Relevance AI is an end-to-end collaborative platform for analyzing unstructured data for actionable insights.
It makes it easy to connect to data, analyze it using machine learning and share it as interactive apps, dashboards or APIs.
We’re on a mission to help data teams create lightbulb moments from unstructured data, with next to no labelling or algorithm training required. And our secret sauce to do this is vector technology.
A pleasure to meet you!
A no-code builder to quickly build and share visualizations, interactive apps and reports.
- Interactive visualizations designed for unstructured data and application of AI and machine learning techniques
- Built for insights and usability to be actionable for anyone
- Share and collaborate easily with comments and access controls
Highly scalable and flexible infrastructure to store, query and analyze unstructured data through an API.
- Managed end-to-end from data to value, hiding all the complexities such as data transformation to vectors, management of data warehouses + vector database and more
- Integrates flexibly with existing data warehouses (e.g. Snowflake, Bigquery, ...) and vector databases (e.g. FAISS, HNSWLib, Elasticsearch, ...)
A library for further experimenting with vectors and unstructured data for even deeper analysis.
- Easily integrate with HuggingFace, SentenceTransformers, etc to extract vectors from unstructured data
- Apply AI algorithms (such as clustering, nearest neighbours, dimensionality reduction, ...) on vectors and data for actionable insights
- Integrates well with Pandas, Sklearn and more
- Create and update apps from a few lines of code
- Also serves as an SDK to the API.
Whilst the market is full of tools to help you find and communicate insights from structured data, our key competitive advantage is the ease and speed at which anyone can unlock insight and get deeper context from different data types such as images and text (think emails, tweets, PowerPoints, PDFs, surveys, support tickets, conversations and more). Analysts do not need to manually label data, they can connect the raw data and go from 0 to 70% in minutes. Our platform is:
- Highly flexible and integrated for the whole lifecycle
Relevance AI's prebuilt workflows analyze unstructured data with often over 80-90% effectiveness. Our flexible platform allows optimization by substitution of models designed by your data team
- Managed and designed for a great experience
Relevance AI is designed with the customers in mind. A platform designed for a great user experience to get the most impactful actionable insights along with specialized support
- Powered by R&D
Relevance AI does not just connect and facilitate each part of the process of analyzing unstructured data. It enhances it further with algorithms developed through lots of R&D
- Constantly innovating
There are variety of use cases applicable to unstructured data, all require new visualizations, algorithms and infrastructure. Relevance AI's R&D and engineering teams are constantly upgrading the platform to improve data analysis and insight extraction.
The only platform for unstructured data insights with all the following in one place.
- Domain agnostic and applicable to different use cases (e.g. customer feedback, project operations, service and support operations)
- Vector embedding engine
- Clustering engine
- Semantic search engine
- Sentiment analysis engine
Vector embedding enginee
Our secret sauce!
Extract the hidden trend and themes across your data.
Search across intent, meaning and context rather that words.
The sentiment (positive, negative or neutral) behind what is written.
Who might churn? What are the most common topics of complaints/approval?
What are the entries that have to do with communication issues? No matter phone calls, emails, SMS, follow-ups, etc.
Are my client coming back?
Relevance AI makes the journey easy no matter what team you are in:
- Don't know programming, stick to our friendly dashboard
- Keen to code, our full guided SDK is in your hand with a single installation https://sdk.relevance.ai
Vectors in general are defined as a list of numbers such as [1,2,3,4,5]. However, in AI vectors are not just a list of numbers but a list that represent concepts via the numbers. This means, when mapping words to vectors, words that are conceptually close to one another are mapped to vectors with small distance from one another. Such vectors are generated by neural network models that are trained on huge sources of data and for variety of tasks. So, working with vectors frees us from the need to provide knowledge sources to a system. The knowledge is already embedded within the vectors. Read more about vectors at What are vectors.
There are many applications for Relevance AI across an organisation from customer feedback data to operational notes.
Analyse all unstructured data in one platform and provide visibility to the health of the business. Automate 70% of the pain that comes with data labelling and training AI models.
Check out our dashboards on Uber's customers comments and reviews of
Search within concepts rather than words. Check out our sample search dashboards.
The general flow from data to insight on Relevance AI's platform is shown in the image below. It consists of signing up, uploading data, vectorizing, analysis (e.g. clustering) and insights.
For more information on the workflow, please visit the General Flow page.
Updated 13 days ago