Assistant
Inform Online 2022 – Sinequa Neural Search
Neural Search Demo
Deep Learning Enterprise Search Without Model Training
Sinequa Neural Search delivers one of the most advanced enterprise search engines available today. By combining cutting-edge deep learning language models with powerful NLP and proven statistical techniques, it enables employees and customers to find information faster and extract insights that drive smarter decisions.
Key Benefits of Sinequa Neural Search
-
Unmatched search relevance that understands context, intent, and nuance
-
Easy deployment with no model training required
-
Simple tuning for rapid optimization
-
High speed and efficiency even at enterprise scale
-
Zero setup or curation needed to start delivering value immediately
Watch Jeff Evernham’s demo to learn what makes neural search different, the use cases that benefit most, and how this next-generation capability transforms information discovery.
Watch the video
About This Demo
Traditional enterprise search matches keywords. Neural search understands meaning. In this demo recorded at Inform Online — Sinequa’s global user conference — Chief Product Officer Jeff Evernham walks through Sinequa Neural Search: what it is, how it works, and why it delivers a fundamentally different level of search relevance for enterprise knowledge workers.
The capability Jeff demonstrates in this video is the same deep learning foundation that powers Sinequa’s current RAG (Retrieval-Augmented Generation) and agentic AI platform — making this a critical watch for anyone evaluating how enterprise AI search actually works under the hood.
What Neural Search Does Differently
Keyword-based enterprise search has a well-documented failure mode: it returns documents that contain the right words but miss the point entirely. A researcher asking about “compound efficacy in oncology trials” gets back every document containing those words — not the ones that actually answer the question.
Neural search solves this by encoding the semantic meaning of both the query and the document content into vector representations, then ranking results by conceptual similarity rather than term frequency. The result is search that understands what the user is trying to find — not just what words they used to ask.
Sinequa’s approach goes further by combining neural search with its proven NLP and statistical retrieval techniques into a hybrid model — capturing the precision of deep learning alongside the reliability of traditional enterprise search. Neither alone is sufficient for the complexity and scale of Global 2000 information environments.
What Jeff Covers in the Demo
- How neural search works — without the PhD required Jeff explains the deep learning language model architecture behind neural search in terms that make sense for enterprise technology evaluators: how transformer models encode semantic meaning, why this produces better relevance than keyword matching, and where it outperforms traditional approaches.
- No model training required One of the most common objections to deploying AI-powered search in the enterprise is the effort required to train models on proprietary data. Sinequa Neural Search requires no model training, no setup, and no curation to start delivering value — it works immediately against existing enterprise content.
- The use cases that benefit most Not every search query benefits equally from neural search. Jeff identifies the use cases where the relevance improvement is most significant — complex natural language queries, cross-domain knowledge discovery, expert finding, and research workflows where keyword search consistently fails to surface the right result.
- Enterprise scale and speed Deep learning models are computationally expensive by nature. Jeff demonstrates how Sinequa delivers neural search relevance at enterprise scale — across hundreds of millions of documents, for hundreds of thousands of users — without compromising query speed.
Neural Search as the Foundation for Enterprise RAG and Agentic AI
Neural search is not a standalone feature — it is the retrieval layer that determines whether GenAI and agentic AI systems produce reliable outputs in enterprise environments. When a GenAI assistant answers a question, it is only as accurate as the documents retrieved to ground its response. When an AI agent executes a multi-step research task, it is only as trustworthy as the search that surfaces relevant knowledge at each step.
This is why Sinequa’s investment in neural search capability — demonstrated in this video — is directly connected to its current agentic AI platform. The deep learning retrieval quality Jeff demonstrates here is what makes Sinequa’s RAG grounding accurate enough to deploy in production for organizations like Pfizer, AstraZeneca, and Siemens, where a hallucinated answer is not an inconvenience but an operational and compliance risk.
Frequently Asked Questions (FAQ)
Keyword search matches terms in a query against terms in documents — it finds pages that contain the right words. Neural search encodes the semantic meaning of both the query and the document into vector representations using deep learning language models, then ranks results by conceptual similarity. The practical difference is significant: neural search surfaces documents that answer the question, even when they use different vocabulary than the query. For enterprise knowledge workers asking complex, natural language questions across large, heterogeneous document collections, this relevance improvement is measurable and immediate.
Neural search is the retrieval layer in a Retrieval-Augmented Generation (RAG) architecture. When a GenAI assistant receives a question, it retrieves relevant documents before generating a response — and the accuracy of the retrieval directly determines the accuracy of the answer. Neural search improves this retrieval step by finding conceptually relevant documents rather than just keyword matches, which means the GenAI layer is grounded in more relevant information and produces more accurate, useful responses. This is why Sinequa’s neural search capability is the same foundation deployed in its current RAG and agentic AI platform.
Neural search delivers the greatest improvement over keyword search in complex, natural language query scenarios: research workflows where users ask questions rather than entering search terms; cross-domain knowledge discovery where relevant information uses different vocabulary across disciplines; expert finding where the relevant expertise is described in varied ways across documents; and compliance or regulatory review where thorough recall — finding all relevant documents, not just those with exact keyword matches — is operationally critical. These are the use cases Jeff Evernham focuses on in this demo.
Share
