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Proven Results & Case Studies:
Growth Engineered by
kōdōkalabs
We Don't Sell Promises.
We Sell Data.
In the algorithmic era, the only metric that matters is the delta between where you are and where you could be if you executed at full speed.
While traditional agencies celebrate “activity” (meetings, reports, hours billed), we celebrate velocity (pages published, keywords ranked, revenue generated).
Below are real-world examples of how the kōdōkalabs Hybrid Model outperforms the status quo.
Case Study 1
B2B SaaS Growth
The Context
A Series B Fintech company was burning €20k/month on LinkedIn Ads. Their organic traffic was stagnant (flatlined at 5k visits/month) because their in-house team could only publish 4 articles a month.
The Challenge
They needed to dominate “Bottom of Funnel” (BoFu) comparison terms (e.g., “Competitor X vs. Competitor Y”) before their Q4 fundraising round.
The kōdōkalabs Solution
- Technical Strategy: We identified that the client had zero visibility for “Competitor Alternatives.”
- The AI Velocity: We built a custom “Comparison Agent” that analyzed G2 reviews of competitors to find their weaknesses.
- The Output: We published 40 distinct “Vs.” pages and 25 “Best [Use Case] Software” pages in just 6 weeks.
- The Human Layer: Our editors added proprietary pricing data and internal screenshots that AI couldn’t access, ensuring “Information Gain.”
The Results (90 Days)
“We expected this level of output to take a year. kōdōkalabs did it in a quarter. The quality was indistinguishable from our senior writers.” — CMO, Fintech Client
Case Study 2
E-Commerce / Programmatic SEO
The Context
A niche electronics retailer with 5,000+ SKUs. They were using default manufacturer descriptions for every product.
The Challenge
Google hammered them with an algorithmic penalty for “Thin/Duplicate Content.” Traffic tanked. They couldn’t afford to hire 20 writers to rewrite 5,000 descriptions.
The kōdōkalabs Solution
This wasn’t a writing problem. It was a data problem.
- The Data Structure: We used Python to scrape the technical specs of every SKU and organized them into a structured JSON dataset.
- The Agentic Workflow: We built a “Product Description Agent” that took the boring specs and wrote unique, benefit-driven copy for 2,000 priority SKUs.
- The Scale: We deployed the content in batches of 500/week, monitoring indexing rates in real-time.
The Results (90 Days)
Penalty: Fully recovered (Manual Action removed).
Why These Results are Repeatable
We didn’t get lucky. We applied a formula.
- We Scoped Correctly: MD-level strategy identified the exact lever to pull (BoFu content vs. Tech Fixes).
- We Executed Fast: AI allowed us to do the work of a 10-person team in days.
- We Verified Quality: Human experts ensured the content satisfied user intent better than the competition.