With the rise of LLM-powered applications, traditional keyword-based search often falls short in terms of context and relevance. I’ve been exploring AI search engines that go beyond string matching to actually understand query intent — tools that leverage embeddings, semantic relevance, or API-based integration with LLMs.
I recently came across Exa, which seems to be purpose-built as an AI search engine for developers. It lets you search using embeddings, fetch clean content, and even find semantically similar pages via API. Has anyone tried this or similar tools like Perplexity, Andi, or Metaphor?
What AI search engines are you using or experimenting with in your projects?
What features matter most to you (speed, API access, ranking quality)?
Any downsides you’ve experienced?
Would love to hear what the DL community recommends for deep research or building smarter AI agents!
In 2025, the landscape of AI search engines has evolved rapidly, offering developers and researchers powerful tools to access, interpret, and apply complex data in real time. These next-gen platforms go far beyond basic search—they understand context, summarize academic content, and even assist in coding or decision-making.
Among the most promising AI search engines in 2025 are:
Perplexity AI – Known for its real-time web access and citation-backed answers, it’s ideal for both academic and technical research.
You com (YouPro) – A developer-friendly engine that integrates AI assistants, coding tools, and AI-generated summaries.
Andi Search – Focuses on privacy and natural-language interaction, great for clean, distraction-free information delivery.
Meta AI Search – Still emerging, but offers integration with Meta’s vast LLMs and open-access research datasets.
Scite.ai – Specialized for scientific and academic research, offering citation context and paper credibility scores.
For businesses and startups in AI app development, these tools help accelerate model training, gather market insights, and validate research. An experienced AI development company will often leverage such platforms during discovery, prototyping, and optimization phases.
Whether you’re refining algorithms or training neural networks, these AI-powered search engines support deeper analysis, faster data gathering, and smarter problem-solving—making them essential for innovation in both academia and enterprise.
Here are some of the most promising AI-powered search engines and tools for developers and researchers in 2025.
1. Perplexity AI: A conversational AI search engine offering Deep Research and “AI agent” browsing via its Comet browser. It performs iterative searches across web and academic sources and synthesizes detailed summaries with citations.
2. Consensus: AI-powered academic search engine grounded in evidence-based synthesis over 200M+ scholarly papers. Offers “Consensus Meter” to visualize agreement or debate among studies.
3. Elicit (by Ought): A semantic search assistant for literature review and research exploration. Finds relevant papers without exact keywords, and extracts key findings with summarization.
4. SciSpace (formerly Typeset.io): All-in-one assistant for summarizing, managing citations, writing support, and interacting with PDFs via “Chat with Papers” feature. Includes Deep Review dialog mode.
5. Paperguide: Automated AI platform for literature synthesis, citation-backed Q&A, multilingual support, and reference management.