
Safety scores for the everyday products parents buy.
A search channel built on comparison pages for a family product safety site.
- Industry
- Consumer products and ecommerce
- Duration
- 8 months, ongoing
- Website
- r3recs.com

The services
SEO, AEO and GEO
4.0xGoogle clicks in one quarter (397 to 1,599, Q2 to Q3 2026)66%of Google clicks from comparison pages, all time38,597live AI-assistant fetches since launch- Owned R3's search strategy end to end: an intent-led strategy centered on product comparisons, and the site architecture and page types covering every stage of a buyer's research
- Built on-page optimization into every template: a standard page structure, structured data across all page types, and a data-driven process for rewriting titles
- Set up the crawl and indexing framework: sitemaps organized by page type, crawler access rules, and index coverage monitoring
- Planned and implemented AI answer engine readiness: an access policy for each AI bot, a machine-readable summary of the scoring method, and a target set of buyer questions mapped to the page that answers it

Web design and development
1,921pages built across 7 page types~92%indexed by Google- Owned R3's page system end to end: five templates (comparison, category, buying guide, ingredient, product), so every newly scored product generates a ranking page automatically
- Built a dynamic comparison engine, wired to live search demand, that generates a head-to-head page for any two scored products on demand
- Built every page type as SEO and AEO infrastructure first, with schema and crawlability in place before a single article was written against it
- Designed and shipped the Visitor, Free and Premium paywall UI with soft gating, so Premium content stays visible to Google while blurred to visitors
- Owned the full release process end to end: end-to-end tests for every template, plus pre-release checks (an SEO-title validator, a redirect checker, a content-image audit) that block a release automatically if any fail

Marketing analytics and optimization
13–62xbot inflation found and removed135days of AI-crawler history recovered8silently failed jobs found and fixed- Owned R3's full measurement stack end to end, alone: Search Console and GA4 synced daily into a database, an AI crawler tracker, and a GTM scorecard with automated executive narration
- Engineered a verified measurement layer for affiliate and AI-crawler data: built bot-filtering logic that corrected tracking errors of 13 to 62x, and a recovery system that restored 135 days of crawler history a fragile earlier version had lost
- Designed and built an automated health-monitoring system across every scheduled job, with alerting that fires the instant any job goes silent, catching eight jobs, including the delayed-email sender, that had been failing undetected for months

Content strategy and production
28posts published from one check mark each58curator picks published, 9 more auto-refused- Built a Slack-to-carousel pipeline: a request becomes a researched, fact-checked carousel in Figma, with a review thread before anything posts
- Built 8 Figma carousel templates feeding one system that publishes automatically across 6 platforms, instead of a separate production line per channel
- Wrote curator picks in the founder's own voice, published automatically from a single pasted link, with a fact and brand-voice check before anything goes out

AI video production
39videos rendered by the carousel-to-video engine3video formats designed and built- Owned the full 8-stage video pipeline end to end: data, script, voice, editing and QA, producing vertical video for TikTok, Instagram Reels and YouTube Shorts
- Solved a real audio-sync problem: built word-level forced alignment via Whisper so on-screen text and scores land on the exact frame the voiceover says them
- Built and voiced an AI presenter persona that narrates every video, in three distinct formats: single-product reviews, top-5 countdowns and head-to-head comparisons
- Built a persistent, always-on rendering worker that renders video automatically, with no one starting each render by hand
- Built a second, lighter engine, in R3's own codebase, that turns a finished social carousel directly into a short vertical video with no separate production step

Growth and product marketing
20lifecycle emails wired0–816indexed pages, beat the 600 to 800 target band0–1,606ranking keywords, 775 in the top 10, beat target0–35,228weekly impressions, hit target- Built email capture into the pages parents already read, with a send cap and quiet hours from day one, so signup never requires a separate funnel and no parent gets flooded
- Built welcome flows for new subscribers and alerts that fire the moment a saved product is recalled or its score changes
- Owned R3's go-to-market strategy end to end: a competitive moat analysis against five named competitors, a 4-phase, 12-week launch framework, and the revenue model behind it
- Owned R3's growth-loops system end to end, alone: six named, numbered loops, each with its own danger-signal threshold and target, tracked in a purpose-built scorecard every week for 13 straight weeks
- Designed the three-tier pricing and paywall strategy from a full codebase audit, iterated at least four times over three months against competitive benchmarking
- Built and ran a 0-to-first-paying-users research program: a multi-framework interview instrument (The Mom Test, Continuous Discovery Habits, Jobs to be Done) with bias-defeating questions
- Audited R3's live scoring weights against its own safety-incident data and caught a trust risk before it could become a bad headline: chemical-exposure risk counted for more than half a baby carrier's score while suffocation and hip-safety risk, the categories that actually kill, counted for about 40%, with the legal standard dropped from scoring entirely

AI workflow automation
406products checked in the first sweep77/265brands with a recall history found38categories launched in 11 weeks- Built a daily scan of four US government recall sources, matched against every brand and product R3 scores, with matching rules that act on certainty: an exact match pulls the product immediately, a near match waits for a person to confirm
- Added a recall gate to the scoring system itself, so a recalled product can't score well
- Built the bridge from a confirmed recall to the content calendar: a dated recall brief drafts automatically, capped so recalls never crowd out everything else
- Built a nightly demand-scoring system that proposes the next categories to launch, gated behind an automated fact-check step that blocks any safety claim it can't trace to a source
- Owned the full launch pipeline end to end: scoring, fact-check, images, hub wiring, the safety angle for each category, and a guide so the founder can run a launch without me

Brand, PR and events
2false or unverifiable claims found and removed from the pricing page- Owned R3's positioning end to end: a single source of truth for what it is and who it's for, one line replacing inconsistent page-by-page descriptions
- Built platform playbooks and a quality checklist defining R3's voice, so every page, post and email reads the same way
- Built an automated, build-time check that blocks copy drifting from R3's voice or stating a claim R3 can't back, so nothing waits on a person to catch it
- Reviewed every claim on the pricing page and removed 2 that R3 couldn't stand behind: a "7-day free trial" claim and a "25,000+ families" claim

The challenge
Renn Labs built R3 from scratch in early 2026: a new site that scores everyday products for safety, from air fryers and water filters to car seats and baby lotion, and helps US parents choose between them, positioned as the Wirecutter for clean parenting. It launched with 750+ scored products across 162 categories.
The brief called for speed and leverage over headcount: launch fast, scale content with AI instead of a writing team, and reach parents without the years of traffic an established review site already has. I joined in February 2026 as R3's only hands-on marketer, reporting to the founder and the Chief of Staff, expected to run search, content, social, email and recall alerts myself, as systems rather than as tasks done by hand.
A brand-new domain starts with nothing to rank on. Nobody searched for R3 by name, and parents already trusted established review sites, so every visit had to come from a product or category search. By May 2026, Google showed R3 about 90,000 times a month, and parents clicked about 0.12% of the time.
Two problems sat underneath. The pages changed almost every day, so Google kept seeing a moving target, and bots and team visits mixed into the analytics, so nobody could tell which channel worked.
Where it started
Built from zero, expected to move fast
Renn Labs set out to launch R3 fast and scale it with AI instead of a writing team, positioned from day one as the Wirecutter for clean parenting.
A new domain starts with nothing to rank on
R3 had no brand searches, in a category where parents already trusted established review sites. By May, Google showed R3 about 90,000 times a month and parents clicked about 0.12% of the time.
Numbers nobody could trust
Team visits, bots and tracking that changed with every redesign mixed into the analytics, so nobody could tell which channel was working.
One marketer, expected to run a team's channels
Search, content, social, email and recall alerts all needed running every week, with no team behind them, so the work had to run as systems.

My approach
I started by sorting R3's search demand by intent. About 89% of it was shopping: "best", "safest", "X vs Y". Only about 11% was safety research. A parent searching "best air fryer" is still worried about what's in the coating, they just won't type the chemistry, so I made safety the deciding detail inside the comparison and category pages they already look for.
From late May I stopped the daily page changes and gave every template one fixed structure, so Google had a stable site to rank and the team had numbers it could trust. Then I built the rest as systems one marketer can run: pages that grow with the catalog, a weekly search loop, a social post factory, recall watch and lifecycle email.
- Sorted R3's search demand by intent before building anything
- Made the comparison page the core template, with the safety score as the tie-breaker
- Stopped the daily page changes and fixed one structure per template
- Cleaned the analytics of bot clicks, team visits and jobs that never ran
- Wrote every template for AI answer engines as well as Google
- Built social, recall watch and email as systems that run every week
My tool stack
The tools I used on R3's work, taken from the projects on this page.
Google Search Console
Google Analytics 4
Google Tag Manager
Supabase
PostHog
Microsoft Clarity
Slack
Figma
Google Sheets
Remotion
TikTok
Instagram
Threads
Buffer
ElevenLabs
Resend
The impact
Parents now choose R3 in the results. Monthly Google clicks rose from 113 in May to 662 in August 2026, 5.9x in three months, while impressions fell from 90,858 to 66,377 and click-through went from 0.12% to 1.00%. Comparison pages earn 66% of R3's Google clicks, 891 of them rank on Google, and since August 33 to 48 visitors a week arrive from AI answers.
The rest runs every week without a team behind it. A finished social post costs one request and one check mark, and 28 went out that way. A recall reaches R3's pages within a day, with a person checking anything uncertain. And the team decides from numbers it can check, after I found bots had inflated a key click count 13 to 62 times.
- Comparison pages as the front door, earning two thirds of Google clicks
- Pages that grow every time the catalog grows, with no one writing them by hand
- A weekly search loop that flags searches one step from page one
- Clean numbers, with an alarm when any scheduled job goes quiet
- Recall alerts on the site within a day of a notice
- A social post factory run from one check mark, and 20 lifecycle emails with send caps
Project results
- 662
Monthly Google clicks
May to August 2026. Source: Google Search Console, R3.
- 4.0x
Google clicks, one quarter (397 to 1,599)
Q2 to Q3 2026. Source: Google Search Console API, R3.
- 1.00%
Click-through rate, up from 0.12%
May to August 2026. Source: Google Search Console, R3.
- 891
Comparison pages ranking on Google
2026. Source: Google Search Console, R3.
- ~92%
Of sitemap URLs indexed by Google
October 2026. Source: Google URL Inspection API, R3.
- 38,597
Live AI-assistant fetches since launch
March to October 2026. Source: Supabase ai_bot_stats_daily, R3.
- 48
Visitors a week from AI answers, at most
August to September 2026. Source: GA4, R3.
- 62x
Peak bot inflation found in the affiliate click count, then removed
March 2026. Source: R3's own crawler and analytics logs.
- 8
Scheduled jobs found silently failing, then fixed
2026. Source: R3's own job logs.
- 38
Product categories taken live
11 weeks, June to August 2026. Source: R3's category launch log.
- 20
Lifecycle emails wired, each with a send cap and quiet hours
March to August 2026. Source: R3's email engine.
- 3
Tiers designed: Visitor, Free and Premium
February 2026. Source: R3.
- 0
Unbacked claims left on the pricing page
October 2026. Source: R3's pricing page, before and after.
- 406
Products checked in the recall scan's first sweep
2026. Source: R3's recall scan.
- 77
Of 265 brands checked with a recall history found
2026. Source: R3's recall scan.
- 28
Social posts published, each from one check mark
May to October 2026. Source: R3's content calendar and publishing log.
- 39
Videos rendered by the carousel-to-video engine
May to October 2026. Source: R3's carousel-to-video rendering log.
- 58
Curator picks published, 9 refused for failing checks
2026. Source: R3's curator drop log.

















