SaaS

SaaS teams live with a specific kind of pressure: every new customer has to be cheaper to serve than the last one, but the roadmap never stops growing. The architecture decisions made in year one — tenancy model, billing integration, how permissions are structured — tend to still be load-bearing in year five, for better or worse.

The problems we see most often aren't feature gaps. They're structural: a single-tenant data model that was fine at 20 customers and is now a migration project at 200; an internal admin tool that was never built because the product tool was always more urgent; usage-based billing bolted onto a system that was designed for flat monthly plans; support and onboarding teams doing by hand what should be self-service.

AI changes what's newly practical here too — not as a bolt-on chatbot, but as a way to cut the cost of support, onboarding, and internal operations that scale linearly with customer count today. A knowledge assistant trained on your own docs can resolve a meaningful share of support tickets before a human ever sees them. Document and data ingestion pipelines can turn "customer sends us a spreadsheet" into a supported workflow instead of a manual one-off.

We work with SaaS teams on the parts that are hard to unwind later: multi-tenant data architecture, subscription and billing logic, internal tooling that scales with headcount instead of against it, and the AI-assisted support and onboarding flows that keep cost-to-serve flat as the customer base grows.