For Professional Services & B2B Firms
Know exactly where your firm stands before the committee decides without you
For consultants, agencies, IT and managed-service providers, and specialist B2B firms who suspect they are invisible in AI-answer research and want a measured starting position before spending on anything else.
Every engagement is directed by a technical specialist and reviewed before delivery.
What this is
The Surface Intelligence Audit is a scored, specialist-read diagnostic of exactly where your firm stands across the four surfaces a buying committee now reads: classic search on your category terms, AI answers and Share-of-Answer on the comparison questions buyers ask, third-party proof and reputation, and the technical foundation every engine has to parse. You get a Machine-Readiness Score with your named competitors scored on the identical category-question panel, and a prioritized list of the corrections that move your number most, before any retainer is discussed. With 94 percent of B2B buyers now using AI somewhere in their most recent purchase, and much of that research done before a vendor is ever contacted, this audit shows whether you are even on the list a committee started from.
The problem
Why professional-services and B2B firms lose here
You cannot see the shortlist your buyers build, and neither can anyone else, until it results in a form fill or a call. With most of a B2B purchase now decided before a salesperson is ever contacted, a firm that has never checked whether it appears in the AI answers a buying committee reads has no idea whether it was ever in the running.
A single relationship cannot cover a committee. Buying groups for a considered B2B purchase now typically run six to thirteen stakeholders, each checking a different concern, and no sales conversation reaches all of them. What they read instead is your visible presence, and most firms have never measured what that presence actually says.
A scorecard with no human reading behind it does not tell a firm what to fix first. A B2B buyer's research spans classic search, AI answers, review platforms, and your technical foundation at once, and a single-surface tool check misses the interaction between them.
So the real starting question is not who to hire. It is: on the exact category and comparison questions your buying committee is asking right now, how visible are you, how do you compare to the specific competitors you lose deals to, and which correction is worth doing first.
The evidence
What the numbers show
94 percent of B2B buyers used AI during their most recent purchase.
established Forrester, 2026 Buyers' Journey Survey, 18,000 global business buyers, as reported April 2026.
85 percent of buyers think more highly of a vendor an AI chatbot mentions by name; 51 percent now start research with an AI chatbot rather than Google.
established G2, March 2026 survey of 1,076 B2B software buyers and decision-makers (PR Newswire).
85 percent of B2B AI-search citations for category queries come from third-party sources, not vendor websites, independently found twice.
established Valasys, 2026 analysis; Rampiq AI Visibility Optimization Program, 30 B2B brands, May 2026.
92 percent of surveyed B2B buyers say AI has shaped their vendor shortlist, 45 percent "significantly."
emerging Semrush commissioned survey, 643 US B2B professionals, March-April 2026.
How it works
The work, made checkable
- 01
We freeze the category-question panel first
We build the real solution, category and head-to-head comparison questions your buying committee actually investigates, phrased the way a committee phrases them, and lock the panel before any reading is taken, so the score is repeatable and the competitor comparison is fair.
- 02
We read all four pillars against published standards
We measure your classic-search presence on the frozen panel, your technical foundation against Core Web Vitals field thresholds and indexation checks, and your third-party proof and reputation across the review platforms and directories your category actually uses.
- 03
We sample the AI-answer surface many times per engine
Answer engines are not deterministic, so a single check is worthless. We run each frozen category question across ChatGPT, Perplexity, Gemini, Copilot and Google AI Overviews many times, and report how often you appear as a rate with a confidence band, stamped with engine, locale and date.
- 04
We score the named competitors you actually lose to
We run the same panel against the competitors you identify, so your Machine-Readiness Score sits next to theirs on identical category questions. The result shows your number, and where the gap is, on which surface, and against whom.
- 05
We rank the corrections that move the number most
We triage every finding by impact against effort into a prioritized register, most valuable first, not an undifferentiated list of everything imperfect. You get an ordered plan you could hand to any competent team, including your own.
- 06
A specialist writes and reviews it, then dates and ships it
A technical specialist reads the raw signals, writes the narrative, and reviews the whole diagnostic before delivery. Your document is dated and carries its engine set, locale and question panel, so it is a true baseline any future work can be measured against.
Included
What is delivered
- The Machine-Readiness Score, one figure from 0 to 100, with a sub-score for each of the four pillars: Classic Search, AI Answers and Share-of-Answer, Third-Party Proof and Reputation, and Technical Foundation.
- A frozen panel of your real category, solution and head-to-head comparison questions, locked for the run so the reading is repeatable.
- AI-answer sampling across ChatGPT, Perplexity, Gemini, Copilot and Google AI Overviews, run many times per engine and reported as appearance rates with confidence bands, each stamped with engine, locale and date.
- Named competitors scored on the identical question panel, with the comparison group disclosed.
- Technical Foundation checks against published standards: Core Web Vitals field thresholds, indexation and crawl hygiene, canonical consistency, and schema parsing.
- A prioritized findings register, most valuable correction first, ranked by impact against effort.
- A specialist-written summary in plain English, dated and reviewed before delivery.
The outcome
What it moves
- A single Machine-Readiness Score from 0 to 100, with the four pillars always shown beneath it, so you can see which surface is carrying your visibility to a buying committee and which is quietly losing it.
- A clear read of your Share-of-Answer inside AI engines on your real category and comparison questions, reported as a rate with a confidence band, never a promised citation.
- Named competitors scored on the exact same category-question panel, so the gap between you and the firm actually winning the deal is visible, not asserted.
- A prioritized findings register that tells you which correction to do first, ranked by impact against effort, rather than a flat list of everything imperfect.
- A dated baseline you own, so any future visibility work, with us or anyone else, can be measured against a real starting number.
Straight answers
Questions
You are based overseas. Does an audit from India actually understand my US B2B market?
We build the question panel from your real US category and comparison terms, run it against US engine results at the stated locale, and score it against your named US competitors. The firm, RavenGroup Global Tech Private Limited, bills in USD, and every reading is stamped with the exact locale and engine set it was taken against.
Is this just a tool export, or does a person actually read it?
A technical specialist assembles the question panel, reads the raw signals from every pillar, writes the narrative, and reviews the whole diagnostic before it reaches you. The deliverable shows its working: the disclosed panel, the dated samples, and the reasoning behind each priority.
How is this different from a generic B2B SEO audit?
A generic audit measures classic-search rankings alone. This one is built specifically for a considered B2B purchase: it samples the AI-answer surface on the category and comparison questions your buying committee actually asks, and reads your third-party proof footprint on the review platforms and directories those answers cite, beyond your on-page technical health.
Will the audit guarantee we get cited by ChatGPT or rank for our category?
No. AI-answer selection is undocumented and volatile. The audit measures your present position, ranks the corrections that move it most, and reports with variance. We commit to method and measurement, never to a promised number.
We already have SEO and PR running. Why do we need this first?
Because most firms run those as separate efforts measured by different numbers or none. This audit reads all four surfaces, classic search, AI answers, third-party proof, and technical foundation, together and against your named competitors, so you can see which one is actually losing you the shortlist before committing more budget anywhere.
Provenance
Sources
- Forrester, 2026 Buyers' Journey Survey, 18,000 global business buyers (established)
- G2/PR Newswire, "New G2 Research: Half of B2B Software Buyers Now Start Their Research With AI Chatbots," March 2026 (established)
- Valasys, 85% of B2B AI Search Citations Come From Review Sites, Not Brand Websites, 2026 (established, corroborated)
- Rampiq, AI Visibility Optimization Program, 30 B2B brands, May 31 2026 (established, corroborated)
- Semrush, "How AI Tools Shape the B2B Buying Process," survey of 643 respondents, March-April 2026 (emerging, single commissioned survey)
- web.dev, Core Web Vitals (Google), field thresholds for LCP, INP and CLS