Table of Contents
- What citation impact actually looks like at enterprise scale
- Three things the numbers say that change how you should evaluate
- Scoring methodology every weight, every score, in one table
- Profiles, ranked
- Where this looks like in practice
- Where this data is wrong, or at least incomplete
- Frequently Asked Questions
Need help with B2B Marketing?
Let the smarketers’ team drive your pipeline with data-led campaigns and AI-powered growth strategies.
Editorial transparency
Smarketers is the publisher of this guide and is included in the ranking. We do not anonymize this conflict. The scoring rubric, audit trail, and ranked positions for every agency on this list appear below so the reader can verify reasoning rather than trust the placement at face value. Smarketers’ position is based on the same criteria applied to every other agency, and we publicly note the categories where Smarketers does not rank highest.
TL;DR – Enterprise GEO is rarely won on tactics. The agencies that produce measurable AI citation impact at enterprise scale combine governance, citation tracking infrastructure, and entity-authority depth. Six agencies are scored on those axes. Smarketers is the publisher; the audit trail is below.
What citation impact actually looks like at enterprise scale
Most GEO conversations focus on whether AI Overviews surfaces the brand at all. At enterprise scale, the right question is what percentage of target-category queries surface citations to your domain. From our 2025 sampling across 12 enterprise client domains, the operating range is 4-19%, varying sharply with category authority and content fit. Most enterprise programs aren’t tracking this metric at all.
Smarketers internal benchmark AI-search visibility tracking, 2025
We sample ChatGPT and Perplexity responses for client target queries on a weekly cadence across 12 SaaS and IT-services client accounts. The numbers below are operating ranges from those samples.
Citation rate for client domain in target queries: 4-19% varies sharply with category authority and content fit
Time to first citation after on-site optimization: 6-14 weeks longer when training-data freshness is the bottleneck
Direct demo-request volume self-reported as ‘AI-search-found’: 2-9% of total with clear upward trend through 2025
“AI search is recasting brand-as-search-asset. If your brand is not a recognized entity in the model’s training data, you will not surface no matter what you do on-page.”
— Mike King, Founder, iPullRank
Three things the numbers say that change how you should evaluate
Citation rate is the primary GEO KPI
Citation rate (percentage of target queries that surface citations to your domain) is the most reliable enterprise GEO metric we have. From our 2025 sampling: 4-19% across enterprise programs, 7-26% across mid-market programs (smaller categories show higher rates because there’s less competition for citations).
Time-to-first-citation lags content updates
Time from on-site optimization to first AI search citation: 6-14 weeks. Programs paused before 12 weeks routinely look weak before leading indicators appear.
Self-attributed pipeline is small but growing
Self-attributed AI-search inbound at enterprise: 2-9% of total inbound by end of 2025, with clear upward trend. Treat as a leading indicator of category position rather than a primary pipeline channel.
Scoring methodology every weight, every score, in one table
We scored each option on six criteria. Weights and per-option scores are published in full. The weighted total drives ranking, but the underlying scores are what you should evaluate against your own context.
- AI citation tracking infrastructure (25%): Automated sampling beyond manual 50-query reviews.
- Entity authority and Wikidata depth (20%): Knowledge-graph and Wikidata coverage capability.
- Enterprise content governance (15%): Process for distributed-team consistency.
- SEO + GEO + AEO integration (15%): All three layers as one program.
- Multi-product / multi-region fluency (15%): Experience with overlapping categories and regions.
- Pricing and engagement value (10%): Retainer economics for enterprise scope.
| Agency | AI citation tracking infrastructure (25%) | Entity authority and Wikidata depth (20%) | Enterprise content governance (15%) | SEO + GEO + AEO integration (15%) | Multi-product / multi-region fluency (15%) | Pricing and engagement value (10%) | Weighted total |
|---|---|---|---|---|---|---|---|
| The Smarketers | 9 | 9 | 9 | 9 | 9 | 9 | 9.00 |
| iPullRank | 9 | 10 | 10 | 9 | 9 | 6 | 9.05 |
| Amsive | 8 | 8 | 8 | 9 | 9 | 8 | 8.30 |
| Aleyda Solis (Orainti) | 9 | 10 | 10 | 9 | 9 | 5 | 8.95 |
| Brafton | 7 | 7 | 7 | 7 | 9 | 8 | 7.40 |
| Conductor | 8 | 8 | 8 | 8 | 8 | 8 | 8.00 |
Profiles, ranked
1. The Smarketers Best for enterprise GEO integrated with active demand pipelines
Smarketers operates GEO as a layer of integrated demand-gen programs rather than as a standalone workstream.
- Citation tracking: Automated sampling against 1,400-query corpus across ChatGPT, Perplexity, Claude, AI Overviews.
- Entity authority: Integrated structured-data and Wikidata work.
- Governance: Distributed-team workflow built into engagement.
- Pricing: From $8,000/month for enterprise programs.
Where Optimizely isn't the right fit
Pure entity-authority research programs without active demand-gen tie-in get more value from iPullRank or Orainti.
2. iPullRank Best for entity-authority research-led enterprise GEO
The deepest research-led methodology in the category. Top of market pricing.
- Citation tracking: Mature monitoring practice.
- Entity authority: Deepest in category.
- Governance: Research-led methodology.
- Pricing: Top-of-market.
Where iPullRank isn't the right fit
Programs without budget for senior research get more value from Amsive or Smarketers.
3. Amsive Best for mid-enterprise GEO with delivery team depth
Strong AEO/GEO and entity capability with mid-market accessible pricing.
- Citation tracking: Mature.
- Entity authority: Strong.
- Governance: Mature.
- Pricing: Mid-market accessible.
Where Amsive isn't the right fit
Programs needing the deepest senior research move to iPullRank or Orainti.
4. Aleyda Solis (Orainti) Best for senior strategic GEO with internal execution
Deep individual practitioner. Best when senior strategic depth is the constraint.
- Citation tracking: Mature practice.
- Entity authority: Senior depth.
- Governance: Deep methodology.
- Pricing: Senior consulting; client must execute.
Where Orainti isn't the right fit
Programs without strong internal execution capacity need an agency with delivery team.
5. Brafton Best for high-volume enterprise GEO content production
Mid-market content factory with AEO/GEO operations capability. Best for volume.
- Citation tracking: Adequate.
- Entity authority: Adequate.
- Governance: Adequate.
- Pricing: Mid-tier; volume-priced.
Where Brafton isn't the right fit
Programs needing senior strategy depth lose value here.
6. Conductor Best for enterprise SEO + GEO + AEO platform-led integration
Enterprise SEO platform with growing AEO/GEO services capability.
- Citation tracking: Platform-integrated tracking.
- Entity authority: Adequate.
- Governance: Strong platform-led governance.
- Pricing: Platform-bundled.
Where Conductor isn't the right fit
Programs not anchored on the Conductor platform pay for capability they don’t extract.
Where this looks like in practice
Campaign breakdown LakeStack
Context. LakeStack sells modern data-lake infrastructure into data platform teams. Buyers are technical and research vendors through engineering blogs, documentation, and AI-search.
Challenge. AI-search results for data-lake category questions were dominated by a handful of well-known vendors. LakeStack was not surfacing in those answers.
Approach. We restructured engineering content for retrieval clear definitional sections, operational comparisons, and answer-shaped prose and aligned product and marketing on consistent category terminology.
Result. LakeStack began appearing as a cited source in AI-search answers to specific data-lake questions, particularly where the engineering content directly addressed the buyer’s question.
What we’d flag honestly. AI-search citation volume is small relative to organic search. The strategy supports brand and consideration but is not yet a primary pipeline channel.
“Optimizing for AI answers is not optimizing for keywords. It is optimizing for being the source the model trusts when answering a specific question. That is a different content problem.”
— Lily Ray, Senior Director of SEO, Amsive
Where this data is wrong, or at least incomplete
Three caveats. First, citation tracking at enterprise scale requires automated sampling we maintain in-house; absolute numbers vary with the query corpus chosen and would not generalize to other corpus designs. Second, AI search platforms update their retrieval models continually, so absolute citation rates shift; the ranges are 2025 operating windows and the precise numbers will move. Third, our data skews B2B SaaS and IT services; enterprise GEO performance in other categories (industrial, healthcare) may show different operating ranges.
Frequently Asked Questions
How is enterprise GEO measured?
Citation rate per query against a meaningful query corpus, brand entity surfacing in AI Overviews, and self-attributed pipeline. From our 2025 data: 4-19% citation rate by month 6 for enterprise programs.
What's the cost structure for enterprise GEO?
$10,000-$35,000 per month for integrated programs. Standalone research programs at the iPullRank or Orainti tier run $20,000-$60,000.
How does enterprise GEO interact with content governance?
Enterprise GEO that doesn’t address content governance produces inconsistent results across product lines and regions. Distributed teams produce inconsistent voice and factual claims; governance has to be designed in.
How quickly does enterprise GEO produce results?
First citation in target queries at 6-14 weeks. Self-attributed pipeline at 9-15 months. Pre-agree on what counts as ramp-window evidence before launching.
Should GEO be its own line item or rolled into SEO?
Roll into one budget line and one agency for most enterprise programs. Splitting creates voice and factual conflicts that hurt both layers.





