Business Software for Claude

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.

FlowRunner stands out as a visual workflow automation platform that integrates native AI agent orchestration, allowing agents to execute workflows independently while pausing for necessary human input. What sets FlowRunner apart is its unique approach to oversight; while most platforms treat approval as a workflow step, FlowRunner empowers agents to reach out to humans as callable tools during execution. It effectively gathers the necessary context and options, then communicates with a designated individual via email, Slack, WhatsApp, or phone, resuming the workflow once a decision is made. Notably, a run can remain on hold for up to a year, ensuring that time-sensitive decisions, like those requiring three days, do not have a deadline. In addition to this, features like audit trails, role-based access control (RBAC), and service level agreement (SLA) tracking are available starting from the mid-tier package, rather than being confined to custom enterprise agreements, and a business associate agreement (BAA) can also be acquired. Every subscription level allows for unlimited users, unlimited workflows, and full access to the integration catalog, with no limitations based on user count or connectors. Billing is structured around the number of workflow runs rather than the individual action steps taken, ensuring a transparent pricing model. Agents are also able to utilize their own AI provider keys, with no additional charges for inference. Furthermore, users can choose between cloud-hosted and self-hosted options, providing flexibility to fit various operational needs.

DemandBird serves as a comprehensive social media management solution tailored for B2B teams and agencies, integrating planning, content creation, approval processes, publishing, and analytics into a single streamlined workflow. Its innovative Content Studio empowers users to brainstorm ideas using AI technology, craft these ideas into drafts in their unique voice, and tailor content for various social media platforms. All published content is organized within a content library, allowing past posts to be reused, refreshed, or adapted for different channels, eliminating the need to start from scratch each time. With a convenient one-click repurposing feature, users can easily modify saved content to fit the chosen platform, while the Auto-Adapt functionality intelligently adjusts a single concept for diverse channels like LinkedIn, X, Instagram, Facebook, Substack, YouTube, Threads, and Bluesky. Additionally, the platform's scheduling tools enable teams to effectively queue posts across multiple platforms, manage timing, approve content, and rely on DemandBird to handle the publishing process seamlessly. This ensures that teams can focus on strategy and creativity, rather than the logistics of content distribution.

Rhizome serves as an AI regulatory collaborator specifically designed for teams in the life sciences sector. While general AI assistants offer remarkable capabilities, there are distinct requirements for an AI tool that aids in making decisions regarding drugs or devices worth over $100 million. Our agents are strictly limited to referencing facts derived from primary source documents, ensuring that nothing is validated based on agent memory; we assess their performance based on this criterion, and there have been no instances of inaccuracies reported in the past 18 months. The databases that underpin regulatory decisions, including Drugs@FDA, guidance documents, clinicaltrials.gov, and various standards, often exist in formats that are not easily searchable by computers, with a significant amount being image-only PDFs. We have developed the ability to ingest and process all of this information, encompassing over 75 health authority databases across more than 12 markets as of August 2026, with this number continually growing. This equates to over 45 million documents that our agents can query comprehensively. In contrast to a general assistant, which typically references 5 to 10 sources, Rhizome is capable of examining hundreds to thousands of documents, as the essential question is often, "Did we overlook a precedent?" This thorough approach ensures that our users are equipped with the most comprehensive and accurate information possible.

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.

Looops is an innovative website creation platform powered by AI, enabling users to build fully functional websites simply by articulating their desires in everyday language. Rather than relying on pre-existing templates or grappling with complex design software, users engage in a conversation with the AI to construct web pages, modify layouts, revise text, produce images, and fine-tune the site, all while witnessing real-time updates in the live preview. Every component displayed, from buttons and headlines to images, can be easily modified with straightforward natural-language commands. Additionally, users have the option to upload sketches, screenshots, inspirational images, or Figma designs, allowing Looops to tailor the website according to the provided visual cues. Each adjustment is saved as a checkpoint, granting users the flexibility to revert specific changes or completely roll back the site to a previous state. The platform excels in offering responsive design, ensuring pages load rapidly, and maintaining an SEO-friendly architecture. It also features contact forms, databases, user authentication, member areas, dynamic content capabilities, an integrated visual content management system, visitor analytics, and secure data storage. Publishing a site is seamless and can be achieved with just a single click, followed by straightforward hosting options that enhance user convenience. This comprehensive approach to website building makes Looops an appealing choice for both novices and experienced developers alike.

Invora is self-hosted invoicing and billing software bought once rather than rented. It covers estimates that convert to invoices in one click, branded PDF documents, a public link each client opens without an account, part-payments with outstanding balances by client and by age, recurring invoices that always land as drafts for review, a reusable item catalogue and configurable tax rates. Invoices and payment reminders can be emailed through your own SMTP server, with every attempt recorded in a send log, and due dates published as a calendar feed. Invora records payments but never processes cards: you paste your own payment link, which appears as a Pay button on unpaid documents only.

SuperApp is an innovative AI-driven collaboration platform that integrates individuals, advanced AI technologies, and content creation into a single, cohesive live thread, guiding users from initial concept to completed project. Rather than dispersing tasks across various tools for communication, artificial intelligence, and content development, it maintains a unified environment where discussions, documents, references, decisions, feedback, revisions, approvals, and final outputs are interlinked within a common, enduring framework. This allows teams and AI to engage in real-time brainstorming, deliberation, and creation, with the system intelligently directing tasks to the most suitable AI models for each phase of the project. Additionally, users have the flexibility to highlight their preferred AI model or assess multiple options within the same shared thread, mitigating concerns about being locked into a single provider. The platform ensures that all completed work is preserved in the context where it was developed, fostering an effective environment for teams to create, evaluate, modify, and finalize various outputs such as documents, presentations, spreadsheets, applications, dashboards, and tools, all seamlessly integrated into the ongoing conversation. Ultimately, SuperApp enhances collaboration by ensuring that every stage of the creative process is connected and accessible, promoting efficiency and innovation.

Shyne serves as an innovative AI-driven collaborative workspace where teams and agents co-create on an endless canvas, effectively consolidating presentations, documents, social media content, images, videos, websites, and prototypes into a unified live interface. Users have the ability to prompt anywhere within this canvas, with an AI agent working alongside their cursor in real time, while ensuring that every frame remains fully editable by both individuals and agents simultaneously. Brand kits ensure that colors, fonts, and logos are consistently aligned across all creations, maintaining a coherent brand identity. Teams can effortlessly generate images and videos directly on the canvas, animate components along a real timeline, and produce interactive prototypes, all while being able to import files as editable layers, which streamlines the process from design to publishing without the need to switch between different tools. Additionally, a built-in content calendar enhances scheduling for platforms like Instagram, LinkedIn, X, and the web, allowing for easy resizing of content tailored to various channels, thus making it a multifaceted tool for creative collaboration. Ultimately, Shyne not only simplifies the design process but also fosters a seamless integration of creativity and functionality.

Lettertrace is a platform designed for open-source AI visibility monitoring that tracks the frequency with which AI models like Claude, ChatGPT, Gemini, and Google AI Overviews mention and endorse specific brands. Users input the topics their customers frequently inquire about, and the platform efficiently creates diverse natural prompts to observe how AI assistants respond to these queries. Each monitoring session generates valuable data points, enabling teams to assess visibility, prominence, sentiment, and share of voice related to various topics and models over time. Additionally, competitor analysis reveals how often competing brands are referenced, which entities are recommended by AI, and how a particular brand stacks up against its rivals. The generated reports transform raw AI responses into essential metrics that AEO and GEO teams rely on, such as brand visibility, share of voice, sentiment analysis, recommendations, and competitive standings. By offering scheduled monitoring options on a daily or weekly basis, the platform helps create actionable trend lines, while its multi-model analysis capability allows for a side-by-side comparison of results from different AI answer engines, providing a comprehensive view of brand performance. This multifaceted approach equips brands with the insights necessary to refine their strategies in an increasingly competitive marketplace.

OpenBudget seamlessly links your banking and investment accounts with AI assistants like Claude and ChatGPT via MCP, allowing you to pose genuine questions regarding your finances and receive accurate answers. Once the connection is established, all transactions and holdings are synchronized automatically, enabling you to inquire about your spending habits, net worth, dividend income, or potential capital gains tax liabilities before selling assets, with Claude providing responses directly from your actual accounts rather than relying on estimates. This connection is strictly read-only, meaning OpenBudget can access your data but is unable to transfer funds, execute trades, or modify any details in your accounts. Additionally, it integrates with any AI client you currently utilize, ensuring you’re not confined to a single application to benefit from its features. This flexibility empowers users to make informed financial decisions while maintaining full control over their accounts.

ZeroCMS is a Git-based content management system designed specifically for Astro, leveraging AI technology. By simply connecting to your GitHub repository, non-technical editors can set up a complete visual CMS in less than 10 minutes without the need for code alterations, plugins, or configuration files in the repository. ZeroCMS seamlessly integrates with your current Astro content collections, Zod schemas, and components, automatically generating editing forms instead of requiring a separate content model to maintain. Editors can interact with a live preview of the actual site, enabling real-time collaboration among multiple users. Each time content is published, it creates a clean Git commit in the branch that your hosting service monitors, allowing platforms like Vercel, Netlify, Cloudflare Pages, or any CI pipeline to deploy as they normally would. By keeping Git as the source of truth, ZeroCMS ensures that users are not locked into a single vendor. Additionally, the integrated AI agent enhances the editing process by reading and revising content in context while also performing audits for SEO, accessibility, readability, and identifying broken links. This combination of features makes ZeroCMS a powerful solution for streamlined content management without the typical hurdles associated with traditional CMS platforms.

Colrows represents an advanced autonomous semantic layer designed to enhance the dependability of enterprise AI at a production level. Many enterprise AI projects experience delays due to the phenomenon of model hallucinations and the absence of verifiable answers. Colrows addresses the context rather than modifying the model itself. This innovative platform perpetually traverses databases, data warehouses, catalogs, and various documentation to create a fluid business graph that encompasses entities, metrics, and logic. Positioned between enterprise data and AI interfaces, Colrows transforms natural language inputs into governed and auditable SQL query plans, ensuring that every output is directly linked to a confirmed single source of truth. Notable features comprise: AI Data Analyst: Offers conversational analytics with comprehensive SQL lineage tracking. Semantic API: Provides a standardized semantic framework for internal copilots and agents. Auto-Crawl Engine: Facilitates ongoing metadata synchronization without the need for manual coding. Colrows guarantees both mathematical precision and regulatory compliance that probabilistic retrieval-augmented generation (RAG) solutions are unable to achieve, making it particularly advantageous for industries that are under strict regulatory scrutiny. Its ability to maintain high standards of accuracy and accountability sets it apart as a vital tool for businesses operating in complex compliance environments.

OpenCode Zen functions as an AI portal, providing coding agents with a meticulously selected array of dependable and optimized AI models that have been rigorously tested and validated by the OpenCode team. This initiative addresses the inconsistencies arising from the vast assortment of available models, as well as the various configurations and service methods employed by different providers, which can result in fluctuating performance and quality. The team conducts thorough evaluations of a carefully chosen group of models, collaborates with model teams and providers to establish optimal operational parameters, ensures accurate service delivery, and benchmarks each model-provider pairing prior to making recommendations. Users engage with Zen in the same manner as other providers within OpenCode, utilizing an API key to access a direct interface that displays the suggested model selections. Additionally, its usage is entirely voluntary, allowing developers the flexibility to integrate it with other coding agents, thereby preventing vendor lock-in while still enabling access to validated model configurations. Ultimately, OpenCode Zen empowers developers by streamlining their AI model selection process while ensuring consistent quality and performance across various coding tasks.

OpenCode Go empowers programmers globally by offering dependable access to a handpicked selection of proficient open coding models. Tailored for users around the world, it emphasizes consistent global accessibility, ample usage allowances, and models specifically optimized for coding-related tasks. While open models have achieved performance levels comparable to proprietary options for coding assignments, variations in provider quality, latency, and availability exist. To mitigate these issues, the OpenCode team rigorously evaluates chosen models, collaborates with model teams and providers to optimize their delivery, and conducts benchmarks on each model-provider pairing prior to making recommendations. Users of Go engage with it like any other provider within OpenCode, utilizing an API key to directly explore the available models in the interface. This feature is entirely optional and can seamlessly integrate with other coding agents, effectively preventing vendor lock-in. By ensuring flexibility and access, OpenCode Go stands out as a valuable tool in the coding community.

Slack Code offers a shared coding platform that unites team members and AI agents to collaboratively develop software transparently. By mentioning an agent, a temporary code channel is initiated for a designated task, which allows the team to have a focused space for tracking progress, providing guidance, reviewing modifications, and approving outcomes, all while keeping main channels uncluttered. The agents leverage the conversations and knowledge they are authorized to access, enabling them to utilize relevant team context right from the outset. Team members can articulate their development needs, allowing an agent to generate functional code as everyone observes, contributes feedback, and makes decisions on what gets deployed. Once the task is completed, these code channels are automatically archived, yet their context remains searchable for later use. Users can find and manage agents within the Agents tab, where they can monitor ongoing sessions, check live statuses, and identify when their input is required. Additionally, enhanced thread features create more descriptive titles, making it simpler to locate and continue previous work, thereby promoting a more organized workflow. This innovative approach not only enhances collaboration but also streamlines the entire coding process for teams.

Wikis.ai is a versatile AI workspace that enables users to pose a single question and receive distinct, independent responses from various leading AI models displayed side by side. This platform eliminates the need to flip between different tabs and services; instead, users can send a unified prompt to models like GPT, Claude, Gemini, and DeepSeek, allowing them to observe each response in real-time and compare diverse methodologies within a well-organized grid. By maintaining the complexity of each answer, it prevents the oversimplification of multiple viewpoints into a singular anonymous response, thus aiding users in identifying points of agreement and contention among the models, as well as claims that may require additional scrutiny. Instances of consensus can act as an initial indicator of reliability, while areas of disagreement emphasize topics that merit further exploration. Additionally, Wikis.ai features an AI Wiki that offers editorially curated guides covering essential AI concepts, various models, infrastructure, agent frameworks, safety considerations, and other relevant subjects, enriching the user’s understanding and engagement with AI technologies. This comprehensive approach not only enhances the user experience but also promotes informed decision-making when leveraging AI insights.