We are dataops experts

This is the most important, and often messiest, step in any data program, and we are fast and good at it. We can also introduce modern software development practices so that your staff can properly test pipelines before they go live in production.

We can help you move your data workflows to public or private cloud tech stacks. We like old systems because they contain well-understood, valuable insights, and if you move them to cloud delivery models it gives you a great platform to accelerate your data program.

We have experience with big data platforms and can help build, optimize or manage a big data platform so that it securely and seamlessly integrates with analysts' tools and workflows.

Our experienced, senior data analysts can take on projects so you can accelerate your program. We have experience with data operations in both large and small companies.

We understand how to deliver on established platforms like Tableau, Microsoft 365 Graph API (Excel, PowerBI), AirTable, JasperReports and BIRT. We can also formulate a modern, real-time approach to results delivery.

We can help you build a scalable machine learning platform that doesn't break the bank. We build the infrastructure so your analysts can easily deploy highly parallelized algorithms like monte carlo simulations, k-means clustering, k-nearest neighbors and more.

We can integrate open LLMs such as Llama2-Code-Llama to help your data analysts generate code without anything ever leaving your cloud.

Complex, production-facing systems indicate problems long before failure. Because we've done this for so long we have cool ideas around materialization that strike a good balance between the needs of production-debug and trend analysis without costing a fortune. (Logging and metrics often are a top cloud account expense.)

We can help you build APIs that deliver real-time analytics back to production systems that meet performance and security requirements. Have an idea for a fraud signal? We can help you integrate that into production.

With GitOps your analysts simply commit their work to a git branch and it deploys to either dev or prod depending on the branch. Now you have total auditability of what is running and who deployed it. Rollback is simply a git revert, and you can rebuild an entire environment with one command (or replicate an environment for disruptive development testing).

We can provide a way for analysts to stop on a breakpoint in their local Pycharm against an Airflow DAG running in the cloud. This is incredibly transformative, because it's almost impossible to recreate data pipeline conditions in test, and the weirdest stuff seems to always happen in the pipelines. You get to more robust pipelines faster.

Your data contains PII and trade secrets, but often analysts download it to their laptops because the data tools setup makes it hard to work with the data safely. We can fix this so the analyst can work in a way that is not only safer, but much easier.