A patient newly diagnosed with stage III lung cancer or low-risk prostate cancer lands on your radiation oncology site in the worst week of their life. They have already heard from the surgeon and the medical oncologist. Now they need to understand what radiation actually does, why your center offers SBRT in five fractions instead of IMRT in 28, and whether their Medicare Advantage plan will cover the IGRT setup. A generic chatbot gives them the Wikipedia version, which doubles their confusion.

SleekAI maps your real content. Treatment pages on IMRT, VMAT, SBRT, SRS, brachytherapy, low dose rate seeds, and proton therapy live in wp_posts with structured postmeta for fractionation, typical session length, immobilization requirements, and supported cancer sites. Equipment pages describe your TrueBeam, Versa HD, Halcyon, or proton gantry with the exact specs. Physician bios tag radiation oncologists by site focus: thoracic, GU, breast, head and neck, CNS, GI, pediatrics. The model sees this on every reply, so a question about lung SBRT gets your real protocol with your physician's name.

The conversation also has a clear funnel. Patients want to know fractionation, total treatment time, side effects, insurance, and the consult timeline. SleekAI lets you bake that funnel into presets so the chat moves a worried patient from confusion to a consult booking in a few exchanges, with the right oncologist for their site and your nearest center.

Radiation oncology is the most technically complex of the cancer specialties, and patients arrive on your site already overwhelmed. They have just been told they have cancer. They have already met the surgeon and the medical oncologist.

Now they need to understand a modality whose names alone are intimidating: IMRT, VMAT, SBRT, SRS, IGRT, HDR, LDR, proton. A generic chatbot makes this worse by hedging every answer with disclaimers, paraphrasing the Wikipedia entry, and never quite naming the doctor or the center that would handle their case. SleekAI replaces that with structured local data on every reply.

Your modalities, your linacs, your oncologists, your trials, your coverage notes. The model gets all of it as input, so a patient asking about lung SBRT learns about your TrueBeam, your thoracic oncologist, your 5-fraction protocol, and your Medicare coverage in one calm conversation. That is the front door radiation oncology has always needed.

Conversation logs then show you which sites drive the most curiosity, which referring specialties send the most chats, and which trial pages convert to coordinator contacts. Use that to shape outreach, hire site-focused staff, and decide where to put the next linac. The chatbot becomes a quiet strategic instrument, not just a website widget.