Craig AI

The problem

Multi-location clinics were drowning their front-desk teams in manual work: paper-and-portal patient intake, endless phone tag for scheduling, and re-keying the same data across systems. Intake alone took 12 minutes per patient, and no-shows and scheduling errors leaked revenue every week.

The clinic didn't need another portal. It needed the repetitive work to simply stop landing on a human.

What we built

Craig AI is an AI concierge embedded into the clinic's actual front-desk workflow — not a chatbot bolted onto the website. It:

• Runs patient intake conversationally and writes structured data straight into the clinic's systems
• Handles scheduling, reminders, and rescheduling autonomously to cut no-shows
• Triages and routes front-desk requests, escalating only what needs a human
• Operates with evaluation and guardrails so accuracy is measured, not assumed

The results

| Metric | Before | After | |---|---|---| | Patient intake time | 12 minutes | under 4 minutes | | Front-desk admin time | baseline | down 60%+ | | Patients handled per day | baseline | ~40% more |

The front desk stopped being a data-entry station and went back to being a patient-facing team.

Why it worked

We scoped the highest-leverage moments first — intake and scheduling — instead of trying to "add AI" everywhere. Then we built it into the existing stack and stayed on to monitor and tune it. That is the difference between an AI demo and an AI system a clinic runs on every day.