Hero Image


Rapid Retrieval-Augmented Generation system

Let AI do the heavy lifting on your data. We call it Xtraction

scroll down next block

Create your first AI employee: A subject matter expert

Xtraction is a fast-track Retrieval Augmented Generation (RAG) system: gravity9’s answer to deriving insights from data.

gravity9 have pre-built a number of internal components which can be assembled to support use cases big and small, usually ones that consist of thousands of unstructured documents and hundreds of possible questions asked against them. Our approach to Retrieval-Augmented Generation is a stepping stone into the world of Artificial Intelligence, one that we aim to make accessible to organizations big and small.

Timeframe: 4-6 weeks

The Problem Retrieval Augmented Generation Solves

Xtractionis a rapid solution to the problem of deriving reliable, usable insight from data. Xtraction creates a foundational Retrieval-Augmented Generation system in approximately 6-8 weeks, often leveraging MongoDB Vector Search. The solution consumes a wide volume of structured and unstructured data – such as documents, PDF scans, and written content — and enables insights to be extracted when queried by a user. Xtraction is best used to answer questions that are difficult for humans due to the high volume and low organization of data. Over time, Xtraction systems can be optimized to support commonly encountered queries and can scale upwards without heavy compromise to the technology.

Xtraction Explained

The Xtraction offering spends time understanding the specific customer problem, determines the components in scope, and examines how data exists for use.

The solution assembles pre-built pipelines and models to enable data ingestion and classification, while creating needed infrastructure on cloud-hosted platforms. Custom development effort is applied to suit the specific use case, helping to encircle the in-scope components and optimized for usage.

Data sources are organized and fed into the learning model to vectorize content and to support the search process. A first version user interface enables querying of the system; one that can be enhanced to the desire of the customer. A training and refinement period follows to enable common queries to be optimized.


A system hosted in a customer’s cloud infrastructure which consumes a subset of data, enabling responses to customer queries. This platform can be expanded to support greater volumes of data, different types of data, and different channels of data. A user interface is built to enable human interaction with the system.

I want to talk about Xtraction

Yes, we know it’s a web form, but we promise it will be a member of our AI team who responds to you.
Unlock the power of intelligent data retrieval with our cutting-edge RAG AI offering. Accelerate your journey to actionable insights and innovation.

Contact Us

"*" indicates required fields