Business Software for Amazon Bedrock

Anthropic has introduced Claude Haiku 4.5, its newest small language model aimed at achieving near-frontier capabilities at a significantly reduced cost. This model mirrors the coding and reasoning abilities of the company's mid-tier Sonnet 4, yet operates at approximately one-third of the expense while delivering over double the processing speed. According to benchmarks highlighted by Anthropic, Haiku 4.5 either matches or surpasses the performance of Sonnet 4 in critical areas such as code generation and intricate "computer use" workflows. The model is specifically optimized for scenarios requiring real-time, low-latency performance, making it ideal for applications like chat assistants, customer support, and pair-programming. Available through the Claude API under the designation “claude-haiku-4-5,” Haiku 4.5 is designed for large-scale implementations where cost-effectiveness, responsiveness, and advanced intelligence are essential. Now accessible on Claude Code and various applications, this model's efficiency allows users to achieve greater productivity within their usage confines while still enjoying top-tier performance. Moreover, its launch marks a significant step forward in providing businesses with affordable yet high-quality AI solutions.

Rebolt is a sophisticated AI platform tailored for enterprises, allowing businesses to develop bespoke applications and intelligent agents through simple verbal commands directed at the AI. It provides seamless integration with various corporate tools like OneDrive, SharePoint, Salesforce, and Slack, as well as custom APIs, and includes essential infrastructure such as databases, file storage, scheduling capabilities (like cron jobs), audit logs, and separate environments for staging and production deployment. Users can generate applications and agents without any need for API key coding, merely by articulating their requirements in natural language, while still ensuring robust enterprise security features, permissions mapping through systems like Azure groups, and role-based access controls. This platform is specifically engineered for constructing operational workflows, internal tools, and automation that link to the firm's existing data and services, thus empowering non-technical users or low-code teams to quickly create solutions that can replace spreadsheets, cumbersome manual processes, and disjointed SaaS solutions. Additionally, Rebolt's intuitive design fosters increased collaboration among teams, enhancing productivity and innovation within the organization.

BotDojo serves as a robust AI enablement platform tailored for enterprises, allowing companies to create, implement, oversee, and expand intelligent agents across various communication channels like chat, voice, email, and web, all through an intuitive low-code visual workflow designer that seamlessly integrates with existing enterprise data systems. It boasts a library of over 100 pre-built templates aimed at streamlining typical applications, including support automation, knowledge retrieval, sales analytics, and internal operations, while also facilitating branching logic, memory capabilities, and the orchestration of tools such as code, RPA, and web browsing. In addition, BotDojo establishes connections with essential business tools like CRMs, ticketing platforms, and databases to enhance its functionality. The platform further fosters continuous improvement and learning for agents through human feedback loops, enabling employees to mentor agents by providing feedback, embedding corrections into agent memory and responses, and assessing performance using comprehensive observability metrics, including deflection rates, first-contact resolution, and cost per interaction. Ultimately, BotDojo not only optimizes operational efficiency but also ensures that intelligent agents evolve and adapt to meet organizational needs effectively.

Nao is an innovative data IDE powered by artificial intelligence, specifically tailored for data teams, seamlessly merging a code editor with direct access to your data warehouse, enabling you to write, test, and manage data-related code while retaining complete contextual awareness. It is compatible with various data warehouses, including Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Upon connection, nao enhances the conventional data warehouse console by providing features like schema-aware SQL auto-completion, data previews, SQL worksheets, and effortless navigation between multiple warehouses. At the heart of nao lies its intelligent AI agent, which possesses comprehensive knowledge of your data schema, tables, columns, metadata, as well as your codebase or data-stack context. This agent is capable of generating SQL queries, constructing entire data transformation models such as those used in dbt workflows, refactoring existing code, updating documentation, conducting data quality assessments, and performing data-diff tests. Furthermore, it can uncover insights and facilitate exploratory analytics, all while maintaining strict adherence to data structure and quality standards. With its robust capabilities, nao empowers data teams to streamline their workflows and enhance productivity significantly.

Liquid AI's LFM2.5 represents an advanced iteration of on-device AI foundation models, engineered to provide high-efficiency and performance for AI inference on edge devices like smartphones, laptops, vehicles, IoT systems, and embedded hardware without the need for cloud computing resources. This new version builds upon the earlier LFM2 framework by greatly enhancing the scale of pretraining and the stages of reinforcement learning, resulting in a suite of hybrid models that boast around 1.2 billion parameters while effectively balancing instruction adherence, reasoning skills, and multimodal functionalities for practical applications. The LFM2.5 series comprises various models including Base (for fine-tuning and personalization), Instruct (designed for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language variants, all meticulously crafted for rapid on-device inference even with stringent memory limitations. These models are also made available as open-weight options, facilitating deployment through platforms such as llama.cpp, MLX, vLLM, and ONNX, thus ensuring versatility for developers. With these enhancements, LFM2.5 positions itself as a robust solution for diverse AI-driven tasks in real-world environments.

Toolspend is an innovative spend management platform powered by AI, aimed at providing organizations with comprehensive insights into their expenses related to AI and SaaS through a cohesive, automated dashboard. By linking seamlessly with AI service providers and financial systems, it uncovers actual usage trends, highlights which teams are responsible for spending, and aligns token metrics with billing details. This platform surpasses basic subscription monitoring by evaluating usage behaviors, allowing it to identify underused licenses, duplicated tools across different departments, and areas where overpayments may occur. With features such as real-time monitoring, alerts for unexpected usage spikes, and month-end forecasting, teams can better prepare for costs prior to receiving invoices. Additionally, it offers AI-generated suggestions, like transitioning to more affordable models or halting resources that are not in use, which assists companies in minimizing waste and managing budget increases effectively. Furthermore, by leveraging its insights, organizations can make informed decisions that enhance their operational efficiency.

asqav is a cutting-edge platform focused on AI governance and security, aimed at ensuring that AI agents are always prepared for audits by offering real-time oversight, enforcement, and a reliable record of each action performed by the agents. It features a streamlined SDK that empowers developers to embed governance functionalities directly into their AI agents with minimal code, facilitating comprehensive monitoring throughout the entire lifecycle of AI activities. Additionally, the platform incorporates behavioral analysis to identify potential problems like drift, rate limits, and scope breaches, as well as sophisticated threat detection mechanisms that can recognize issues such as prompt injections, leaks of sensitive information, harmful outputs, and other dangers. Policy enforcement is achieved through customizable “policy gates,” which implement specific rules for each agent, conduct preflight assessments, and provide dynamic approvals before any actions are taken, thereby guaranteeing that agents function within established parameters. Furthermore, asqav enhances security with automated incident response features, allowing for the suspension, isolation, or escalation of agents deemed risky, all of which contribute to a robust framework for maintaining AI accountability and safety. In this way, asqav not only safeguards AI operations but also promotes trust in their deployment across various sectors.

GPT-5.5 Pro is a next-generation AI model built for execution-heavy tasks across coding, research, business analysis, and scientific workflows. It can interpret complex instructions, break them into steps, and carry work through to completion using tools and automation. The model supports tasks such as generating documents, building applications, analyzing datasets, and navigating software environments. It is designed to operate across tools, enabling seamless workflows from idea to output. In addition, GPT-5.5 Pro integrates with workspace agents—customizable AI agents that automate recurring and multi-step processes across teams. These agents can handle tasks like lead research, reporting, and workflow automation, running independently or on schedules. Built with enterprise-grade safeguards, the model ensures secure and controlled automation. It helps organizations improve productivity by reducing manual effort and accelerating decision-making. GPT-5.5 Pro is ideal for teams looking to scale operations and handle complex workloads efficiently.

Sourcebot is a self-hosted platform for code comprehension that assists developers and AI agents in searching, navigating, and reasoning through extensive codebases, regardless of their size. It allows teams to index repositories from various sources, including GitHub, GitLab, and Bitbucket, enabling exploration through a cohesive interface that offers quick, multi-repository searches with sophisticated filtering options, regex capabilities, and queries tailored to specific programming languages. The platform features an "ask mode" that lets users ask questions in everyday language, while an integrated language model scours the indexed code, tracks references, and provides structured responses with inline citations linked to the corresponding code snippets. Beyond search functionalities, Sourcebot enhances the development experience by incorporating IDE-level navigation tools such as go-to-definition and find-references across all repositories, as well as a built-in file explorer that showcases syntax highlighting and complete visibility of code. This comprehensive set of features empowers developers to work more efficiently and gain deeper insights into their projects.

Agent Control represents a groundbreaking open-source framework designed to manage the behavior of AI agents on a large scale, setting a new benchmark for governance in this domain. It addresses the issue of disjointed and hardcoded checks by providing teams with a unified governance layer that enforces regulations at each step, all managed from a single control interface that can be updated dynamically without altering the agent's underlying code. Developers can easily designate any function as governable by applying the control() decorator, thereby transforming key decision points within an agent into independently regulated control points, each equipped with its own governance policies. When a decorated function runs, Agent Control assesses the input or output against the prevailing policy and generates a response that could be to deny, steer, warn, log, or allow the action. If a denial occurs, the SDK triggers a ControlViolationError, preventing any unsafe actions from being executed. This separation of policies from the actual code empowers developers to strategically position control hooks, while policy teams determine the enforcement specifics of those hooks, ensuring a collaborative approach to governance. The flexibility and robustness of Agent Control make it an invaluable tool for organizations looking to standardize AI agent governance effectively.

Preloop serves as an open-source control plane designed for AI agents that perform tangible actions. It integrates a multi-layered security approach featuring an MCP firewall for managing tool access, an AI model gateway that ensures cost-effectiveness, safety, and accountability, along with policy-as-code that incorporates human oversight, all while providing runtime session visibility and audit trails—all within a self-hosted environment. Given the rapid capabilities of AI agents to deploy code, modify infrastructure, manage financial transactions, access production data, and incur model costs almost instantaneously, Preloop empowers teams to regulate agent activities, monitor expenditures, and determine which actions necessitate human consent. It is compatible with a variety of tools such as OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any agents that adhere to MCP standards. Additionally, access rules can evaluate not only the tool names but also arguments and context, utilizing CEL expressions to establish detailed conditions. Furthermore, teams have the flexibility to initiate with observability features and progressively introduce approval and denial protocols without the need for SDKs or extensive modifications to existing applications, thus streamlining the implementation process. This comprehensive approach ensures that organizations remain in control of their AI agents' functionalities and impacts.

Ejentum serves as a structured reasoning framework tailored for agentic AI, enhancing the reliability, auditability, and discipline of LLM agents during intricate or protracted tasks. This innovative tool can be invoked by agents mid-task, facilitating precise cognitive operations tailored to the specific challenges they face, allowing for real-time corrections in reasoning rather than depending solely on static prompts. Designed to prevent AI agents from deviating, flattering, fabricating, or fixating on incorrect hypotheses, Ejentum also ensures they don’t settle for superficial answers or lose vital context over successive steps. The framework boasts 679 capabilities organized into four cognitive harnesses: reasoning, code, anti-deception, and memory. Within the reasoning harness, analytical capabilities are directed towards understanding causality, time, space, simulation, abstraction, and metacognition, which aids agents in steering clear of merely recognizing surface patterns. By integrating these diverse functionalities, Ejentum empowers AI to maintain a deeper engagement with tasks, ultimately enhancing the quality of their outputs.

Vision Agents is a versatile open-source Python framework designed for developing low-latency voice and video AI agents utilizing any model. This framework empowers developers to integrate large language models, speech recognition, and vision models from over 25 different providers, enabling the creation of real-time agents for applications such as telehealth, voice assistance, live coaching, video analysis, interactive avatars, security surveillance, sports commentary, and a variety of other multimodal uses. Its architecture is tailored to facilitate the development of agents capable of listening, speaking, seeing, processing media, accessing tools, and providing instant responses, all while operating on Stream's expansive global edge network, which ensures latency below 500ms. With just a minimal Python setup, developers can quickly create their first agent by leveraging platforms like Gemini Realtime, OpenAI, Deepgram, ElevenLabs, Stream, or other compatible providers. Furthermore, Vision Agents accommodates both real-time speech-to-speech models and tailored speech-to-text, language processing, and text-to-speech pipelines, allowing teams to either rapidly deploy a functional voice agent or exercise complete control over the components involved in speech recognition, language reasoning, and text-to-speech functionalities. Overall, this framework not only simplifies the process of building sophisticated AI agents but also enhances flexibility and performance across diverse applications.

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.

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.

Stable Diffusion is a generative image model family from Stability AI designed to help users create high-quality images across many styles and use cases. The models can generate photography, 3D visuals, paintings, line art, illustrations, product concepts, branded assets, and other creative outputs from text prompts. Stable Diffusion is built for strong prompt following, giving users more control over the final image and making it useful for detailed creative direction. The model family includes options optimized for professional image quality, faster generation, and customization on consumer hardware. Users can deploy Stable Diffusion through a self-hosted license, integrate it through the Stability AI API, access it through cloud partners, or use it in web-based creative tools. Stability AI also offers image editing APIs and tools for editing uploaded or generated images. These tools support object erasing, inpainting, outpainting, upscaling, sketch-based generation, structural control, and style control. Stable Diffusion can support workflows such as brand style creation, product photography, concept art, marketing visuals, app experiences, creative tools, and enterprise image generation. By combining flexible deployment, image generation, editing, and customization, Stable Diffusion gives teams a powerful foundation for building and scaling AI-powered visual creation.

We focus on creating and executing solutions that leverage collective intelligence and augmented technology. Stability AI is dedicated to developing open AI tools that enable us to unlock our full potential. Our team consists of passionate builders who are genuinely concerned about the real-world impact of our work. Significant progress often arises from collaboration across various teams, where we embrace the challenge of questioning established norms and fostering creativity. Our core motivation lies in producing groundbreaking ideas and transforming them into practical solutions. We prioritize innovation over tradition, believing that our diverse perspectives strengthen our approach. By valuing these differences, we aim to find common ground and harness the power of varied viewpoints to drive our mission forward. Ultimately, our commitment to collaboration and creativity cultivates an environment where transformative ideas can thrive.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Pixtral Large is an expansive multimodal model featuring 124 billion parameters, crafted by Mistral AI and enhancing their previous Mistral Large 2 framework. This model combines a 123-billion-parameter multimodal decoder with a 1-billion-parameter vision encoder, allowing it to excel in the interpretation of various content types, including documents, charts, and natural images, all while retaining superior text comprehension abilities. With the capability to manage a context window of 128,000 tokens, Pixtral Large can efficiently analyze at least 30 high-resolution images at once. It has achieved remarkable results on benchmarks like MathVista, DocVQA, and VQAv2, outpacing competitors such as GPT-4o and Gemini-1.5 Pro. Available for research and educational purposes under the Mistral Research License, it also has a Mistral Commercial License for business applications. This versatility makes Pixtral Large a valuable tool for both academic research and commercial innovations.

Transitioning from development to production, as well as from traditional data engineering to artificial intelligence, requires securing the various environments, pipelines, tools, and open-source components integral to your data and AI supply chain. It is essential to continuously identify, prevent, and rectify security and compliance vulnerabilities in AI before they reach production. In addition, monitoring AI applications in real-time allows for the detection and mitigation of adversarial AI attacks while enforcing specific application guardrails. Noma integrates smoothly across your data and AI supply chain and applications, providing a detailed map of all data pipelines, notebooks, MLOps tools, open-source AI elements, and both first- and third-party models along with datasets, thereby automatically generating a thorough AI/ML bill of materials (BOM). Additionally, Noma constantly identifies and offers actionable solutions for security issues, including misconfigurations, AI-related vulnerabilities, and non-compliant training data usage throughout your data and AI supply chain. This proactive approach enables organizations to enhance their AI security posture effectively, ensuring that potential threats are addressed before they can impact production. Ultimately, adopting such measures not only fortifies security but also boosts overall confidence in AI systems.

SaaSConstruct is an all-in-one template that accelerates the creation of SaaS applications on the AWS cloud platform. It features a complete, pre-configured codebase that integrates cloud infrastructure alongside both frontend and backend elements, facilitating the quick deployment of SaaS websites with essential functionalities like user authentication, payment processing, and customizable AI chatbots. Built upon AWS services, the infrastructure allows for easy scalability and integration with other AWS tools as required. By providing these ready-to-use components, SaaSConstruct enables developers to efficiently test and launch their projects, allowing them to concentrate on core features rather than getting bogged down by initial setup tasks. This template includes code for the majority of standard SaaS processes such as payments and authentication, significantly reducing your AWS expenses during the development phase. Additionally, it supports the integration of AI models like those from AWS Bedrock. With SaaSConstruct, you can save countless hours of development time, ensuring you have everything necessary to launch your SaaS product today while maintaining a focus on innovation.

Celonis offers an AI Development platform that empowers businesses to create and implement sophisticated AI copilots, assistants, and agents, all based on a digital replica of their operational processes. These Process Copilots function as generative AI chatbots tailored for particular applications, delivering rapid insights into processes through an easy-to-use chat interface. They are capable of addressing urgent inquiries regarding metrics and workflows, producing visual data representations such as charts and tables, and pinpointing areas that require enhancement in key performance indicators. This innovative method allows organizations to efficiently expand their process insights, which in turn improves decision-making and boosts operational efficiency. Additionally, the annotation builder feature enables users to organize data to extract valuable insights from various sources, including emails and service tickets, utilizing generative AI techniques. For instance, it can categorize customer service orders and evaluate the risks associated with moving materials between plants, thereby facilitating AI assistants that offer actionable recommendations for user consideration. Ultimately, this platform not only streamlines operations but also fosters a culture of data-driven decision-making within enterprises.

Kong AI Gateway serves as a sophisticated semantic AI gateway that manages and secures traffic from Large Language Models (LLMs), facilitating the rapid integration of Generative AI (GenAI) through innovative semantic AI plugins. This platform empowers users to seamlessly integrate, secure, and monitor widely-used LLMs while enhancing AI interactions with features like semantic caching and robust security protocols. Additionally, it introduces advanced prompt engineering techniques to ensure compliance and governance are maintained. Developers benefit from the simplicity of adapting their existing AI applications with just a single line of code, which significantly streamlines the migration process. Furthermore, Kong AI Gateway provides no-code AI integrations, enabling users to transform and enrich API responses effortlessly through declarative configurations. By establishing advanced prompt security measures, it determines acceptable behaviors and facilitates the creation of optimized prompts using AI templates that are compatible with OpenAI's interface. This powerful combination of features positions Kong AI Gateway as an essential tool for organizations looking to harness the full potential of AI technology.

Created by LM-SYS, RouteLLM is a publicly available toolkit that enables users to direct tasks among various large language models to enhance resource management and efficiency. It features strategy-driven routing, which assists developers in optimizing speed, precision, and expenses by dynamically choosing the most suitable model for each specific input. This innovative approach not only streamlines workflows but also enhances the overall performance of language model applications.