GEOLAB

How we work

The Geolab Method. Built for how models choose.

Six stages, a locked prompt set, every action a hypothesis. We re-run the same questions so you can see what moved.

Principles

What the method will not skip.

Measure first

No tactics until we know how often — and how — engines name you.

Work is a hypothesis

Every Structure, Engineer or Amplify action is tied to named prompts. If we can’t test it later, we don’t ship it.

Models trust sources

Entity, corroboration and consistent description beat classic on-page tricks.

Compounding is the product

One-off work decays. Measured signals, on cadence, are what engines remember.

Prove it on the same questions

A locked prompt set is re-run on cadence so progress is visible and attributable.

Five questions

Why a model names you — or names someone else.

  1. 01

    Can engines find you

    Is the content crawlable, accessible and on-topic — eligible to be retrieved at all?

  2. 02

    Can they quote you

    Can a generative system cleanly parse, extract and attribute a usable passage?

  3. 03

    Is the brand clear

    Are you described clearly, consistently and with the right relationships across the open web?

  4. 04

    Do others back you

    Are independent, trusted sources reinforcing your claims and entity?

  5. 05

    Does it stick

    Have lasting associations been established that survive model updates and time?

The six stages

01

Measure

Establish how the brand appears — or fails to appear — across major AI engines.

We define a category-specific prompt set (typically 40–120 prompts) covering brand, competitor, category, problem/solution, comparison and local prompts. Each is run across priority engines. We record mention, citation, sentiment, competing brands, and supporting sources, then produce a baseline Visibility Score and Share of Voice.

  • Baseline report
  • Priority prompt clusters
  • Trusted-source list

Success: A quantified picture of current AI visibility and the prompts that matter most.

02

Map

Explain why models choose certain sources and ignore others.

Entity analysis, source mapping, content-pattern teardown and competitor review against five questions — can engines find you, quote you, see a clear brand, find others backing you, and does it stick. We look for missing corroboration and form testable hypotheses linked to specific prompt clusters.

  • Entity health assessment
  • Trusted source map
  • Scores against those five questions
  • Prioritised hypotheses

Success: Evidence for why the brand is or isn’t selected by the models.

03

Structure

Make the brand technically and structurally legible to generative systems.

Entity clarity, answer-first page design, claim–evidence proximity, schema, crawler signals (including llms.txt and robots), and information architecture that reinforces topical relationships. Foundation work so models can parse, attribute and trust what you own.

  • Technical and structural recommendations
  • Updated entity reference points
  • Priority owned pages for engineering

Success: Owned presence is significantly easier for models to parse and cite.

04

Engineer

Create or rework content so engines can retrieve, extract and cite it.

Not classic SEO content. Volume for volume’s sake is avoided. We fill gaps against high-value prompt clusters, rewrite high-potential pages into answer-first formats, and build definitive, well-sourced resources. Depth, factual density and citability take priority.

  • Engineered content (new or reworked)
  • GEO-tuned briefs and style guidelines
  • Early citation movement on target prompts

Success: New or improved assets start earning citations or better mention quality.

05

Amplify

Build the corroborating signals models use when deciding whom to trust.

Targeted digital PR focused on citability, consistent brand description across third-party platforms, strategic mentions in places models already trust, and source diversification so the brand is not reliant on a single domain.

  • Authority and corroboration plan
  • Placements and mentions secured
  • Updated source map

Success: Independent sources reinforce the brand’s entity and claims.

06

Prove & Iterate

Re-measure on the same prompt set and refine continuously.

The core prompt set is re-run on an agreed cadence — typically monthly on Programme work. We compare against baseline, handle noise across cycles, diagnose stalls against those five questions, and feed learning back into Structure, Engineer and Amplify. Every action stays tied to a hypothesis.

  • Visibility reports on cadence
  • Commentary on what moved and why
  • Updated priorities and case-study data

Success: Measurable movement in citation rate, share of voice, or mention quality.

Start with a Geo Audit