Technology
Technology companies have more data than most industries and often less internal infrastructure to make real use of it. We help software and tech firms build the data platforms, AI foundations, and cost-visibility tooling that engineering and product teams actually rely on.
What we get right in technology.
Internal data platforms
Product telemetry, usage analytics, and business metrics in a single governed layer, not twelve competing dashboards nobody trusts.
AI productionization
Take the proof-of-concept your team shipped and turn it into something with evals, observability, cost budgets, and a path to ownership. The boring infrastructure that makes AI a product feature.
Cloud cost visibility
Azure spend that reflects workload, not historical accident. Chargeback models, rightsizing, and the data to defend allocation decisions when Finance calls.
What we get asked to build.
- Product analytics and usage platforms
- AI/ML productionization and evaluation frameworks
- Internal developer tooling and observability
- Cloud cost allocation and optimization
- Build a data platform for product telemetry and feature usage analytics
- Stand up Azure ML pipelines for rapid model iteration and deployment
- Implement cloud cost chargeback by team, product, or environment
- Build evaluation harnesses and observability for production AI features
What we bring to technology.
We build what engineers actually use
Technology companies are the most demanding internal users of their own data platforms. We build product telemetry, usage analytics, and engineering metrics that engineering and product teams rely on, not just report on.
AI productionization is our core
Turning a model from a proof of concept into a production feature is most of the hard work. Evals, observability, cost budgets, and deployment pipelines: we have built this infrastructure and know what breaks in production that does not break in a notebook.
Cloud cost visibility for technical teams
We build the cost attribution and chargeback models that engineering teams and Finance can both use. The CFO needs a number. The engineering team needs to understand which workloads are expensive and why.
Internal tooling that compounds
Improvements to CI/CD pipelines, testing infrastructure, and observability tooling pay back on every future sprint. We build the internal developer tooling that makes engineering teams faster over time, not just for the current project.
What clients typically see.
Doing similar work in technology?
Tell us what you are trying to change. We will either be useful, or point you to who would be.