Get a recommendation
Tell us your requirements and our advisors will help you compare and shortlist the best-fit options, free and unbiased.
A real human, fast
Someone on our team replies within one business day, no bots, no ticket queue.
Routed to the right team
Buying, selling, partnering, or investing, you reach the people who can actually help.
Independent & unbiased
No pushy sales. Just honest guidance grounded in the ecosystem.
Tailored to your context
Tell us what you need and we shape the next steps around it.
Who are you? Pick the option that fits best.
111 Listings in AI Assistants Available
What is Microsoft 365 Copilot? Microsoft 365 Copilot is Microsoft's AI assistant built into Microsoft 365 apps such as Word, Excel, PowerPoint, Outlook and Teams. It grounds answers in your organization's work data and respects existing permissions. Key capabilities of Microsoft 365 Copilot Copilot in Office apps: Draft, summarize and analyze inside Word, Excel, PowerPoint, Outlook and Teams Work data grounding: Answers use emails, files and chats you can access Researcher and Analyst agents: Agents for deep research and data analysis Copilot Pages: Collaborative pages that capture AI output Permission-aware answers: Honors existing access controls Enterprise admin controls: Admin settings for governance How Microsoft 365 Copilot works Copilot sends your prompt along with relevant Microsoft 365 data to a large language model through Microsoft Graph, then returns output inside the app you are working in. Answers draw only on content you already have permission to see. Who uses Microsoft 365 Copilot? Businesses already on Microsoft 365, from small teams to large enterprises, that want AI in daily Office work. Microsoft 365 Copilot pricing Per third-party reports, Microsoft 365 Copilot is $30 per user per month on an annual term and requires a qualifying Microsoft 365 license. A Copilot Business option for up to 300 users is reported at $21 (promotionally $18). Copilot Chat is free. Microsoft 365 Copilot alternatives Alternatives include Google Workspace with Gemini, Airtable AI and Appian AI for workflow platforms. Microsoft 365 Copilot is tied to the Microsoft 365 suite.
Capabilities
Deployment
Compliance
Saaskart Market Grid™
Explore how leading AI Assistants solutions compare based on customer satisfaction, market presence, adoption, and buyer feedback. The Market Grid helps you identify category leaders, high-performing solutions, and emerging products within the AI Assistants ecosystem.
Category Leader
Magical
#1 in AI Assistants
Best Value AI Assistants
Amazon Q Business
From $3/mo
Trending
Magical
Most viewed
Market Insights
Derived from live Saaskart marketplace data, engagement, reviews, and pricing for this category.
Tech stacks
See where ai assistants fits in a complete stack, with the other software, AI agents and services each business needs.
What is Algomatic? Algomatic is an AI agent AI agent offering a Japanese startup building vertical AI agents for recruiting, sales and operations. Founded in 2023 and based in Tokyo, Japan, Algomatic helps Japanese businesses automate AI agent work and get results faster. Key capabilities of Algomatic Recruiting agents Sales automation Document generation Business workflows Japanese document processing Enterprise workflows How Algomatic works Algomatic takes text as input and produces text and actions. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Microsoft 365, Slack, Salesforce and kintone, so the agent works inside existing workflows. Who uses Algomatic? Algomatic is built for Japanese businesses. It suits teams that want recruiting agents and sales automation without adding headcount, while keeping people in control of review and final decisions. Algomatic vs ExaWizards Algomatic is often compared with ExaWizards. Algomatic stands out for recruiting agents and document generation. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
Deployment
Compliance
What is VectorShift? VectorShift is an AI platform for building and running AI pipelines, offered through a no-code interface and an SDK. Its current homepage positions it as an AI operating system for private market investors. Key capabilities of VectorShift No-code and SDK builders: create pipelines visually or in code Knowledge bases: index files for retrieval in pipelines Interfaces: publish a pipeline as a user-facing interface API access: call pipelines programmatically Presentation review: cross-references investment decks against data room contents Portfolio monitoring: flags deviations from investment theses How VectorShift works Users assemble a pipeline from components, connect a knowledge base, and run it through an interface or API. The private-markets product adds a table interface for comparing hundreds of deals, a data room tracker and a meeting recorder. The vendor states customer data is never used to train models. Who uses VectorShift? Builders who want AI workflows without heavy engineering, and private market investment firms that want institutional knowledge indexed across deals. VectorShift pricing Starter is free with 1 pipeline and 1,000 actions a month. Standard is $20 per month billed annually ($25 monthly) with 5 pipelines and 10,000 actions. Pro is $100 billed annually ($125 monthly) with unlimited pipelines and 100,000 actions. VectorShift alternatives Beam AI automates business processes with agents, Browser Use lets agents drive a browser, and Notion AI works inside Notion workspaces. VectorShift combines a pipeline builder with SOC 2 Type II security.
Deployment
Compliance
What is Doubao? Doubao is ByteDance's AI assistant and the family of large language models behind it. The chatbot launched in August 2023 and runs on models built by ByteDance's Seed team. The doubao.com page identifies it as an AI assistant under ByteDance. Key capabilities of Doubao Conversational assistant: General chat in Chinese and other languages Search: AI-assisted search answers Writing help: Drafts and edits text Image generation: Creates images from prompts Voice and video conversation: Talks with users by voice and video Seed model family: Runs on ByteDance Seed models, with Seed 2.0 released February 2026 per third-party reports How Doubao works Users chat with Doubao on the web or in the app, and the assistant answers with models from ByteDance's Seed team. Third-party sources cite Seed 2.0 as the stable release in February 2026 and Seed 2.1 Pro as a newer flagship in June 2026. The vendor homepage itself shows little detail, so most specifics here come from third-party reports. Who uses Doubao? Consumers in China who want a free everyday assistant. Third-party sources report about 382 million monthly users in June 2026, making it China's most used AI app. Doubao pricing Doubao was free until June 24, 2026, when ByteDance reportedly launched Doubao Pro at CNY 68, 200 and 500 per month. Third-party reports say search, writing, image generation and voice and video conversation stay free, and the paid tiers mostly buy larger usage quotas. These are not read from the vendor page. Doubao alternatives Alternatives include DeepSeek for open-weight reasoning models, Kimi for long-context chat, and Qwen from Alibaba for a competing model family.
Deployment
What is Kissan AI? Kissan AI is a farmer assistant AI agent offering a multilingual AI assistant for farmers in India that answers crop, pest and farming questions by voice and text. Founded in 2023 and based in India, Kissan AI helps farmers and agri businesses in India automate farmer assistant work and get results faster. Key capabilities of Kissan AI Voice Q&A in Indian languages Pest and disease advice Scheme information Farming best practices Crop advice in local languages Image-based diagnosis How Kissan AI works Kissan AI takes voice and text as input and produces voice and text. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as WhatsApp, iOS, Android and John Deere Operations Center, so the agent works inside existing workflows. Who uses Kissan AI? Kissan AI is built for farmers and agri businesses in India. It suits teams that want voice Q&A in Indian languages and pest and disease advice without adding headcount, while keeping people in control of review and final decisions. Kissan AI vs Plantix Kissan AI is often compared with Plantix. Kissan AI stands out for voice Q&A in Indian languages and scheme information. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
Capabilities
Deployment
Compliance
What is Appian AI? Appian AI is the AI layer of the Appian process platform, offering AI agents and copilots within governed, deterministic workflows. It combines process orchestration, a data fabric, intelligent document processing and guardrails for enterprise use. Key capabilities of Appian AI AI agents: Evaluate data, apply judgment and take allowed actions Process orchestration: Deterministic workflows coordinating AI, systems and people DocCenter: Intelligent document processing for any document type Data Fabric: Governed access to enterprise data without migration Work allocation: Routes tasks to the right AI model or worker Guardrails and audit: Input and output guardrails and immutable audit logs Multi-model support: Choose preferred AI providers in the admin console How Appian AI works Appian places AI agents inside a process model so humans, systems and AI work in a defined sequence. Agents read data through the Data Fabric, apply judgment and run only permitted actions, and each action is logged. Guardrails check inputs and outputs, and customer data is not used to train LLMs. Who uses Appian AI? Regulated enterprises. Typical uses include claim processing, compliance investigation, customer service triage and exception handling. Appian AI pricing Appian does not publish AI pricing on the product page. Contact the vendor for a quote. Appian AI alternatives Alternatives include SAP Joule for SAP-centered work, Microsoft 365 Copilot for productivity apps and Induced AI for browser automation. Appian anchors agents in process orchestration.
Deployment
Compliance
What is Qwen? Qwen is Alibaba Cloud's family of large language and multimodal models, offered through a free consumer chat app, open-weight downloads and a paid developer API. The Qwen3 series is released under the Apache 2.0 license on Hugging Face. Key capabilities of Qwen Qwen Chat: free chat app at chat.qwen.ai Qwen3 open-weight models: sizes from 0.6B to 235B-A22B under Apache 2.0 Coder models: such as Qwen3 Coder Plus Vision-language models: such as Qwen3-VL Plus Developer API: through Alibaba Cloud Model Studio, formerly DashScope Self-hosting: download weights and run on your own hardware How Qwen works Consumers chat in the app or on the web at no charge. Developers call models through the Model Studio API, billed per token, or download open weights from Hugging Face and run them locally. Third-party gateways also resell access. Who uses Qwen? Everyday users, developers and enterprises wanting multilingual, coding and vision models with an open-weight option. Qwen pricing The Qwen Chat app is free. API use is billed per token through Alibaba Cloud Model Studio, and third-party reports cite roughly 1 million free tokens per model for new accounts for 90 days. Check current rates with the vendor. Qwen alternatives ChatGLM, ERNIE Bot and Tencent Hunyuan are other Chinese model families, while Notion AI and ChatGPT are assistant products. Qwen is notable for open-weight releases.
Capabilities
Deployment
What is Jan? Jan is a free, open-source desktop application that works as a ChatGPT alternative. It lets you run open models locally on your computer or plug in online models, and it is developed in public on GitHub. Key capabilities of Jan Local models: Download and run open models on your own machine Cloud models: Connect to OpenAI, Anthropic, Google, Mistral and other providers Open source: Built in public, with 44.7K GitHub stars Model hub: 123 models published on Hugging Face by the project Jan Agent: An agent application alongside Jan Desktop Memory: A context retention feature that the vendor lists as upcoming How Jan works You install Jan Desktop, then either download an open model such as Llama, Gemma, Qwen or DeepSeek and run it locally, or add an API key for an online provider such as OpenAI, Anthropic or Google. Conversations happen in a chat interface on your machine. The vendor lists 6.8 million downloads and a Discord community of 15,000+ members. Who uses Jan? Jan appeals to developers and privacy-minded users who want to run models offline, and to people who prefer one interface for both local and cloud models. Because it is open source, teams can inspect and modify it. Jan pricing Jan is free and open source. There is no paid plan listed on its site, though connecting a cloud provider means paying that provider separately for API usage. Jan alternatives Alternatives include Fellou and Dia, which are AI-first browsers, MaxAI, a browser extension assistant, and MultiOn, which automates web tasks. Jan differs by focusing on local, offline model use in an open-source desktop app.
Deployment
What is Atlassian Rovo? Atlassian Rovo is Atlassian's generative AI product for unlocking organizational knowledge by connecting work across teams and tools. It builds on the Teamwork Graph to give context-aware help. Key capabilities of Atlassian Rovo Cross-tool context: Pulls context across people, projects and code Rovo agents: Agents handle next steps to keep workflows moving Proactive help: Suggests, drafts and surfaces help before you ask Admin control: Admins control what AI can access Connectors: Connects data from external apps Search and chat: Finds answers across connected content How Atlassian Rovo works Rovo indexes work in Atlassian products and connected apps through the Teamwork Graph, which links people, projects and content. Users query it through search and chat or use agents that complete workflow steps, within permissions set by admins. Who uses Atlassian Rovo? Teams on Jira, Confluence and related Atlassian products. Atlassian shows customers including Sprout Social, Reddit and Cisco. Atlassian Rovo pricing Atlassian mentions a premium edition but the page reviewed did not list prices. Rovo access is tied to Atlassian plans, so check Atlassian for current packaging. Atlassian Rovo alternatives Alternatives include Notion AI for workspace knowledge, Glean for enterprise search, and Slack AI for chat-based summaries.
Deployment
Compliance
What is Pi? Pi is a personal AI companion AI agent offering a supportive, conversational personal AI from Inflection AI built for friendly everyday chats. Founded in 2022 and based in Palo Alto, California, USA, Pi helps individuals seeking a conversational AI automate personal AI companion work and get results faster. Key capabilities of Pi Empathetic conversation Voice chat Advice and brainstorming Free access Cross-app actions Personal context memory How Pi works Pi takes text and audio as input and produces text and audio. It is powered by Inflection models, with the vendor managing prompts, models and updates. It connects to tools such as Gmail, Google Calendar, Slack and Notion, so the agent works inside existing workflows. Who uses Pi? Pi is built for individuals seeking a conversational AI. It suits teams that want empathetic conversation and voice chat without adding headcount, while keeping people in control of review and final decisions. Pi vs ChatGPT Pi is often compared with ChatGPT. Pi stands out for empathetic conversation and advice and brainstorming. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
Deployment
Compliance
What is ChatGLM? ChatGLM is a bilingual Chinese and English AI assistant from Zhipu AI, offered to consumers as Zhipu Qingyan at chatglm.cn. It is built on the GLM family of models, some of which are released as open weights. Key capabilities of ChatGLM Chat assistant: Conversation, writing and question answering in Chinese and English Document reading: Upload documents and ask questions about them Image understanding: Vision models accept images alongside text Coding help: GLM models are used for code generation and coding agents Agentic tools: Agent-style task execution in the Zhipu product line Developer API: GLM models are available through Zhipu and Z.ai APIs How ChatGLM works You type a prompt, optionally attach files or images, and a GLM model generates text or code. Developers can call the same model family through the API, and some GLM models are published with open weights for self-hosting. Outputs should be reviewed by the user before use. Who uses ChatGLM? ChatGLM is used by Chinese-language consumers who want an assistant on web and mobile, and by developers and enterprises building on GLM models through the API or open weights. ChatGLM pricing The consumer assistant is described by third-party reviews as free with a daily quota and optional paid membership. Zhipu has published free API models such as GLM-4.7-Flash and GLM-4.5-Flash, with paid models billed per token. Check the vendor for current rates. ChatGLM alternatives Alternatives include Qwen from Alibaba, Doubao from ByteDance and Microsoft Copilot. ChatGLM is distinguished by the open-weight GLM model family from Zhipu AI.
Deployment
What is Scrapybara? Scrapybara is a computer use sandboxes AI agent offering virtual desktops and sandboxes that let AI agents operate computers and browsers. Founded in 2024 and based in San Francisco, California, USA, Scrapybara helps developers building computer-use agents automate computer use sandboxes work and get results faster. Key capabilities of Scrapybara Remote desktop instances Computer-use agent SDK Browser and code tools Scaling Developer SDKs Open-source components How Scrapybara works Scrapybara takes screen and text as input and produces actions. It is powered by Anthropic, OpenAI (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as Python, TypeScript, OpenAI and Anthropic, so the agent works inside existing workflows. Who uses Scrapybara? Scrapybara is built for developers building computer-use agents. It suits teams that want remote desktop instances and computer-use agent SDK without adding headcount, while keeping people in control of review and final decisions. Scrapybara vs E2B Scrapybara is often compared with E2B. Scrapybara stands out for remote desktop instances and browser and code tools. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
Deployment
Compliance
AI assistants are general-purpose, LLM-powered helpers that answer questions, draft content, automate tasks, and work across your tools and knowledge, for individuals and teams. This guide explains what AI assistants are, how they work, what matters, and how to choose one.
AI assistants are general-purpose, LLM-powered helpers that answer questions, draft content, automate tasks, and work across your tools and knowledge, for individuals and teams. This guide explains what AI assistants are, how they work, what matters, and how to choose one.
An AI assistant is a conversational, LLM-powered tool that helps with a broad range of work: answering questions, writing and summarizing, analyzing documents, and increasingly taking actions across connected apps and data.
Unlike narrow single-task bots, assistants are general-purpose and extensible, grounded in your knowledge, connected to tools via integrations, and often customizable with custom instructions, skills, or agents.
The category spans consumer and enterprise assistants, embedded copilots inside productivity suites, and platforms for building custom assistants over company knowledge. Buyers weigh capability and accuracy, knowledge grounding, data security, and integration with their stack.
A user asks a question or gives a task in natural language; the assistant uses an LLM, optionally grounded in your documents and connected apps, to respond, generate content, or take actions, maintaining context across the conversation.
Platforms combine one or more LLMs, retrieval grounding over your knowledge, tool/app integrations, and controls for permissions and safety, often with customization via instructions, custom assistants, or workflows.
Individuals and teams connect knowledge sources and apps, configure custom assistants and permissions, and use the assistant for daily work, with admins governing access, data, and usage.
Answer questions, draft and edit content, summarize, and brainstorm across any topic or task.
Ground responses in your documents and data so answers are accurate and company-specific.
Connect to your apps to retrieve information and take actions, not just chat.
Create tailored assistants with custom instructions, knowledge, and capabilities for specific roles.
Work with text, documents, images, and increasingly voice and data files.
Permissions, data controls, and audit for safe team and enterprise deployment.
A single assistant accelerates writing, research, analysis, and everyday knowledge work.
Grounded assistants answer from company knowledge so people find information fast.
Connected assistants take actions across apps, reducing manual steps.
Custom assistants tailor capabilities to teams and workflows.
Bring answers and actions into one interface instead of juggling apps.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| General-purpose assistants | Broad Q&A, writing, analysis | Any | Versatile, easy to adopt | Generic without grounding |
| Enterprise knowledge assistants | Answers grounded in company data | Mid-market to enterprise | Accurate, secure, governed | Setup and data connection |
| Embedded copilots | AI inside productivity/SaaS suites | Any | In-context, no switching | Tied to one ecosystem |
| Custom assistant platforms | Build tailored assistants/agents | SMB to enterprise | Fit to specific workflows | Requires configuration |
Technology: Technology teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Healthcare: Healthcare teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Financial Services: Financial Services teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Retail & E-commerce: Retail & E-commerce teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Education: Education teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Professional Services: Professional Services teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Manufacturing: Manufacturing teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Media: Media teams use AI assistants to answer questions from company knowledge, draft and analyze content, and automate tasks across their tools, with governance over data and access.
Test on your real tasks and questions; evaluate response quality, reasoning, and grounding accuracy.
Confirm it can connect to and accurately answer from your documents and data.
Check connections to your apps and whether the assistant can take actions, not just chat.
Verify data controls, permissions, no-training guarantees, and admin/audit features.
Assess custom assistants, instructions, and workflow building for your roles.
Understand per-seat or usage pricing and how it scales across the team.
Assistants are evolving into agents that complete multi-step tasks across apps with oversight.
Deeper, secure grounding in company knowledge is making answers accurate and trustworthy.
Multimodal and voice interfaces are broadening how people work with assistants.
Buyers should prioritize capability and grounding accuracy, integrations and actions, strong security and governance, and customization.
An AI assistant is a general-purpose, LLM-powered tool that helps with a broad range of work, answering questions, writing and summarizing, analyzing documents, and increasingly taking actions across connected apps. Unlike narrow task bots, assistants are extensible and can be grounded in your knowledge and customized with instructions and skills, available as consumer tools, enterprise assistants, embedded copilots, and custom-build platforms.
Chatbots are typically built for a specific purpose like customer support and a defined audience. AI assistants are general-purpose helpers for a wide range of tasks, often for internal users, with broader capabilities, knowledge grounding, app integrations, and customization. The line is blurring as both grow more capable, but assistants emphasize versatility and action across tools.
Enterprise assistants can connect to and ground answers in your documents, wikis, and data so responses are accurate and company-specific rather than generic. This requires secure integration and proper permissions, confirm how the assistant retrieves, secures, and controls access to your knowledge before connecting sensitive data.
Reputable enterprise assistants offer encryption, SSO, role-based permissions, audit logs, and no-training guarantees on business data. Security varies widely, especially between consumer and enterprise tiers, so confirm data handling, residency, and governance controls before deploying across a team.
Increasingly, yes. Through app integrations, assistants can retrieve information and perform actions, creating records, sending messages, updating systems, and agentic assistants can complete multi-step tasks. The depth of integration determines what's possible, so verify the specific actions and apps you need are supported.
Assistants are built on large language models, and some platforms let you choose among models or use the latest frontier models. Model choice affects capability, speed, and cost. If model flexibility matters, confirm which models are available and whether you can select or switch them.
Common models are per-seat subscriptions, usage-based (tokens or messages), or as features within productivity suites, often with tiers for advanced models, knowledge grounding, and admin controls. Estimate your team size and usage, and weigh grounding and governance features alongside cost.
Prioritize capability and accuracy on your real tasks, knowledge grounding, integrations and the ability to take actions, security and governance, customization, and pricing. Decide whether you need a general assistant, an enterprise knowledge assistant, or a custom-build platform, and trial it on real workflows before rolling out.