Search results are increasingly written by AI, not just listed by it. Getting found now means two things: a technically sound site that Google can crawl and rank, and content structured so an AI system can lift a clean answer from it and cite the source.
This practice grew out of technical SEO work scaling organic search for a high-growth automotive SaaS platform (the case study below). The underlying skill hasn't changed: crawl-log literacy and structured-data discipline. What changed is the audience. That discipline used to be aimed purely at Google's ranking algorithm. Now it's aimed at a second one: the retrieval logic inside large language models.
We don't run paid campaigns. If a client asks how paid should be sized against organic, we'll advise, but the budget belongs to organic, and we'd rather be honest about that than sell a channel we're not the right team to run.
What is generative engine optimization, and why does it matter now?
GEO structures content so AI answer engines can extract, cite, and attribute it. It matters because a growing share of research now happens inside chat interfaces and AI Overviews instead of a list of links. An AI system isn't ranking ten pages for a person to click through. It's synthesizing one answer from a handful of sources, and those sources need to hand it something extractable: a direct answer, a dense fact, a clean comparison.
Four practices we apply to every page:
- A direct 40-60 word answer before any framing.
- Question-format headings that mirror how people phrase queries to an AI system.
- Entity-linked JSON-LD, so the organization, author, and service are machine-legible.
- Dense, citable key-facts blocks and comparison tables in place of adjective-heavy prose.
What technical SEO work is included?
Technical SEO is the foundation underneath GEO. If a page isn't crawlable, fast, and properly structured, no amount of AI-answer optimization helps, because most answer engines still rely on the underlying search index to discover content at all.
Technical audits
Core Web Vitals and CrUX analysis, crawl budget optimization, JSON-LD implementation, indexation and canonicalization review, and internal link architecture.
Content strategy
Topical authority mapping, semantic keyword clustering, content-gap analysis, and entity optimization built to an E-E-A-T-aligned editorial standard.
Data-backed growth
Search Console and GA4 instrumentation, Python-powered rank tracking, and reporting built for executives evaluating spend, not just marketers checking rankings.
How is this measured?
Every engagement starts by agreeing on the numbers that will define success, before any work begins. We instrument Search Console and GA4 on day one, track rankings weekly with Python scripts rather than a dashboard snapshot once a month, and report against a short, fixed list: indexed pages, ranking position on target terms, and traffic that actually converts to pipeline.
When something we recommend is a hypothesis rather than a proven pattern, we say so and propose how to test it cheaply before committing budget to it at scale. Growth claims on this page (the case study below included) are the same numbers a client would see in their own dashboard, not a rounded-up version of them.
How we decide
- 1. Agree on the numbers that define success before any work begins, not after.
- 2. Instrument tracking on day one; report against real dashboard data, not a rounded-up summary.
- 3. Flag every recommendation that's a hypothesis rather than a proven pattern, and propose a cheap way to test it first.
What tools power this work?
The same toolkit runs every engagement: Python for automation and rank tracking, Google Search Console and GA4 for analytics, Screaming Frog for technical crawling, Ahrefs and Semrush for competitive research, and JSON-LD/schema tooling for structured data.
Python
Automation
Google Search Console
Analytics
GA4
Analytics
Screaming Frog
Technical
Ahrefs
Research
Semrush
Research
JSON-LD / Schema
Structured data
| Technical SEO | GEO | |
|---|---|---|
| What it controls | Whether a page is crawled, indexed, and eligible to rank. | Whether an indexed page gets extracted and cited in an AI answer. |
| Core levers | Core Web Vitals, crawl budget, internal linking, schema. | Answer-first structure, entity linking, citable fact density. |
| Time to effect | Weeks, as crawl and index signals update. | Faster once indexed: answer engines re-check sources often. |
| Measured by | Search Console coverage and ranking position. | Citation and reference tracking in answer-engine outputs. |
Case study
Proven results
a high-growth automotive SaaS platform
Led by NCR Digital's founder, Niekos Robbins, prior to founding the firm · 7-month engagement
Domain Rating
20 → 44 in 7 months
A full-stack technical SEO transformation for a high-growth automotive SaaS platform. Rebuilt content silos by search intent, resolved Core Web Vitals deficits, deployed programmatic JSON-LD schema at scale, and ran a targeted link-building and disavow campaign. A stagnant domain became a compounding organic asset.
Every figure below is sourced to the tool that produced it and states its own measurement window — no rounding, no unscoped comparisons.
20 → 44
Domain Rating
in 7 months · Ahrefs
3.2x
Organic traffic
YoY increase · Google Analytics 4
47
First-page rankings
new keywords, in-window · Ahrefs rank tracking
100
Lighthouse score
all categories, at handoff · Google Lighthouse
DR trajectory (Ahrefs)
Bars are scaled to their own 0-100 axis.
Levers pulled
- Core Web Vitals: LCP, CLS, INP remediation
- Programmatic schema at 12k+ pages
- Content silo rebuild with semantic clusters
- Toxic backlink disavow and link-building sprint
- GSC-guided crawl budget management
Related insights
- What the First 90 Days of an SEO Engagement Look Like
- How to Choose an SEO Consultant: What to Ask Before You Sign
- In-House vs. Agency vs. Boutique vs. Freelance SEO
- How AI Answer Engines Choose What to Cite
- Canonical Tags: When to Use Them and When They Break Everything
- Core Web Vitals Explained: LCP, CLS, and INP for Non-Engineers
- What Is Crawl Budget and Why It Matters for SaaS Sites
- Robots.txt Mistakes That Are Blocking Your Best Pages
- How to Read a Crawl Log: A Beginner's Guide
- The Difference Between Indexing and Ranking (And Why It Matters)
- Technical SEO ROI: Measure the Revenue Impact
Frequently Asked Questions
What is generative engine optimization (GEO)?
GEO structures a page so AI answer engines, ChatGPT, Perplexity, Google AI Overviews, Claude, can extract and cite it. It sits on top of technical SEO fundamentals: answer-first structure, extractable facts, and entity-linked structured data.
Is GEO replacing traditional SEO?
No, it extends it. Crawlability, Core Web Vitals, and backlink authority still determine whether a page gets indexed and considered credible. Most answer engines still rely on that index. GEO is what happens after: whether the content is structured cleanly enough for a model to lift a citable answer from it.
Do you handle paid search too?
We advise on how paid should be sized against what organic is already producing, but building and running paid campaigns isn’t something we take on. Our work is organic: technical SEO and AI answer visibility.
How is this different from a typical SEO agency?
Most SEO work stops at rankings. We report against a small set of numbers agreed at the start of an engagement, usually indexed pages, ranking movement on target terms, and traffic that converts, and we say plainly when a recommendation is a guess versus something the data actually supports.