How Aurecon's Team of 7,500 Employees Use Nomic to Understand and Build With Their Data in One-Tenth of the Time

I leverage Nomic's collaborative capabilities to enhance my data, from simple deduplication processes to large-scale tagging of specific data points. The most significant improvement for me personally has been using Nomic to better explain the data that powers our AI systems. This has helped my stakeholders gain a clearer understanding of the bigger picture and has been particularly beneficial in areas such as risk management. By unlocking data explainability, Nomic has driven improvements in quality, cost savings, and data democratization across Aurecon. It has effectively brought our subject matter experts and domain specialists closer to the data and equipped them with new tools and capabilities to power data-centric outputs.

For example, I was able to assemble a large dataset to fine-tune a model—something that was previously beyond reach and far outside my usual tasks. Similarly, a leading bridge engineer was able to create a Nomic map to curate client feedback and requirements, despite lacking the technical expertise to interpret the data in previous data workflows. This demonstrates that Nomic not only unlocks new capabilities for our team but also motivates both senior leadership and data/software engineers to make decisions with data at the core.

I believe Nomic has boosted my team's productivity across technical and non-technical members.

Personally, Nomic has revitalized my excitement for being back in the midst of data work, making it something I look forward to rather than dread.

Overall, it's clear that without Nomic, our investment in leveraging generative AI for our customers would have been significantly more challenging.

Nomic is gradually becoming our primary backend for search and a key component in our synthetic generation pipelines. For general users at Aurecon, it offers a low-code solution to empower our team with embedding models and data curation. For our application layer, the Nomic Platform streamlines our tech stack and provides a consistent framework for building tools around our data.

Nomic has enabled more advanced approaches to synthetic data generation and, in cases of training task-specific models, has facilitated the involvement of domain experts through curation and feedback. This has resulted in increased productivity, the development of novel workflows, and improved user experience while reducing complexity in our workload.

Nomic represents a significant advancement in how we organize, retrieve, and derive value from our existing project knowledge and domain-specific data. Its ability to integrate large-scale, disparate content—or even just the metadata associated with that content—into a Nomic data map provides a new way to grant access and support our team in their work without needing to reinvent the wheel. This efficiency allows our experts to focus more on innovation and delivering high-impact results, rather than spending time sifting through tons of data.

Nomic instantly resolves the long-standing issue of information silos across teams. Previously, accessing critical knowledge required knowing the right people or having specific details about the information sought. Take, for instance, a recent project where we needed to consolidate hundreds of separate engineering documents into a new standard. Nomic transformed this process by eliminating the tedious manual "copy and paste" tasks and integrating key information, expertise, and collaboration capabilities into a unified platform.

With Nomic, teams can swiftly surface and utilize data, driving productivity and improving the bottom-line for our team. This gives us the ability to focus on what truly matters—delivering groundbreaking solutions that elevate our clients' success.

Previously, working with, visualizing, or deriving useful insights from text data was incredibly challenging and labor-intensive. However, with Nomic, the process has been streamlined, eliminating much of the guesswork and enabling us to effectively analyze text datasets to tackle large-scale problems. This has significantly reduced our workload, fostered creativity, accelerated insights, and minimized downtime when datasets change.

For example, I recently presented a Nomic map as a proof of concept to a team. They could immediately understand how the problem could be addressed using Nomic and LLMs. The insights were clear and easy to communicate. Unlike before, where explaining how our insights would translate into value required extensive time, the team was able to interact with the Nomic map interface and instantly grasp its potential.