Welcome to the Nexla blog! We’re happy to debut our new blog and logo just in time for the AT&T Developer Summit and CES. We want to make it easier for companies to be able to collaborate with data, and for data engineers to do their jobs faster and with ease. We aim to simplify the complexity of data operations in the machine learning age. We wanted our logo to reflect this commitment to simplicity.
We wanted our new logo to feel fresh, friendly, and human. We chose a clean and clear sans serif font for our company name so that it’s human-readable. Our mark represents data streams crossing, turning corners, in an elongated “N” formation.
We chose the colors blue and orange for our mark because they’re the most intense and something of a hallmark of modernity. They’re also the most complimentary- they bring out new qualities in each other. We believe the same thing happens when you enrich data from different sources. Some theorists believe the color blue represents trust while orange represents friendliness. At Nexla, we seek to be your trusted partner in data operations automation in an accessible, friendly way. For these reasons, orange and blue was the natural choice.
On the Nexla blog, we’ll be writing on all things data operations in the machine learning age. To keep up to date, enter your email to receive an update with every post.
Forbes Highlights Nexla’s Role in Solving Financial Software Challenges
In the News: Forbes recognizes how Nexla defines and enables automated data mapping—matching data fields across systems to reduce errors and fragmentation. By applying AI and machine learning to integration, Nexla helps make financial data flows more accurate and resilient.
In the News: Nexla CTO Amey Desai shares how Nexla supports MCP, enabling secure, AI-native data integration through Nexsets and agentic orchestration without compromising standards.
Data Variety: The Silent Killer of AI — And How to Conquer It
In the News: Nexla CEO Saket Saurabh explains how data variety—differences in formats, sources, and structures—is a major reason AI initiatives fail to scale. You’ll learn about modern strategies like virtual data products and agentic integration.