I built CherryPickr after running into a simple, expensive truth: most AI content does not rank because it is written first and validated later. The fix was not “better writing.” It was better inputs.
"Ninety days after implementation, organic traffic was up 41%."
CherryPickr flips the flow. We start at the SERP, extract what winners are doing, turn those findings into constraints, then generate content that explicitly exceeds the competitive bar. In the first 90 days across three topic clusters:
The rest of this case study explains why I built it, how it works, what I learned, and what I’d change next.
I was shipping good-looking content that still underperformed. Patterns kept repeating:
If we engineered content from measured SERP constraints - not gut feel - we would publish fewer pages, each more likely to rank. That meant:
Success looked like faster throughput, stronger early rankings, and lower rework. Main considerations at the starts were as followed:
Example payload you can use in LLMs to get similar results:
{
"query": "laser hair removal vs waxing cost",
"targets": {
"content_type": "comparison+guide",
"word_count": 2200,
"h2_min": 9,
"h3_min": 12
},
"semantic_phrases": [
{"phrase":"pain level comparison","required":true},
{"phrase":"treatment frequency","required":true},
{"phrase":"long-term cost","required":true},
{"phrase":"skin types and contraindications","required":true}
],
"competitors": {
"titles":[
"Laser vs Waxing: Which Is Cheaper Long-Term?",
"Waxing vs Laser Hair Removal Cost Comparison"
],
"has_faq_rate": 0.7,
"schema_types":["Article","FAQPage"]
},
"faq":[
"Is laser hair removal worth it long-term?",
"Does waxing cause ingrown hairs more than laser?"
],
"tech": {
"title_max": 60,
"meta_max": 155,
"schema":["Article","FAQPage"]
},
"internal_links":[
{"anchor":"treatment prep checklist","url":"/pre-treatment-checklist/"},
{"anchor":"aftercare guide","url":"/aftercare/"}
]
}
The system turns that into a role + instruction prompt with hard requirements for headings, FAQ, schema, phrasing, and internal links.
A draft cannot publish unless it passes:
If any check fails, the job returns with precise fix instructions and the draft is regenerated or lightly edited.
The result was predictable: a lot of publishing energy with thin traffic. I didn’t need a “content writer.” I needed a content system that made the right page, the right way, the first time.
[Keyword Queue]
↓
[SERP Fetcher] → [SERP Cache]
↓
[Page Scraper/Renderer] → [Signal Store (Postgres + blobs)]
↓
[Ranking Analyzer] → [Gap Detector] → [Outline Scorer]
↓
[Prompt Builder] → [AI Generator] → [QA Validator]
↓
[CMS Publisher] → [A/B Title Tests]
↓
[Search Console + Analytics Collector] → [KPI Dashboard]
Scraping fragility: many ranking pages render critical content with JavaScript, so naïve HTML fetches miss headings, FAQs, and schema. I stabilized extraction with Playwright-based rendering, deterministic fallbacks when rendering fails, and a cache-first strategy that stores both raw HTML and parsed artifacts. Polite rate limiting and rotating fingerprints kept the crawler reliable and compliant, while checksum comparisons prevented reprocessing unchanged pages.
Model drift: output quality can degrade as models update or as prompts accumulate subtle changes. I versioned prompts by SERP archetype and locked them to use-case IDs, then tracked QA pass rates per version. When pass rates dipped or variance increased, I could roll back a single prompt family, run A/B validations, or re-tune constraints without touching the rest of the system.
Overfitting to a single SERP snapshot: SERPs are living systems. To avoid building content that perfectly fits yesterday’s winners, I cached snapshots for reproducibility but enforced scheduled refreshes and mandatory rechecks before publishing high-value pages. For volatile keywords, I compared two recent snapshots and only accepted constraints that were stable across both.
Automation for its own sake: automation should remove toil, not judgment. Human editors still review outlines and final drafts with a short, objective checklist: intent alignment, clarity of explanations, evidence and examples, and whether the piece earns the click against current titles. The pipeline handles data gathering, guardrails, schema, and links; editors focus on truth, tone, and usefulness.
AI assistants reward pages that are explicit, structured, and verifiable. I tuned outputs for LLM discoverability by making claims sourceable, adding concise definitions and step-by-step procedures, and writing headings that map cleanly to questions users actually ask. Every article includes valid JSON-LD (Article plus FAQ or HowTo when SERP prevalence justifies it), tight summaries at the top, and short, self-contained answers inside each section so models can extract helpful snippets without extra context. I reduced ambiguity by preferring concrete nouns over pronouns, naming entities consistently, and using tables for comparisons. Finally, I linked out to authoritative references where facts matter and maintained author bios with real-world experience to satisfy E-E-A-T signals that LLMs increasingly weigh when choosing what to surface.
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209%
More Bookings
with a higher average customer value thanks to increased sales of premium treatments.
106
phone calls
direct response to our high-intent Google Ads campaigns.
110%
More foor traffic
customers actively coming in for consultations and treatments.
8X
Return on Ad Spent
every dollar spent on ads generated eight dollars in revenue.
