iPullRank
A New York City agency founded in 2014, combining enterprise technical SEO and content engineering with relevance engineering, generative-search strategy, measurement and authority work.
Hub Score v2.0
59/100
Limited public profile
Top 60%The structured capability record is narrow; verify fit before shortlisting.
Tied #206 with 21 other companies of 399 listed United States companies.
Entry retainer
Not published
No recurring floor published; confirm current scope
Operating history
0 years
At peer median
Documented breadth
6 services
5 industry categories
Evidence confidence
98% · High
291 comparable industry peers
Website traffic
No public estimate
HypeStat · Checked 26 Aug 2026
Traffic context: No defensible current public traffic estimate was recorded for this domain; this field is display-only and does not affect Hub Score. Traffic is display-only and does not affect Hub Score.
Open HypeStat sourceBuyer decision brief
Where the public evidence points
iPullRank qualifies through current official material establishing a New York City base and 2014 founding, a detailed traditional and AI-search service model, named current leaders and three prominently published outcome examples. The current about page names four leaders but does not present them as the complete company roster, so headcount remains unscored. Its productized, deliverable-led model is public, but exact dollar pricing is not; the old $15,000 seed price and 20-50 team band have therefore been removed.
Official company material establishes a New York City base and 2014 founding
Current service model spans traditional and AI search
Current anonymized cases publish revenue and recovery figures
Relative context
How iPullRank compares
Compared with 291 United States profiles sharing at least one documented industry.
| Measure | Company | Peer median |
|---|---|---|
| Entry retainer floor | Not published | $1.8k/mo |
| Years operating | 0 | Not published |
| Hub Score v2.0 | 59 | 62 |
| Evidence confidence | 98% | 95% |
Peer comparisons are computed from the current directory dataset, not external market estimates.
Documented services
Linked capabilities are included only when documented in the company research record.
Documented industries
Industry inclusion signals public experience, not exclusive specialisation.
Verified profile evidence
Products and operating claims
New York City operating base
Current official enterprise guidance says iPullRank is based in New York City and serves local and international clients; current anniversary material also documents company gatherings in New York.
iPullRank enterprise guide
Founded in 2014
Current official guidance and the company's eighth-anniversary history explicitly state that Michael King founded iPullRank in 2014.
iPullRank anniversary
Four current leaders named; complete size unscored
The current about page names Michael King, Kami Hess, Fajr Muhammad and Zach Chahalis in leadership roles but does not label this as a complete employee roster.
iPullRank about
Technical SEO, content and relevance engineering
Current services span technical and enterprise search, content strategy and engineering, AI-search measurement, digital PR and authority, creative production and consulting.
iPullRank AI search
Productized, deliverable-led model
The company describes scopes organized around tangible deliverables rather than hours and publishes staged AI-search programs across emerging, growth and elite organizational maturity.
iPullRank home
No public recurring dollar minimum
Current service and contact pages describe customized productized programs but publish no exact comparable recurring SEO price.
iPullRank home
First-party case evidence
Published client outcomes
Anonymized global bank
$2.4 billion in incremental revenue attributed by the company to its content-engineering strategy
Current company-published financial-services result; the client is withheld under NDA and the public page does not expose raw attribution methodology or comparison dates.
Open sourceAnonymized global ecommerce marketplace
$290 million in revenue associated with AI-generated category-page content
Current company-published ecommerce result; client identity, baseline, date window and independent attribution evidence are not public.
Open sourceAnonymized automotive publisher
130% traffic recovery after five years of algorithm-update losses
Current company-published media result; the public summary does not expose the absolute traffic baseline, exact time window or raw analytics.
Open sourceTransparent scoring
Why Hub Score v2.0 is 59
Operating resilience
3/153 points under 3 years; 6 at 3-5; 9 at 6-10; 11 at 11-15; 13 at 16-20; 15 at 21+ years.
Delivery capability
22/25A monotonic diminishing-return curve: 1 service = 6, 2 = 10, 3 = 14, 4 = 17, 5 = 20, 6 = 22, 7+ = 25.
Sector evidence
25/25Industry coverage contributes 10, 14, 16 or 18 points; 1, 2 or 3+ source-linked case studies add 3, 5 or 7 points.
Commercial clarity
9/204 for a live site + 6 for a disclosed entry floor + 5 for a stated pricing model + 5 for a specific best-fit client statement.
Delivery capacity
0/15Team bands map monotonically: 10-20 = 5, 20-50 = 8, 50-150 = 11, 150-250 = 13, 250-500 or 500+ = 15.
The five capability factors total exactly 100. Evidence Confidence is calculated and displayed separately. See sources, guardrails and formulas →
Questions for the sales call
- Request read-only analytics, Search Console, AI-visibility and revenue-attribution evidence for a comparable client, including absolute baselines, dates and calculation rules.
- Confirm the complete delivery roster, named leads, monthly capacity and use of contractors; the public four-person leadership section is not a headcount.
- Obtain a dollar proposal mapping every deliverable, implementation responsibility, term, measurement system and asset-ownership rule.
What this profile does not prove
- Current team size and recurring dollar price are not public; the old directory estimates were removed.
- All three selected outcomes are anonymized company-authored summaries rather than independently audited cases.
- Large revenue figures require buyer-side validation of attribution, incrementality, baseline and the client's own implementation contribution.