What is your startup about? (Your founder’s story)
At engAIge, we are transforming the way organizations process and analyze unstructured data and documents. In a world where everything is interconnected, we believe that documents should be treated the same way—with links, relationships, and hierarchical structures reflecting their real-world complexity. Currently, we are focused on the financial services industry, where we apply our deep domain knowledge to help clients manage vast volumes of critical documents. Our platform not only streamlines document processing but also extracts valuable insights by understanding how documents relate to one another.
Our journey began with a simple vision: to create a solution that treats documents as part of a larger ecosystem rather than isolated files. This vision quickly grew into a powerful platform that helps companies navigate complex document management challenges. By integrating cutting-edge technologies, we ensure that our clients can efficiently process and make sense of their interconnected data.
Who can best benefit from adopting your solution?
Our solution is particularly well-suited for industries dealing with high volumes of complex, interrelated documents. Financial services, legal sectors, and healthcare providers are some of the industries that can greatly benefit from our approach. In financial services, for instance, documents often reference one another—contracts, regulatory filings, and financial reports are all part of a broader network. Our platform makes it easier to manage these documents in a way that preserves their inherent connections, enabling users to access, process, and analyze them more effectively.
Any organization facing challenges in managing document-heavy workflows or trying to derive insights from a large pool of unstructured data will find our solution valuable. We’re helping them reduce manual effort, avoid errors, and speed up decision-making processes by leveraging document relationships and hierarchies.
What made you decide to implement Neo4j for your startup?
As we built engAIge from the ground up, it was essential to find a database solution that would give us the flexibility and scalability to manage complex document relationships. Neo4j stood out because of its ability to model relationships natively in a graph format. This was perfectly aligned with our core objective of capturing document interconnections.
Additionally, Neo4j’s seamless integration with our existing tech stack, which includes Python, FastAPI, and Microsoft Azure, made it an ideal choice. The fact that Neo4j is available in the Azure Marketplace as a one-click deployment further simplified the process of getting it up and running. This made deployment easier, quicker, and more secure, ensuring we could focus on building features rather than managing infrastructure.
How did Neo4j fit into your existing tech stack or workflows?
Neo4j fit naturally into our workflow, complementing the tools we were already using. By integrating it with our Python-based backend and FastAPI framework, we were able to create a seamless flow for querying and processing document relationships. Azure, being our cloud platform, provided a secure environment where Neo4j could be deployed and managed efficiently.
The flexibility Neo4j offers allows us to scale our document processing solutions as our clients' needs grow. Whether it’s handling thousands or millions of documents, Neo4j enables us to perform highly efficient queries on large datasets, making it easy to find patterns and connections that would otherwise be difficult to uncover in traditional database models.
Have any tips you want to share with the community?
One of the strengths of Neo4j is its schema-less nature, which provides flexibility. However, we learned early on that it’s important not to take this flexibility for granted. Initially, we added nodes and relationships without much structure, and while this allowed us to move quickly, we later found that our queries became more complex and less efficient as our data grew.
Our advice to the community is to invest time in designing a thoughtful schema upfront, even though Neo4j is schema-less. Treat it like you would a relational database, where proper planning leads to better long-term performance. By continuously refining your data model, you’ll avoid slow queries and the need for extensive testing and debugging down the line. Planning for scalability from the start will pay off as your data grows and your application scales.
