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Average price: 10 products listed
10 Listings in AI Assistants Available
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Price range
₹1700–₹1700/mo
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9 tools
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What is Mistral? Mistral is open-weight AI models software offering open-weight large language models and an AI assistant (Le Chat) from Mistral AI. Founded in 2023 and based in Paris, France, Mistral helps developers and enterprises wanting open models work more efficiently and achieve better outcomes. Key features of Mistral Open-weight LLMs AI assistant (Le Chat) Developer API and models On-prem and private deployment Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses Mistral? Mistral is built for developers and enterprises wanting open models. It suits teams that want open-weight LLMs without spreadsheets and disconnected tools. Why choose Mistral? Compared with alternatives like Anthropic Claude, Mistral differentiates on open-weight LLMs. Pricing is quote-based and scoped to your usage and team size.
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Deployment
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What is Perplexity? Perplexity is AI answer engine software offering an AI answer engine that responds to questions with cited, up-to-date sources. Founded in 2022 and based in San Francisco, California, USA, Perplexity helps professionals and teams researching with AI work more efficiently and achieve better outcomes. Key features of Perplexity AI search with citations Real-time web answers Research and summarization Enterprise search (Perplexity Enterprise) Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses Perplexity? Perplexity is built for professionals and teams researching with AI. It suits teams that want AI search with citations without spreadsheets and disconnected tools. Why choose Perplexity? Compared with alternatives like ChatGPT Enterprise, Perplexity differentiates on AI search with citations. Pricing is quote-based and scoped to your usage and team size.
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What is Krutrim? Krutrim is Indian AI assistant software offering an Indian AI company offering a multilingual AI assistant, LLMs and AI cloud infrastructure. Founded in 2023 and based in Bengaluru, India, Krutrim helps Indian consumers and developers work more efficiently and achieve better outcomes. Key features of Krutrim Multilingual AI assistant Indic LLMs AI cloud and GPUs Developer platform Analytics and reporting Integrations with Ola Krutrim Cloud, REST APIs, Python SDK and more Who uses Krutrim? Krutrim is built for Indian consumers and developers. It suits teams that want multilingual AI assistant without spreadsheets and disconnected tools. Why choose Krutrim? Compared with alternatives like Sarvam AI, Krutrim differentiates on multilingual AI assistant. Pricing is quote-based and scoped to your usage and team size.
Deployment
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What is Meta Llama? Meta Llama is open large language models software offering a family of open large language models (Meta Llama) for building AI applications. Founded in 2023 and based in Menlo Park, California, USA, Meta Llama helps developers building on open models work more efficiently and achieve better outcomes. Key features of Meta Llama Open-weight LLMs Wide ecosystem support Self-hostable models Strong performance Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses Meta Llama? Meta Llama is built for developers building on open models. It suits teams that want open-weight LLMs without spreadsheets and disconnected tools. Why choose Meta Llama? Compared with alternatives like Mistral, Meta Llama differentiates on open-weight LLMs. Pricing is quote-based and scoped to your usage and team size.
Deployment
Compliance
What is Langdock? Langdock is enterprise AI workspace software offering a model-agnostic enterprise AI platform with chat, assistants, agents and workflows. Founded in 2023 and based in Berlin, Germany, Langdock helps European companies work more efficiently and achieve better outcomes. Key features of Langdock Multi-model chat Custom assistants AI agents and workflows EU hosting Analytics and reporting Integrations with Microsoft 365, Google Drive, Confluence and more Who uses Langdock? Langdock is built for European companies. It suits teams that want multi-model chat without spreadsheets and disconnected tools. Why choose Langdock? Compared with alternatives like ChatGPT Enterprise, Langdock differentiates on multi-model chat. Pricing is quote-based and scoped to your usage and team size.
Deployment
Compliance
What is DeepSeek? DeepSeek is open AI models software offering open large language models and an AI assistant known for strong reasoning at low cost. Founded in 2023 and based in Hangzhou, China, DeepSeek helps developers and users wanting capable open models work more efficiently and achieve better outcomes. Key features of DeepSeek Open large language models Strong reasoning models Cost-efficient inference Developer API Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses DeepSeek? DeepSeek is built for developers and users wanting capable open models. It suits teams that want open large language models without spreadsheets and disconnected tools. Why choose DeepSeek? Compared with alternatives like Mistral, DeepSeek differentiates on open large language models. Pricing is quote-based and scoped to your usage and team size.
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What is Hebbia? Hebbia is AI for knowledge work software offering an AI platform (Matrix) that answers complex questions over large document sets for finance and legal. Founded in 2020 and based in New York, New York, USA, Hebbia helps finance, legal and research teams work more efficiently and achieve better outcomes. Key features of Hebbia AI over large document sets Multi-step research agents Citations and traceability Finance and legal use cases Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses Hebbia? Hebbia is built for finance, legal and research teams. It suits teams that want AI over large document sets without spreadsheets and disconnected tools. Why choose Hebbia? Compared with alternatives like Perplexity, Hebbia differentiates on AI over large document sets. Pricing is quote-based and scoped to your usage and team size.
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ShopIQ is an agentic AI commerce platform that turns a single prompt into a fully functional online store. Describe your business in a chat and ShopIQ generates the website, product catalog with AI-written titles and descriptions, AI-enhanced images and videos, and a payment-ready checkout, then lets you run orders, inventory, and customer updates entirely from WhatsApp. Built in India for first-time sellers, D2C brands, and manufacturers going direct, it connects Razorpay and PhonePe for payments, sells across marketplaces and ONDC, supports multiple Indian languages, and charges 0% commission on orders, with AI analytics that track sales and recommend what to do next to grow.
Industry Fit
Deployment
What is Anthropic Claude? Anthropic Claude is AI assistant and LLM software offering a family of large language models and an AI assistant (Claude) built for safety and helpfulness. Founded in 2021 and based in San Francisco, California, USA, Anthropic Claude helps individuals, developers and enterprises using AI work more efficiently and achieve better outcomes. Key features of Anthropic Claude Advanced large language models Long-context understanding Coding and analysis Enterprise and API access Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses Anthropic Claude? Anthropic Claude is built for individuals, developers and enterprises using AI. It suits teams that want advanced large language models without spreadsheets and disconnected tools. Why choose Anthropic Claude? Compared with alternatives like ChatGPT Enterprise, Anthropic Claude differentiates on advanced large language models. Pricing is quote-based and scoped to your usage and team size.
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Compliance
What is ChatGPT Enterprise? ChatGPT Enterprise is enterprise AI assistant software offering an enterprise version of ChatGPT (OpenAI) with security, admin controls and advanced models. Founded in 2023 and based in San Francisco, California, USA, ChatGPT Enterprise helps enterprises deploying AI to employees work more efficiently and achieve better outcomes. Key features of ChatGPT Enterprise Enterprise AI assistant Security and privacy controls Advanced GPT models Admin and analytics Analytics and reporting Integrations with Slack, Microsoft 365, Google Workspace and more Who uses ChatGPT Enterprise? ChatGPT Enterprise is built for enterprises deploying AI to employees. It suits teams that want enterprise AI assistant without spreadsheets and disconnected tools. Why choose ChatGPT Enterprise? Compared with alternatives like Anthropic Claude, ChatGPT Enterprise differentiates on enterprise AI assistant. Pricing is quote-based and scoped to your usage and team size.
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Deployment
Compliance
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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.
Tech stacks
See where ai assistants fits in a complete stack, with the other software, AI agents and services each business needs.
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.
