SaaS / Vertical SaaS
AI capabilities that belong inside your product: evaluated, observable and integrated with the systems that already run the business.
Reality check
What usually hurts here.
01AI features shipped fast now create support load and quality disputes.
02Model spend grows faster than seats or usage.
03AI-assisted workflows must respect multi-tenant permissions and data boundaries.
04Roadmap pressure competes with making existing AI features actually reliable.
Use cases
Where AI earns its place.
AI features embedded in core workflows (drafting, extraction, summarization).
Reliability and evaluation programs for already-shipped features.
Cost/latency programs ahead of scale or pricing changes.
Migration of AI behavior between models or vendors without regressions.
Technical considerations
01
Multi-tenancy and permissions apply to AI features like any other feature.
02
Unit economics belong on the same dashboard as product metrics.
03
Evaluation suites keep up with product change, not just model change.
Services