Saurabh RaiFrom searching multiple apps at once without any ETL to a fully functioning SAAS. Let's explore some Search Engines and Platforms this time!
Why does cross-platform search matter?
Because finding information in the enterprise is challenging: data is scattered across technologies, applications, and platforms. Studies have shown that the average user spends more time gathering and searching for information than actually analyzing it.
This fragmentation makes it difficult to retrieve necessary information quickly and forces users to deal with multiple different interfaces.
That’s why unified data access is essential — it provides users with one interface to all their data, reducing the time spent learning new technologies and simplifying the retrieval process.
Search will be the future of AI
Addressing data fragmentation is why search is poised to become the future of AI. (And AI is most useful when it embraces search!)
AI search engines boost productivity by finding information and presenting it in an easily understandable and actionable format. They utilize advanced natural language processing to interpret queries more effectively, allowing users to interact with the search engine in a context-aware, conversational style. This results in a more effective and intuitive search experience, significantly enhancing productivity beyond what traditional search engines can offer.
Traditional search finds lots of information but forces the user to wade through it to find the best information. AI Search picks up much of the information load by summarizing documents and helping you interactively refine your search.
How AI Search Improves Insights and Actions
Even after finding data, generating meaningful insights from it is difficult. This slows down decision-making and impedes timely actions.
AI-powered solutions can help overcome these challenges.
- Unifying Data Access: AI search tools combine data from diverse sources and intelligently remove duplicates, rank results, and provide summaries, making it easier to find the information you need.
- Providing Valuable Insights: These tools assist in decision-making by answering questions, summarizing data, performing comparisons and more — helping do the things that slow down analysts and prevent them from being productive.
- Enhancing Productivity and Outcomes: Reading a 50 page PDF to answer a single question is wasteful. Changing that is exactly the productivity game-changer AI represents. Now imagine that you have to answer 50 questions, from 50 PDFs.
Top AI Search Engines to Boost Productivity
AI Search sounds good, but how can I take advantage of it?
SWIRL
SWIRL is a powerful open-source AI infrastructure platform designed to run securely on-premises.
It’s intuitive chat and search capabilities enable users to perform natural language queries across multiple data sources without moving or copying data to the cloud or into yet another specialized database. SWIRL can handle arbitrarily large amounts of data.
SWIRL provides over 100 data connectors, including those for popular enterprise apps like Microsoft 365, Slack, and SQL/NoSQL databases, along with support for numerous AI providers such as ChatGPT, Claude, and more.
Users can search only the data they are authorized to access, ensuring maximum privacy. Enterprise users who need more advanced capabilities can take advantage of SWIRL’s enhanced security measures for even greater control.
Swirl doesn’t require you to shift your data into a database or a central repository. Regulated industries like banking and financial services can utilize Swirl for real-time data.
GitHub Repo Link: Swirl on GitHub
Sinequa
Sinequa offers an AI-powered search and analytics platform focused on enterprises with vast data sets. It is designed to index and make searchable a wide range of data types from various sources.
Sinequa is not open-source. It comes with a proprietary license. It also functions primarily as a cloud-based or hybrid solution, integrating with numerous enterprise systems for secure data retrieval.
Sinequa centralizes your data into a single repository before enabling you to search or generate insights. This might concern some industries where moving data into a vendor’s cloud can result in security risk, and in places where real-time data is important because data upload and ETL take time.
Glean
Glean is an enterprise AI search platform designed to integrate with various apps and services. It provides access to over 100 pre-built connectors and a robust enterprise knowledge graph to help users find relevant information across scattered data sources.
Glean also supports AI functionalities like vector search, personalized results, and integration with popular large language models (LLMs) such as OpenAI GPT.
Glean is not open-source but primarily a SaaS solution that relies on cloud services.
Perplexity
Unlike the other three, Perplexity AI focuses more on being a conversational search assistant. It leverages AI to provide immediate answers to user queries by retrieving information from the web and other public datasets.
It is not as specialized for enterprise environments as SWIRL, Glean, or Sinequa and does not offer the extensive enterprise data connectors or deep integrations typical in business environments.
Perplexity AI is also not open-source and is primarily designed for real-time web-based question-answering rather than enterprise-specific search needs.
Perplexity has an enterprise version though it doesn’t have the provision to search siloed data or any apps.
So in tl;dr, this is what the comparison looks like:
AI is making search useful again. And as I stated in the previous post search will be the future of AI. Read about it below.
Join our SWIRL Community
SWIRL is open-source and built in Python.
Watch this video on how to set up SWIRL. Then, join our Slack community and ping me. Let’s build an amazing open-source AI platform together.
