We engineer AI with industry-specific requirements in mind, from regulatory and compliance considerations to data security, operational workflows, and customer expectations. The goal is AI that creates business value while meeting the standards your industry demands.
Pick the one that matters most to you, and we'll point you to the industry page where we've written about handling it in production.
No. These are the patterns we see most often and the constraints we've built the most repeatable expertise around, not a limit on what we take on. If your industry isn't listed, tell us what you're working on and we'll tell you plainly whether it's a fit.
From what actually blocks or breaks AI projects in that domain: regulatory review cycles in fintech and healthcare, data quality and format variance in logistics, real-time accuracy expectations in retail, integration into an existing codebase without downtime for SaaS, and physical environment variables in manufacturing. Each industry page goes into the specific engineering implications.
The underlying engineering (retrieval systems, evaluation harnesses, monitoring, integration patterns) transfers directly. What doesn't transfer automatically is the domain-specific constraint: audit requirements, data sensitivity, uptime expectations. We treat that constraint as a first-class input to the architecture, not an afterthought, whichever industry it comes from.
These are the patterns we see most often, not a limit on what we build. Tell us what you're working on.