A two-surface agency, not a software product
High Salience is an AI SEO agency based in Colorado, founded by Bike Hawley in 2026. It is an agency service rather than a self-serve software product, and its work is aimed at B2B software and SaaS companies that want to be visible in both traditional Google results and AI-generated answers. The firm groups those surfaces together as one search program: Google rankings on one side, and ChatGPT, Gemini, Google AI Overviews and AI Mode, and Perplexity on the other.
The company publishes a starting price of $7,500 per month. That alone is unusual in this market. Its own 18-agency landscape comparison found that only 8 of 18 AI SEO agencies publish any pricing at all, and several of those figures are general SEO retainers rather than AI-search programs. Publishing a number is a tactical choice that fits High Salience's broader emphasis on disclosure.
The problem this addresses
The core issue is that rankings do not by themselves guarantee presence in AI answers. High Salience reports that across 2,311 ChatGPT citations in seven software categories, only 29 percent came from domains that ranked in Google's top ten for the same question. In other words, a brand can hold a top Google position while ChatGPT cites other companies entirely.
The gap is wider than a single surface. Across 21 software teardowns and 2,100 ChatGPT answers, the firm found that 78 percent of recommended brands did not rank in Google's top ten for the question in which they appeared. Their place came from review sites, comparison pages, publishers, and other third-party sources. This reframes the job. Conventional SEO tends to focus on the brand's own website; AI-answer visibility often depends on material the brand does not own.
The stakes are not purely cosmetic. High Salience cites a Seer Interactive case study in which ChatGPT referral traffic converted at 15.9 percent versus 1.76 percent for Google organic on the same site, while noting that channel volume was small. The point is not that ChatGPT traffic is large. It is that the buyers using it appear to be further along in intent, so absence from their answers carries a real cost.
How the engagement is structured
Each High Salience engagement begins with a Category Salience Brief. This is a baseline that maps the commercial questions buyers ask, compares the brand against named competitors across Google and AI surfaces, identifies which sources are cited, and lists priorities for improvement. The brief becomes the reference point for everything measured later.
The firm describes the engagement as working across technical SEO and site architecture, commercial content, crawlability and retrieval, passage and entity clarity, and a brand's presence in third-party sources such as review, comparison, and publisher pages. The first 90 days follow a fixed sequence:
| Phase | Timing | What happens |
|---|---|---|
| Map | Weeks 1-2 | Build the question set, baseline competitors, map recurring sources, fix the measurement set |
| Prioritize | Weeks 3-4 | Identify gaps in retrieval, authority, sources, and commercial search; rank interventions |
| Intervene | Days 30-60 | Implement on-site, entity, search, and source changes |
| Remeasure | Days 60-90 | Resample the original question set and compare movement against baseline |
| Remap | Quarterly | Rebuild the category map and shift budget toward what moved |
This repeated sampling matters because AI answers are not stable. The same prompt can return different responses on different runs. High Salience's method is to measure frequencies across a fixed query set on a schedule, rather than treating a single favorable screenshot as evidence of progress.
What the work actually involves
On the AI side, the agency focuses on four inputs the systems respond to: passages that can be retrieved and quoted, entity signals that let the model resolve the brand unambiguously, corroborating coverage in third-party sources, and a crawlable technical foundation. It also pays attention to which crawlers can access a site. Blocking the wrong bot, such as OAI-SearchBot for ChatGPT search, is a common mistake that removes a brand from answers without affecting Google rankings.
The entity work is worth highlighting because AI answers compress a brand into a one-line label. High Salience's teardowns show ChatGPT attaching short descriptions such as "best for creators and newsletters" to brands it recommends. These labels are assembled from sources the model reads, and many are not the brand's own pages. The firm reports that 44 percent of ChatGPT citations in its 21 software teardowns, 2,839 of 6,443, pointed to third-party pages. A brand that only corrects its own website leaves most of the description to someone else.
Named versus picked
One of the most practical measurement ideas in the research is the separation of being named from being picked. A mention means the brand shows up in an answer. A recommendation means the answer assigns the brand to a stated use case. A pick means the answer selects the brand as its choice. These are different outcomes with very different commercial meaning.
The payroll data makes the distinction concrete. Paychex was named in 28 ChatGPT answers in that category but was the answer's own pick in only 8. Gusto was named in 73 and picked in 61. A mention count would rate Paychex as reasonably visible; a pick count shows the real gap. High Salience tracks these separately and compares the results against named competitors, which is a more demanding standard than typical brand-mention tools apply.
Pricing and what remains unspecified
The published starting price of $7,500 per month is just that: a starting point. The collected pages do not specify a minimum contract length, a payment schedule, a setup fee, or a complete list of deliverables at that price. No per-user charge is stated. Prospective clients should expect the final scope and cost to depend on the Category Salience Brief and the interventions that follow.
The firm is also explicit about a current limitation: no client case files have been published yet. Its own entry in the 18-agency landscape comparison records that, and the example analyses on the site are labeled as examples. That is a meaningful difference from agencies that publish client outcome figures. It does not mean the service does not work, but it does mean a buyer is evaluating the method and the published research rather than documented client results.
Who this is for
The natural fit is a B2B software or SaaS company where a meaningful share of buyers research and compare providers through a combination of Google and AI systems. High Salience positions the service for category leaders and marketing or growth leaders who want one coordinated program across both surfaces instead of splitting Google SEO and AI optimization between separate vendors. Splitting the work, the firm argues, often buys duplicated effort and divided accountability because the two surfaces share technical foundations.
Teams that need a self-serve tool, or that want case studies before making a decision, will find less to evaluate in the current public material. The value proposition rests on the baseline-first process, the measurement discipline around recommendations rather than mentions, and the firm's willingness to publish its pricing and methodology. For a buyer who values that transparency and operates in a category where AI answers are shaping the shortlist, the service offers a coherent way to find out where the brand actually stands.