triggered by factors such as traffic surges, prompt errors, model adjustments, deployment activities, or specific user actions. Notifications and daily indicators, categorized as green, amber, or red based on spending levels, can be dispatched through communication platforms like Slack, Microsoft Teams, email, or webhooks, ensuring teams remain informed about their spending patterns. Ultimately, StackSpend empowers organizations to maintain a firm grip on their AI expenditures, fostering enhanced financial accountability and strategic decision-making.

Ecango is a cutting-edge tool that utilizes artificial intelligence for transcribing audio and video, transforming spoken words into precise and easily searchable text almost instantaneously. Users have the convenience of uploading files through various methods, such as drag-and-drop, after which Ecango swiftly creates the transcript, allowing for direct edits within the browser and the option to export in widely used formats like DOCX, ODT, PDF, SRT, and TXT. The platform excels in providing transcription, subtitles, and translation services for over 90 languages, dialects, and accents, employing sophisticated speech recognition technology to achieve an impressive accuracy rate of up to 99.8%. It also features speaker identification and diarization capabilities, which recognize multiple speakers within a single recording and arrange their dialogue in a clear, user-friendly format. Ecango is compatible with various popular audio and video file types and can automatically process video files without the need for users to extract the audio beforehand. Additionally, its advanced AI algorithms can effectively reduce background noise, thereby enhancing the overall quality of transcription and translation, particularly in challenging recording environments. This makes Ecango not only a versatile tool but also an essential resource for anyone dealing with audio and video content.

SignalPGx serves as a worldwide platform for pharmacogenomics reporting and medication intelligence, catering to laboratories, hospital systems, provider networks, clinics, pharmacy partners, and healthcare organizations. This innovative platform transforms genotype data into medication guidance that is both physician-reviewed and supported by evidence, utilizing white-label PGx reports, SignalAI-assisted interpretation, a Medication Intelligence Graph, Living Patient Passport, medication simulation, and continuous reanalysis. The system is compatible with various file formats, including VCF, Agena MassARRAY, CSV, and custom lab files, and offers EHR-ready integrations such as SMART on FHIR, HL7/FHIR, CDS Hooks, and REST APIs. Designed for enterprise-level healthcare implementation, SignalPGx features tenant isolation, role-based access control, audit logging, encryption, and a scalable multi-region infrastructure hosted on AWS, ensuring that healthcare providers can rely on it for comprehensive medication management. Furthermore, its robust architecture guarantees seamless operation and data security for all users, enhancing the overall efficiency of healthcare delivery.

bb is an innovative, local-first IDE that allows for extensive customization while interacting with AI coding agents, enabling users to automate, control, and even enhance its functionalities. With just a single prompt, users can effortlessly modify nearly every aspect of the environment, including the addition of panels, CLI commands, skills, plugins, and workflows, which become instantly accessible to their agents. The platform’s various features, such as GitHub integration, agent memory, scheduled tasks, and remote access, are structured as plugins utilizing the same tools that users can employ. Furthermore, its command line interface supports integration with external applications, such as shell scripts, cron jobs, and messaging bots from Telegram, Signal, and Slack, which can initiate tasks that remain visible in the sidebar. bb accommodates several coding agents, like Claude Code, Codex, Cursor, Pi, OpenCode, Grok, omp, and Hermes, allowing users to delegate tasks to the most appropriate agent or enable one agent to create and oversee another in distinct threads. Additionally, all work is executed on the user's own device, providing the flexibility for tasks to persist and operate autonomously until the user decides to resume their interaction. This level of independence enhances productivity and allows for a seamless workflow experience.

Nativ is an entirely open-source application designed for macOS, enabling users to execute OpenAI models locally on Apple Silicon, thereby bringing cutting-edge intelligence directly to your workspace without the need for accounts or cloud infrastructure. It features an intuitive chat interface that facilitates streaming responses, supports Markdown and code highlighting, accepts image inputs, and offers performance metrics for each message, all while ensuring that responses are generated locally on the device. The app includes a curated library of models from various teams, such as Google, Cohere, and Liquid AI, and it intelligently suggests models that align with the specifications of your Mac hardware. Built on the MLX-VLM architecture and optimized for M-series unified memory and Metal, Nativ operates models seamlessly without the need for wrappers or translation layers. Users benefit from live telemetry that provides insights into tokens processed per second, memory usage, thermal conditions, and the time taken to generate the first token, giving a clear view of the inference process. Furthermore, Nativ accommodates diverse workflows, including language processing, vision tasks, video analysis, code assistance, and audio manipulation, allowing users to engage in activities like conversing with LLMs, generating image captions, summarizing video content, auto-completing code snippets, transcribing audio files, and producing speech outputs. This versatility makes Nativ an invaluable tool for developers and creators looking to harness local AI capabilities.

P4 (formerly Helix Core) is a high-performance version control system that provides robust capabilities for managing code, assets, and files across global development teams. It supports large-scale projects, enabling seamless collaboration and version tracking for both code and non-code assets, including 3D models and media files. Designed for industries with complex workflows, such as gaming, automotive, and software development, P4 offers unmatched scalability, security, and speed. The platform integrates easily with development tools, providing a comprehensive solution for teams seeking efficient version control across all stages of the development lifecycle.

Fluid Topics is a dedicated Product Knowledge Platform that consolidates and unifies scattered content into a single, authoritative source. From there, it delivers consistent, reliable product knowledge to any channel—documentation portals, in-product help, support tools, or AI applications. Fluid Topics includes an MCP server, enabling LLMs and AI agents to access and interact with this knowledge in a controlled, standardized, and dependable way. It also supports AI-native applications such as RAG-based chatbots and Generative AI copilots. The platform is designed to manage: - Large volumes of content - Multiple product versions and variants - Diverse audiences and usage contexts - Regulatory, compliance, and security requirements

Sprinklr is a Unified-CXM platform that enables enterprises to manage every customer interaction from a single AI-powered system. It combines marketing, social media management, customer service, and consumer insights into one cohesive platform. Sprinklr uses advanced AI to analyze unstructured data from millions of conversations to uncover actionable insights. Its AI copilots and intelligent agents help automate workflows and enhance team efficiency. Marketing teams can run compliant global campaigns while maintaining brand consistency across channels. Customer service teams benefit from omnichannel support tools and real-time context for every interaction. Sprinklr enables human-AI collaboration to deliver more empathetic and personalized experiences. The platform integrates seamlessly with existing enterprise technology stacks. Built for scale, it supports global teams with high customization and governance. Sprinklr helps organizations transform customer experience into a competitive advantage.

Teneo.AI is a market-leading agentic AI platform built to fully automate enterprise customer service. It delivers intelligent AI agents that manage voice and digital interactions with up to 99% resolution accuracy. Designed for speed, Teneo enables organizations to launch pilots in weeks and reach production rapidly. The platform supports omnichannel engagement across voice, chat, apps, and email from a single environment. Voice AI capabilities allow enterprises to handle millions of interactions every month without service disruption. Teneo integrates easily with existing contact center and enterprise systems through open APIs. Built-in analytics provide actionable insights to continuously optimize performance. Enterprise-grade security and governance ensure compliance with internal and regulatory requirements. Organizations achieve significant cost savings while improving first-contact resolution and CSAT. Teneo helps enterprises progress from automation to fully agentless customer service operations.