Best Neural Search Software in Australia

Build natural language understanding and generation into your product with a few lines of code. The Cohere API provides access to models that read billions of web pages and learn to understand the meaning, sentiment, and intent of the words we use. Use the Cohere API to write human-like text by completing a prompt or filling in blanks. You can write copy, generate code, summarize text, and more. Compute the likelihood of text and retrieve representations from the model. Use the likelihood API to filter text based on chosen categories or selected criteria. With representations, you can train your own downstream models on a wide variety of domain-specific natural language tasks. The Cohere API can compute the similarity between pieces of text, and make categorical predictions by comparing the likelihood of different text options. The model has multiple lenses through which to view ideas, so that it can recognize abstract similarities between concepts as distinct as DNA and computers.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Users can easily build recommendation applications on Vespa. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

Sinequa provides intelligent enterprise search connecting workers in the digital workplace with the information, expertise and insights they need to do their jobs. It handles vast and heterogeneous data volumes, offering security and compliance even in the most complex environments. Enabling employees to get relevant information & insights speeds up innovation and optimizes responsiveness to customers. Organizations powered by intelligent search enable people to do their jobs better, resulting in significant cost savings. Delivering insights to employees within the context of their work provides the transparency and speed needed to comply with regulations on a timely basis and mitigate financial and reputational risk. Sinequa’s Neural Search provides the most sophisticated engine for discovering enterprise information assets available on the market today.

Vectara is LLM-powered search-as-a-service. The platform provides a complete ML search pipeline from extraction and indexing to retrieval, re-ranking and calibration. Every element of the platform is API-addressable. Developers can embed the most advanced NLP models for app and site search in minutes. Vectara automatically extracts text from PDF and Office to JSON, HTML, XML, CommonMark, and many more. Encode at scale with cutting edge zero-shot models using deep neural networks optimized for language understanding. Segment data into any number of indexes storing vector encodings optimized for low latency and high recall. Recall candidate results from millions of documents using cutting-edge, zero-shot neural network models. Increase the precision of retrieved results with cross-attentional neural networks to merge and reorder results. Zero in on the true likelihoods that the retrieved response represents a probable answer to the query.

Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually the graph requires locking during indexing, which cause stop-the-world. But Vald uses distributed index graph so it continues to work during indexing. Vald implements its own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery.

Zeta Alpha is the best Neural Discovery Platform for AI and beyond. Use state-of-the-art Neural Search to improve how you and your team discover, organize and share knowledge. Make better decisions, avoid reinventing the wheel, and make staying in the know effortless: the power of modern AI to make an impact with your work faster. With state-of-the-art neural discovery across all relevant AI research and engineering information sources. Ensure that nothing falls through the cracks with a seamless combination of powerful search, organization, and recommendation features. Steer decision-making across the organization and reduce associated risks by maintaining a unified view of relevant internal and external information. Get a clear overview of what your team is reading and working on.

Zevi is a site search engine that leverages natural language processing (NLP) and machine learning (ML) to better understand the search intent of users. Instead of relying on keywords to produce the most relevant search results, Zevi relies on its ML models, which have been trained on vast amounts of multilingual data. As a result, Zevi can deliver extremely relevant results regardless of the search query used, thus providing users with an intuitive search experience that minimizes their cognitive load. In addition, Zevi allows website owners to provide personalized results, promote particular search results based on various criteria, and to use search data to make informed business decisions.

A true second brain for knowledge work. Orchard is a conversational AI assistant that understands complex requests and cites itself with your knowledge. Orchard Classic is still the best AI text editor for editing. Ask questions about your documents, wherever they live. Neural search across your docs + synthesis with AI = the best way to learn from your own work. A text editor that finishes your sentences and suggests related ideas, informed by your institutional knowledge. AI text editing is now contextually aware. We want Orchard to be a personal analyst that understands you and your work. Each time you make a request, Orchard determines whether and how to use what it knows about you. It's like if ChatGPT cited its sources with resources relevant to your work. Orchard can also break down complex tasks more reliably than ChatGPT. Orchard builds a search engine for all of your data. We are actively integrating Orchard with businesses.

Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.

Access countless systems and corporate documents, regardless of platform, and keep track of millions of pieces of data. State-of-the-art neural search techniques combined with enterprise search functionality and numerous standard connectors enable a completely new search experience. INTERGATOR Cloud can be hosted by a German hoster and thus comply with the strict requirements of German and European law (especially data protection). We grow with your requirements. INTERGATOR Cloud can easily be scaled whenever you need more or less search. Search your company data from anywhere in the world and get information without complex VPN solutions. With the help of Natural Language Processing (NLP) and neural networks, models are trained that extract essential information from data and documents and consider the information stock in its entirety. You receive a comprehensive solution for up-to-date information and knowledge management.

Build a natural language interface for your data. NLP is at the core of modern enterprise data processing. We provide developers with the right tools to build production-ready NLP systems quickly and efficiently. Our open-source framework for scalable, API-driven NLP application architectures. We believe in sharing. Our software is open source. We value our community, and we make modern NLP easily accessible, practical, and scalable. Natural language processing (NLP) is a branch of AI that enables machines to process and interpret human language. In general, by implementing NLP, companies can leverage human language to interact with computers and data. Areas of NLP include semantic search, question answering (QA), conversational AI (chatbots), semantic search, text summarization, question generation, text generation, machine translation, text mining, speech recognition, to name a few use cases.

Empower businesses and developers to create cutting-edge neural search, generative AI, and multimodal services using state-of-the-art LMOps, MLOps and cloud-native technologies. Multimodal data is everywhere: from simple tweets to photos on Instagram, short videos on TikTok, audio snippets, Zoom meeting records, PDFs with figures, 3D meshes in games. It is rich and powerful, but that power often hides behind different modalities and incompatible data formats. To enable high-level AI applications, one needs to solve search and create first. Neural Search uses AI to find what you need. A description of a sunrise can match a picture, or a photo of a rose can match a song. Generative AI/Creative AI uses AI to make what you need. It can create an image from a description, or write poems from a picture.

With Jina Search, you can search for anything in seconds - faster and more accurately than any traditional search engine. Our AI search captures all the information stored in images and text, providing you with the most comprehensive results. Unlock the power of search and revolutionize the way you find what you're looking for with Jina Search. In this example, not all items on the dataset had the correct label, making it impossible for Classical Search to retrieve relevant results. Since Jina Search doesn't rely on tags, was successful on finding better items. Take full advantage of using state-of-the-art ML models that are optimized to work with multiple modalities of data, such as images and text while maintaining all your Elasticsearch customization. This means you don’t need to annotate each image in your dataset with labels, Jina Search will automatically understand the image and store it accordingly.

The end to end platform for research. Instantly retrieve and wrangle the 
insights you need, no matter your source
 of unstructured data. Uncover answers across millions of public sources, like SEC Filings, Earnings Calls, and expert network transcripts, or leverage your firm's knowledge. Hebbia instantly hooks into any source of unstructured data in your organization, ingesting any file type or API. Tooling for diligence and research processes lets you work faster, no matter the task. Spread financials, find public comps, or structure unstructured data with the a single button click. The world's largest governments and financial institutions trust Hebbia with their most sensitive data. ‍ Security is at our core. Hebbia is the first and only encrypted search engine on the market.

Improve your embedding metadata and embedding tokens with a user-friendly UI. Seamlessly apply advanced NLP cleansing techniques like TF-IDF, normalize, and enrich your embedding tokens, improving efficiency and accuracy in your LLM-related applications. Optimize the relevance of the content you get back from a vector database, intelligently splitting or merging the content based on its structure and adding void or hidden tokens, making chunks even more semantically coherent. Get full control over your data, effortlessly deploying Embedditor locally on your PC or in your dedicated enterprise cloud or on-premises environment. Applying Embedditor advanced cleansing techniques to filter out embedding irrelevant tokens like stop-words, punctuations, and low-relevant frequent words, you can save up to 40% on the cost of embedding and vector storage while getting better search results.