PharmaGEO Research Hub.

Eighteen field reports anchored on the May 2026 PharmaGEO public index and primary GEO research from Princeton, Ahrefs, IQVIA, and the BMJ. Hard data on what generative engines say about pharma brands across six engines, three languages, and four therapeutic areas — and what to do about it.

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The PharmaGEO Playbook 2026 edition.

The playbook our analysts ship to launch teams. Eight doctrines, the search architecture LLMs reward, engine-by-engine guidance, and a 90-day mobilization plan, anchored on cross-engine and cross-language pharma data.

  • State of GEO in pharma in 2026
  • Recent sources prioritization trends
  • Best practices for Pharma Brands
  • GEO in MLR
  • Owned vs Third party strategies

All articles

Every article is built from real audit data, with citations to peer-reviewed sources where relevant.

Get the full playbook
No. 18 · Comparison

PharmaGEO vs generic GEO tools.

Why pharma GEO is structurally different from B2C GEO, and how PharmaGEO compares with Profound, Evertune, Otterly, Peec.ai, Semrush, and Ahrefs.

Read · 8 min
No. 17 · Engines

There is no AI visibility. There are six.

The same brand can be #4 in OpenAI and #8 in Perplexity in the same week. A 33-point gap, decoded across four therapeutic areas.

Read · 11 min
No. 16 · Language

The language geography of AI visibility.

One molecule, three Answer Rates: 13.8% in English, 48.8% in French, 51.9% in Spanish. Why localization is not translation.

Read · 9 min
No. 15 · Sources

The citation source stack changes by therapeutic area.

NCCN ~85% in oncology. FDA labels lead in obesity. A specialist hub outranks NICE in psoriasis. There is no universal source playbook.

Read · 10 min
No. 14 · Metrics

Why answer rate and share of voice tell different stories.

A brand named in 93% of answers can hold only 22% of the conversation. The 4x compression every brand team should be measuring.

Read · 8 min
No. 13 · Mobilization

The 90-day pharma GEO mobilization plan.

Twelve weeks, three phases, named owners and KPI targets. The operating plan brand teams ship after the audit.

Read · 12 min
No. 12 · Pharmacovigilance

AI is already a pharmacovigilance surface.

FDA labels are the #1 and #2 cited sources in obesity prompts. Engines are already delivering boxed warnings — and surfacing T2D-only molecules on weight-loss queries.

Read · 10 min
No. 11 · Findings

8 key GEO findings every pharma brand needs.

Eight findings from the May 2026 PharmaGEO public index across obesity, lung cancer, atopic dermatitis, and psoriasis.

Read · 10 min
No. 10 · Benchmark

Benchmarking pharma brands across three engines.

A public-data benchmark across OpenAI, Perplexity, and Gemini in four therapeutic areas. Same brand, three rankings.

Read · 13 min
No. 09 · Optimize

The owned content playbook for the AI answer layer.

Eight tactics anchored on Princeton GEO research, Ahrefs brand-mention correlations, and the public PharmaGEO source data.

Read · 12 min
No. 08 · Methodology

How LLMs cite pharma sources, decoded.

Four source archetypes, four therapeutic areas, and the propagation chain that decides which domain wins citations.

Read · 12 min
No. 07 · Behavior

How HCPs are using AI search in 2026.

54% of HCPs use generative AI in clinical contexts (IQVIA). OpenEvidence hit one million consultations in a single day. What HCPs ask, and what they see.

Read · 9 min
No. 06 · Metrics

Measuring share of voice in LLM answers.

The 6-axis PharmaGEO score, sampling design, and the cadence that absorbs 59% monthly citation volatility.

Read · 9 min
No. 05 · Content

Medical writing built for AI retrieval.

The Princeton GEO results table, the 44.2% front-loading rule, and a before/after rewrite MLR can sign off on.

Read · 9 min
No. 04 · Competitive

Competitive blindspots in LLM pharma answers.

Your 2024 SEO competitive map is wrong twice over. A 7-row blindspot audit table, anchored on cross-engine and cross-language data.

Read · 11 min
No. 03 · Regulatory

MLR, EFPIA, and the AI answer layer.

The compliance frame for the AI surface — EPAR mirroring, off-label exposure, and an MLR-friendly content audit checklist.

Read · 12 min
No. 02 · Pattern

What the obesity TA tells us about GLP-1 launches in AI.

53.9% Top-3 SOV. FDA labels at #1 and #2 in citations. Off-label leakage. The structural pattern of every GLP-1 launch in the AI answer layer.

Read · 10 min
No. 01 · Primer

Why pharma needs GEO in 2026.

The state of AI search in pharma — anchored on IQVIA, CMI, and the public PharmaGEO index. The case for moving now.

Read · 8 min

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