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Marqo is an end-to-end vector search engine that bundles embedding generation and vector retrieval into a single system, so developers do not have to run a separate model to create embeddings. You add documents or images and query in natural language, and Marqo handles vectorization, indexing, and search, including multimodal use cases that combine text and images. This all-in-one design reduces the plumbing needed to build semantic search. Marqo is aimed at teams building search and recommendation features who want fewer moving parts than a typical embed-then-store pipeline. It offers open-source deployment and Marqo Cloud, supports custom and fine-tuned models, and focuses on relevance for real-world search, including e-commerce product search where combining images and text matters. Its tensor-based approach targets high-quality retrieval out of the box.
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Vespa is an open-source platform for building large-scale AI-powered search and recommendation systems. Originally developed at Yahoo, it combines vector search, full-text search, and structured filtering with machine-learned ranking and inference, all in a single engine that serves queries with low latency over billions of documents. Vespa Cloud offers a managed version for teams that do not want to operate clusters themselves. Vespa is chosen by organizations with demanding search, recommendation, and RAG workloads that outgrow simpler vector databases. It supports real-time indexing, tensor computation for ranking, and complex queries mixing semantic and keyword signals. Its ability to run inference inside the serving layer makes it powerful for personalized ranking and hybrid retrieval at scale.
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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.
