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33 Listings in AI Search Available
What is Andi? Andi is a generative AI search engine that returns conversational, sourced answers instead of a page of links. It positions itself as privacy-focused and ad-free, and a business and API offering exists alongside the consumer search. Key capabilities of Andi Direct answers: synthesizes information from multiple sources into one response Sourced responses: shows where the information came from Web Q&A: question answering over live web results Text generation: assists with writing and content creation Ad-free search: no ads or user monitoring, per the vendor positioning Search API: Developer and Team tiers reported by third parties How Andi works You type a question and Andi searches the web, then composes a conversational answer with sources. Third-party coverage says search is free with unlimited queries and no account required, and PCMag named it Best Free AI Search Engine in March 2026, a third year of recognition. Who uses Andi? General users who want direct answers without ads use it, and developers and businesses can use the API. Teams and companies are directed to a separate business page on the vendor site. Andi pricing The vendor page did not render pricing for us. Third-party sources report search is free with no account, and an API with a free Developer tier including $5 in credit and a Team tier at $49 a month with $50 in monthly credits. Andi alternatives Alternatives include Perplexity AI for cited search, Phind for developer search, Onyx for enterprise search, Credal for enterprise AI, and Needl.ai for workplace search.
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What is Felo AI? Felo AI is a multilingual AI search and creation platform that lets users search, understand and create with AI. It expands questions across languages, searches current sources and synthesizes results. Its tools include LiveDoc, agents, an LLM Playground, slides creation and API access. Key capabilities of Felo AI Cross-language search: expands queries across languages LiveDoc: AI document workspace Slides: generates presentations from research Agents: agent capabilities for tasks LLM Playground: try multiple models API access: programmatic search How Felo AI works You enter a question in your language, Felo searches sources across languages, synthesizes an answer and lets you turn results into documents or slides. Pro unlocks more professional searches per day and extra models. Who uses Felo AI? Researchers, students and professionals who work across languages use Felo AI. Felo AI pricing Felo AI has a free plan, and the Pro plan is listed at $14.99 per month or $149.99 per year. The free plan has daily limits on professional searches. Felo AI alternatives Felo AI is compared with Phind, Genspark and Onyx. Phind targets developers, Genspark builds agentic search and pages, and Onyx is workplace search.
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Saaskart Market Grid™
Explore how leading AI Search 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 Search ecosystem.
Category Leader
Perplexity AI
#1 in AI Search
Best Value AI Search
Exa
From $4/mo
Trending
Phind
Most viewed
Market Insights
Derived from live Saaskart marketplace data, engagement, reviews, and pricing for this category.
Live Rankings
Tech stacks
See where ai search fits in a complete stack, with the other software, AI agents and services each business needs.
What is Typesense? Typesense is an open-source search engine for developers that returns instant, typo-tolerant results and supports vector and semantic search. Key capabilities of Typesense Typo tolerance: Fuzzy matching returns results despite spelling errors. Vector search: Semantic search by meaning, with built-in RAG. Geo-search: Filters and sorts by location. Faceted navigation: Facets, grouping, dynamic sorting and synonyms. High availability: Built-in replication across nodes. Multi-tenant keys: Scoped API keys per tenant. How Typesense works Typesense keeps its index in memory for speed, exposes a REST API and ships client libraries for JavaScript, Python, Go, PHP, Ruby, Swift, Java, Dart and .NET. You can self-host it or use Typesense Cloud, which provides a dedicated cluster with no limits on records or operations. Who uses Typesense? Developers building site search, ecommerce catalogs and app search who want an alternative to Algolia, Pinecone or Elasticsearch. The vendor says the platform handles 10B searches per month. Typesense pricing The software is open source and free to self-host. Typesense Cloud is priced by cluster configuration (memory from 0.5 GB to 1024 GB, vCPUs, optional high availability and search delivery network), with no starting price listed. Typesense alternatives Meilisearch is another open-source search engine, Algolia is a hosted search service, and Kagi is a paid consumer search engine. Typesense pairs open source with hosted clusters.
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What is Brave Leo? Brave Leo is a private browser assistant AI agent offering Brave private AI assistant built into the browser for summarizing pages and answering questions. Founded in 2023 and based in San Francisco, California, USA, Brave Leo helps Brave browser users automate private browser assistant work and get results faster. Key capabilities of Brave Leo Page summaries Private by design Model choice Bring your own model Cited sources Follow-up questions How Brave Leo works Brave Leo takes text and web pages as input and produces text. It is powered by Multiple models (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as Web browsers, Chrome, iOS and Android, so the agent works inside existing workflows. Who uses Brave Leo? Brave Leo is built for Brave browser users. It suits teams that want page summaries and private by design without adding headcount, while keeping people in control of review and final decisions. Brave Leo vs Microsoft Copilot Brave Leo is often compared with Microsoft Copilot. Brave Leo stands out for page summaries and model choice. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Devv? Devv is an AI product for developers that started as a search engine for coding questions and now markets itself as the first AI coding agent for building full-stack AI products. Key capabilities of Devv Full-stack generation: Builds front end, back end, data layer and authentication from a description. Module selection: Chooses and integrates modules based on your requirements. Management interface: Visual view of tables, permissions and users. Developer search: Answers coding questions using documentation, GitHub and Stack Overflow. Source links: Search answers include clickable sources. How Devv works You describe the product you want, and the coding agent selects modules, generates front-end and back-end code, configures the database and sign-in, and provides a visual interface to inspect tables and users. Its earlier search mode combined language models with real-time data from Stack Overflow, GitHub and developer documentation, citing sources for each answer. Details come from third-party sources, since the homepage showed only its tagline. Who uses Devv? Indie builders, small teams and non-technical creators who want to ship AI-powered products quickly, plus developers who use its search for programming questions. Devv pricing Pricing could not be confirmed from the vendor page. Confirm plans and any free tier in the product. Devv alternatives Phind is a developer-focused AI search, Perplexity AI is a general answer engine, and Duck.ai is a privacy-focused chat. Devv now overlaps with app-building agents.
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What is Komo? Komo is an AI search engine that combines conventional search with AI chat in an ad-free interface. Its site shows a new-search view, a history or library of past searches, a Firsthand section and an Earn section. Key capabilities of Komo AI answers: returns generated answers to search queries Chat mode: supports longer discussions and brainstorming Explore: surfaces community conversations Search history: keeps a library of earlier searches Ad-free interface: positions itself as a private, ad-free alternative Follow-up questions: continues a query as a conversation How Komo works You type a query and Komo returns an AI-written answer, or you switch to chat or Explore for open-ended discussion and community content. Past queries appear in the history section. Model names and detailed privacy terms were not visible on the site pages we could read, so check them in the account area before relying on them. Who uses Komo? Researchers, students and general web users looking for an ad-free AI search alternative try it. Because the vendor pages we could read disclosed little detail, evaluate it on your own queries before adopting it for work. Komo pricing The vendor pricing could not be confirmed. Third-party reviews describe a free basic plan and paid tiers reported at about $30 a month (Essential) and $200 a month (Business), with a custom Enterprise tier, but these reports are dated and unverified. Komo alternatives Alternatives include You.com for multi-model AI search, Exa for AI-native search APIs, Andi for conversational search, Brave Leo for a browser-integrated assistant, and Genspark for agent-style search.
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What is Onyx? Onyx is an open-source AI search and chat platform that connects to a company's apps so teams can ask questions and build custom AI agents grounded in internal knowledge. It offers a chat and search interface, access to major LLMs, and developer APIs. Key capabilities of Onyx Unified chat and search: one interface over connected company data 40+ app connectors: pulls from tools such as Google Drive, Confluence, Slack, Notion, SharePoint and GitHub Custom AI agents: build agents on your own knowledge Major LLM access: choose among leading models Developer APIs: embed Onyx into other tools Enterprise controls: OIDC/SAML SSO and on-premise deployment How Onyx works Admins connect data sources through connectors, Onyx indexes the content, and users ask questions in chat or search with answers grounded in that indexed material. Agents can be configured for specific tasks. The Enterprise plan adds SSO and on-premise deployment. Who uses Onyx? Teams that want an internal AI assistant over their own documents and tools, including those who need a self-hosted deployment for data control. Onyx pricing The Business plan is $20 per user per month with annual billing and offers a free trial. Enterprise pricing is custom and adds OIDC/SAML SSO and on-premise deployment. Onyx alternatives Credal and Needl.ai are enterprise AI search and governance products, while Brave Search, Genspark and You.com are public web AI search engines rather than internal knowledge tools.
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What is Duck.ai? Duck.ai is DuckDuckGo's private AI chat, described by the company as free. It lets users chat with models from other AI providers while DuckDuckGo anonymizes the requests. Key capabilities of Duck.ai Private chat: requests proxied so your IP address is not shared Model choice: switch between models from several AI providers No training on chats: chats are not used to train models Free access: no account required for the free version Web answers: real-time web access for current questions Plus tier: subscribers get access to additional models How Duck.ai works You type a prompt on Duck.ai and DuckDuckGo forwards it to the model provider through a proxy, so the provider does not see your IP address. You pick a model from the available list. DuckDuckGo says chats are anonymized and not used to train models. Who uses Duck.ai? Duck.ai suits people who want a general AI chat with privacy protections and no sign-up, such as for quick questions, drafting and research. Duck.ai pricing Duck.ai is free to use. DuckDuckGo subscribers on the Plus plan can access additional models, and the Plus price is not listed on the Duck.ai page. Duck.ai alternatives Alternatives include Brave Leo for in-browser AI in Brave, You.com for AI search and Qwant for a privacy-focused European search engine.
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What is Linkup? Linkup is a web search API AI agent offering a web search API built for AI applications that returns grounded, source-cited results. Founded in 2024 and based in Paris, France, Linkup helps AI developers and enterprises automate web search API work and get results faster. Key capabilities of Linkup Search API Premium source access Answer grounding LLM integrations Agent-ready APIs Usage-based pricing How Linkup works Linkup takes text as input and produces text and JSON. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as LangChain, LlamaIndex, OpenAI SDK and Playwright, so the agent works inside existing workflows. Who uses Linkup? Linkup is built for AI developers and enterprises. It suits teams that want search API and premium source access without adding headcount, while keeping people in control of review and final decisions. Linkup vs Tavily Linkup is often compared with Tavily. Linkup stands out for search API and answer grounding. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Raffle? Raffle is an AI search for service AI agent offering an AI search and answers platform for customer service and websites that surfaces answers from company knowledge. Based in Copenhagen, Denmark, Raffle helps customer service teams automate AI search for service work and get results faster. Key capabilities of Raffle Site search Customer self-service Agent knowledge search Insights on questions Knowledge-grounded answers Human handoff How Raffle works Raffle takes text as input and produces text and search results. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Zendesk, Salesforce, Intercom and Freshdesk, so the agent works inside existing workflows. Who uses Raffle? Raffle is built for customer service teams. It suits teams that want site search and customer self-service without adding headcount, while keeping people in control of review and final decisions. Raffle vs Kapa.ai Raffle is often compared with Kapa.ai. Raffle stands out for site search and agent knowledge search. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Google Agentspace? Google Agentspace is an enterprise search and agents AI agent offering Google enterprise platform for AI search and agents across company data and apps. Founded in 2024 and based in Mountain View, California, USA, Google Agentspace helps enterprises on Google Cloud automate enterprise search and agents work and get results faster. Key capabilities of Google Agentspace Enterprise search Gemini-powered agents Agent gallery Connectors Permission-aware answers Enterprise admin controls How Google Agentspace works Google Agentspace takes text and documents as input and produces text and actions. It is powered by Google Gemini models, with the vendor managing prompts, models and updates. It connects to tools such as Microsoft 365, Google Workspace, Slack and Salesforce, so the agent works inside existing workflows. Who uses Google Agentspace? Google Agentspace is built for enterprises on Google Cloud. It suits teams that want enterprise search and Gemini-powered agents without adding headcount, while keeping people in control of review and final decisions. Google Agentspace vs Glean Google Agentspace is often compared with Glean. Google Agentspace stands out for enterprise search and agent gallery. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is You.com? You.com is a search AI agent offering an AI search and research platform with research agents and enterprise APIs. Founded in 2020 and based in Palo Alto, California, USA, You.com helps professionals and enterprises automate search work and get results faster. Key capabilities of You.com AI search with citations Research agents Model choice Web search APIs Cited answers Export to reference managers How You.com works You.com takes text and files as input and produces text. It is powered by Multiple LLMs (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as Zotero, Mendeley, Google Scholar and PubMed, so the agent works inside existing workflows. Who uses You.com? You.com is built for professionals and enterprises. It suits teams that want AI search with citations and research agents without adding headcount, while keeping people in control of review and final decisions. You.com vs Perplexity You.com is often compared with Perplexity. You.com stands out for AI search with citations and model choice. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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AI search (enterprise and semantic search) understands meaning and intent to find answers across documents, apps, and data, and increasingly generates direct, cited answers rather than just links. This guide explains what AI search is, how it works, what matters, and how to choose one.
AI search (enterprise and semantic search) understands meaning and intent to find answers across documents, apps, and data, and increasingly generates direct, cited answers rather than just links. This guide explains what AI search is, how it works, what matters, and how to choose one.
AI search uses embeddings, semantic understanding, and LLMs to retrieve information by meaning rather than keywords, and to generate synthesized, cited answers from across your connected content and applications.
It spans enterprise/workplace search (unified search across company apps and documents), semantic site and product search, and answer engines that return generated responses grounded in sources.
The category has shifted from keyword indexes to retrieval-augmented generation (RAG): find the right content, then synthesize an answer with citations. Buyers weigh answer accuracy and grounding, connector coverage, permissions/security, and relevance quality.
A user enters a natural-language query; the system retrieves semantically relevant content from connected sources (respecting permissions), then either returns ranked results or uses an LLM to generate a direct, cited answer.
Platforms combine connectors to your apps and data, embeddings and a vector index, permission-aware retrieval, and an LLM answer layer with citations and guardrails.
Teams connect content sources and configure permissions and relevance; users search in natural language while admins govern access, monitor quality, and tune connectors and content.
Understand meaning and intent to surface relevant results even without exact keyword matches.
Synthesize direct answers from sources with citations so users get answers, not just links.
Search across documents, wikis, tickets, chat, and business apps from one place.
Respect access controls so users only see content they're allowed to.
Tune ranking and monitor search analytics to improve answer quality over time.
Encryption, access controls, and data governance for sensitive enterprise content.
Semantic search and generated answers cut the time spent hunting across apps and docs.
Unified search surfaces information trapped across tools and teams.
Accurate, cited answers reduce repetitive questions to colleagues and support.
Meaning-based retrieval beats brittle keyword matching for real questions.
Analytics reveal what people look for and where content gaps exist.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| Enterprise/workplace search | Unified search across company apps | Mid-market to enterprise | Breaks down silos | Connector and permission setup |
| Semantic site/product search | Search on websites and stores | Any | Better relevance and conversion | Tuning for catalog/content |
| Answer engines (RAG) | Generated cited answers over content | Any | Direct answers, not links | Grounding quality critical |
| Developer search APIs | Embeddable semantic/RAG search | SaaS and enterprise | Flexible, custom | Engineering effort |
Technology: Technology teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Healthcare: Healthcare teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Financial Services: Financial Services teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Retail & E-commerce: Retail & E-commerce teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Education: Education teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Professional Services: Professional Services teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Manufacturing: Manufacturing teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Media: Media teams use AI search to find answers across documents and apps, power self-service, and surface knowledge by meaning, with permission-aware, cited results.
Test on your content and questions; verify answers are accurate, grounded, and cited.
Confirm connectors for the apps and data sources your knowledge actually lives in.
Verify permission-aware retrieval so users never see content they shouldn't, plus enterprise security.
Assess semantic ranking quality on your real queries, not a demo set.
Check speed and scalability across your content volume and user base.
Understand seat, query, or index pricing and how it scales.
Search is shifting decisively from links to grounded, cited answers and conversational follow-ups.
Agentic search will take actions and complete tasks on top of finding information.
Permission-aware, real-time grounding across all company data is becoming the enterprise standard.
Buyers should prioritize answer accuracy and grounding, connector coverage, permission enforcement, and relevance quality.
AI search uses embeddings, semantic understanding, and LLMs to find information by meaning rather than keywords, and increasingly to generate direct, cited answers from your connected content and apps. It spans enterprise/workplace search across company tools, semantic site and product search, and answer engines that synthesize responses grounded in sources.
Keyword search matches exact terms and returns a list of links. AI search understands meaning and intent, so it finds relevant content even without matching words, and can generate a synthesized answer with citations instead of making users dig through results. This typically improves relevance and dramatically cuts time-to-answer.
Retrieval-augmented generation (RAG) is the core pattern behind modern AI search and answer engines: the system retrieves the most relevant content from your sources, then an LLM uses that content to generate a grounded, cited answer. Grounding answers in retrieved sources is what keeps them accurate and verifiable rather than hallucinated.
It must, for enterprise use. Quality enterprise search enforces permission-aware retrieval so users only see content they're authorized to access, even in generated answers. This is technically complex across many systems, so confirm how the tool handles permissions before connecting sensitive content.
Generated answers can be wrong if poorly grounded, which is why grounding and citations matter. Choose tools that retrieve from your actual content, cite sources you can verify, and constrain answers to what's supported. Test accuracy on your real questions and content before relying on it.
Reputable enterprise tools offer encryption, SSO, permission-aware access, audit logs, and no-training guarantees on your content. Given that search touches sensitive company knowledge, confirm security, data residency, and governance controls before deploying across the organization.
Common models are per-seat subscriptions, usage-based (per query), or by indexed documents/data volume, sometimes with connector tiers. Estimate your user count, query volume, and content size, and check connector coverage and limits when comparing cost.
Prioritize answer accuracy and grounding, coverage of connectors for where your knowledge lives, permission-aware retrieval and security, relevance quality on real queries, latency and scale, and pricing. Run a proof of concept on your actual content and questions, and validate answer accuracy before rolling out.
