vector database

emno takes on the heavy lifting so
you can focus on building the next-gen AI applications       


Building with emno

Designed for speed, simplicity, and effortless scalability.

Connect Data

Simply connect your data source (or use our in-built data importers) to bring data into emno.      

Get fast, relevant results

Call emno’s APIs to query your data from anywhere in the world. Results are served from the closest location to your users and application.

No third step!

Congratulations - you just saved hundreds of hours of engineering effort. emno handles generating embeddings, chunking, optimizing, entity extraction, and more.


🚀 Rocket fuel for your AI Applications

On the Edge
Faster access to your data. Your data is served from the closest point to your users and app.
Heavy Lifting
From chunking and embedding your text data to query optimization, context retrieval, and augmented generation — emno does it all.
Fast, relevant results
Speed through data in milliseconds. Leverage emno’s out-of-the-box configurations and filters, ensuring quick and accurate results across diverse search tasks.
Developer Friendly
Easy-to-use APIs with SDKs for developers to get started. You can run emno on your machine and sync data directly to our cloud-services whenever you are ready.
No-Code Friendly
Building a No-code application on Bubble? emno’s Bubble plugin simplifies adding and using a vector database in your app.
Fully managed
Launch, use, and scale your AI solution without needing to maintain vector database infrastructure, monitor services, or troubleshoot algorithms.
No Lock-in
Export your data anytime you want. emno supports exporting to SQL, CSV, and Parquet file formats.

Simplifying vector management

Automated processes for vector operations

Creating and Storing Vectors

In traditional workflows, extracting and vectorizing textual data from different sources is a time taking multistep process.

process of creating and storing vectors in traditional workflows.

emno streamlines your data pipeline by auto-segmenting text and transforming it into optimized vectors, which are then efficiently stored in the vector database, optimizing your time and resources.

optimized process of creating and storing vectors with emno
// get collection
const collection = await emno.getCollection('your_awesome_collection');

// add text to collection
const addedVectors = await collection.addText([content: 'your_long_form_text_content' }]);

Querying Vectors

Typically, querying involves creating search text, segmenting it, vectorizing the segments, and then searching the database—a multi-step process.

process of querying vectors in traditional workflows.

With emno, just input your query text. emno automatically handles the rest, converting your text into vectors and quickly retrieving accurate results from the vector database.

optimized process of querying vectors with emno.
// get collection
const collection = await emno.getCollection('your_awesome_collection');

// query text in the collection
const queryResultsVectors = await collection.queryByText({
  content: ['your_long_form_query_text'],
  topK: 10,

Start building with emno today

Try emno for as long as you like with our free Hacker plan. Then upgrade easily when it's time to scale.