The frameworks we use to engineer revenue.

Submit a URL and the audit runs against four independent layers. These include classical conversion which focuses on load speed, form friction, primary CTA visibility, and trust density. We also look at intake including response time, channel coverage, and after-hours behavior. Traditional SEO is analyzed for technical health, schema, internal linking, and topical authority. Finally, we assess LLM-readiness to see whether large language models can extract clean, citable answers from your pages.

Each layer is scored on a 0–100 scale with a written rationale, and we attach a prioritized punch list. This list highlights what to fix first, what to fix in the next sprint, and what is structural enough to require a rebuild. The scoring rubric is published in full, and nothing is hidden behind a sales-team gate.

Roughly a third of operators who run the audit end up booking a strategy call. The other two-thirds take the report to their internal team and ship the fixes themselves. Both are good outcomes for us. The second group becomes a future referral source, while the report becomes evidence for the first group's CFO when they decide to hire.

The single highest-leverage operational improvement in healthcare growth is response time. Inbound leads contacted within 60 seconds convert at three to five times the rate of leads contacted within an hour, and the curve falls off a cliff after the first business day. Most multi-location practices respond in 4 to 24 hours.

The playbook walks through the exact architecture. This includes an AI front-line agent that answers in seconds via voice, SMS, web chat, and form follow-up. It also covers a clean handoff to a human when intent crosses a defined threshold, HIPAA-aware data handling, and a CRM write that closes the loop back to the original ad or organic source so attribution does not break.

It also covers the change-management piece, which is where most rollouts fail. We discuss how to introduce 24/7 AI intake without alienating front-desk staff, how to script the agent to match a specific brand voice, and how to handle the edge cases like insurance verification, scheduling conflicts, and callback preferences that make or break trust.

ChatGPT, Gemini, and Perplexity are now the first stop for a meaningful share of high-intent healthcare research. Unlike the ten blue links in Google, large language models pick a handful of sources, synthesize them, and cite. If your site is not in the citation set, you are invisible, even if you rank well in classical search results.

The framework documents what we have learned from instrumented LLM rank tracking across hundreds of healthcare queries. We look at the structural signals that drive citation like clean H-tags, factual specificity, schema, original data, and named authors. We also examine content patterns that get extracted as direct answers and the entity-graph work that makes a brand legible to the underlying retrieval models.

It is not a checklist of hacks. It is a content architecture that also happens to perform well in classical search and improves on-page conversion. The same page that earns a Perplexity citation is usually a page that converts a logged-in visitor too, because clarity is the shared underlying property.

Every framework on this site assumes one underlying piece of infrastructure. This is a closed-loop attribution layer that ties an ad spend dollar, an organic visit, or an LLM citation to a booked appointment, a procedure type, and recognized revenue. Without it, every other system is a guess.

The data-model write-up covers the schema. This includes a unified events table, conformed dimensions for source, campaign, channel, and creative, deterministic identity stitching where consent allows, and a privacy posture that is comfortable with HIPAA, state laws, and platform policies. It also walks through the dashboard discipline, helping identify which questions a CMO should be able to answer in under 30 seconds and which ones are red flags when they take longer.

Treat this as the system of record. Once it exists, the rest of marketing stops being a debate about taste and becomes a series of resourcing decisions against known unit economics.

Every Rocklane engagement is structured as a 30-day install rather than a perpetual retainer dressed up as strategy. The playbook describes exactly what happens in each of the four weeks, what artifacts are produced, what your team is expected to contribute, and how the system is handed off into steady-state operation.

Week one covers discovery and instrumentation to get the analytics and intake telemetry honest before anyone touches creative. Week two focuses on architecture and copy including site structure, primary service-line pages, intake scripts, and paid-media account scaffolding. Week three involves the build and integration with AI intake live, ad accounts launched, and dashboards wired. Week four is for optimization, training, and the formal handoff.

After day 30, we either move into a defined optimization retainer or we do not. There is no auto-renew on a vague monthly fee, and there is no scope creep dressed as account management. The install is the install.

Start with the audit. It will tell you within 24 hours which layer of the revenue stack is the bottleneck for your specific practice. From there, the corresponding playbook tells you what good looks like, and the related service page describes how we would build it if you decide not to do it yourselves.

If you are evaluating us against another agency, we recommend a direct comparison. Ask the other firm for their 30-day install plan, their attribution data model, their LLM citation methodology, and their AI intake script library. If they cannot produce written equivalents, you are comparing engineering to advertising, and the difference will show up in your P&L within a quarter.

If you ever want a second pair of eyes on a draft, an internal RFP, or a vendor proposal, feel free to write to us. We answer.