SEO & AEO · Case Study

Ledgerline

2026
Year
Fintech SaaS
Sector
SEO reset to category #1
Work performed
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From page five to number one.

The job to be done

Become the name category buyers find and trust in search, not just the one they already know from reputation.

The challenge

A category leader with deep authority, and a search curve that sat flat for over twelve months. Discovery leaned on brand and referrals, so the product owned its category on reputation but was invisible in search, ranking around position 43 for the terms its buyers actually used, and authority simply refused to compound.

The process

It was treated as an authority platform, not a blog: precision publishing over volume, and a layer of neutral comparison pages against the established names, built to land in the top one to five. Coverage expanded across non-brand, high-intent queries, the technical base was rebuilt so rankings could compound, and the system held through Google’s March core update.

What made the difference

  • Target choice: broad blog topics gave way to neutral comparison pages against the established names, the exact high-intent queries buyers run before they choose, built to land in the top one to five.
  • Structure over volume: a topical-cluster architecture with tight internal linking replaced scattered posts, so authority concentrated on the pages that actually convert.
  • Technical base: crawl, indexation, and page-experience fixes cleared the path, so new pages ranked in weeks rather than months.
  • Authority, earned: the domain rating climbed 30 to 50 on useful pages and earned citations, not paid links, which is why it held through the March core update.
  • AEO formatting: answer-ready structure and schema mean the pages get cited by ChatGPT, Gemini and Perplexity, not only ranked by Google.

The solution

A structural SEO reset before any scale, comparison-led coverage that became a commercial-intent moat, and quality-weighted publishing so authority compounded first. Average position climbed from 43.9 to 7.4, and the domain rating moved from 30 to 50 in about six months, drawing level with the category’s known names.

What we built

  • A structural SEO reset before any scale
  • A library of buyer-intent and comparison pages built for the top one to five
  • A technical and authority foundation so rankings compound
  • AEO-ready formatting, cited by AI answers as well as search
The proof · Google Search Console

Not a spike. A trajectory.

5.03KTotal clicks
2.74MTotal impressions
0.2%Avg CTR
8.0GSC avg position
Four search signals converge into one rising trajectory
DR 30 → 50
Ahrefs domain rating, up in about six months, level with the category’s known names

Execution timeline

Aug 2025

Engagement start. Baseline 187K impressions over 28 days, average position 43.9.

Sep 2025

First blogs live; the comparison-led structure begins.

Jan 2026

467K impressions over 28 days; average position climbs to 12.3.

Mar 2026

700K over 28 days at position 7.3, steady through Google’s March core update.

Apr 2026

938K over 28 days; a new single-day peak of 50,851 impressions.

May 2026

Crosses 1M monthly impressions and takes #1 in the US for its primary category term.

The result

The brand now ranks #1 in the US for its highest-intent category term, up from page-five invisibility. Organic crossed one million monthly impressions, a durable comparison-page moat holds the top one to five, and about 10% of organic traffic arrives through AI answers, so buyers are educated before the first call.

#1In the US
43.9→7.4Avg. position
1M+Monthly impressions
33MQLs in a month
Common questions
How long does an engagement take?

Most builds run over a focused one-month setup, then a two to three month optimising phase where the numbers compound. The exact timeline flexes to the scope.

What do you need from our team?

Access to your current funnel and data, one point of contact for decisions, and the room to instrument each stage. The heavy lifting is mine.

How do you measure success?

Against one visible funnel: qualified pipeline in, stage by stage conversion, and a forecast the team will put its name to. Not vanity metrics.

Do you work with our existing tools?

Yes. I build on the stack you already run and add an AI layer over it, rather than forcing a migration.

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