AI Design & Integration

Our AI design position was earned in the hardest room available: a clinical system. Pulse Clinic ships an assistant whose result explanations come from a curated glossary and a deterministic flagging engine, whose triage runs on explicit rules where red flags cannot be talked down, and whose note drafting is fenced by redaction going out and grounding checks coming back, so every number in a generated draft must exist in its source. Half of those AI features deliberately use no model at all, and users cannot tell, because the experience of intelligence does not require generation. That judgment, knowing where the model belongs and where it does not, is the actual service.

The design layer matters as much as the plumbing. Chat is usually the wrong interface: we design inline actions attached to the objects users already work with, structured inputs that make the feature discoverable, streaming states that tell the truth about progress, and review surfaces where output is accepted, edited, or discarded before it becomes record. For agents, the same thinking becomes permission tiers and preview queues, because software that acts needs consent design, not just capability.

Integration follows the feature, not the hype: retrieval grounding where answers must come from your content, deterministic engines where truth is fixed, models where synthesis genuinely helps. Everything ships with the failure modes designed: the wrong answer, the empty retrieval, the refusal that routes to a human. If your AI feature cannot fail gracefully, it is not finished.