Check your Machine-Readiness Score

By Industry

The vendor named on the shortlist built before the buyer ever makes contact

For B2B firms, software and technology companies, professional-services practices and specialist providers whose buyers research the category, compare vendors and build a shortlist long before anyone fills in a form, and who want to be the name on it.

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Every engagement is directed by a technical specialist and reviewed before delivery.

What this is

The B2B Visibility System is our coordinated program for companies chosen inside a long, mostly invisible buying process. We engineer the four surfaces a buying committee reads while it quietly builds its shortlist: classic search for the solution and category terms your buyers investigate, the AI answers where they now ask which vendors to consider, the reputation and third-party proof that answer trusts, and the technical foundation every engine has to parse to name you. It is one build run against the Machine-Readiness Score, not disconnected SEO tasks, PR pushes and a LinkedIn calendar that never meet. The outcome is a firm that reads as a credible, well-defined entity on the exact solution and category questions your buyers ask, is present in the AI answers that now shape the shortlist, and is corroborated by the review sites and industry conversations those answers cite. We measure and move your Share-of-Answer. We never promise a ranking, citation or pipeline number.

The problem

Why this matters now

Most of a buyer's work now happens without the vendor in the room. In Forrester's 2026 Buyers' Journey Survey of 18,000 business buyers, 94 percent used AI during their most recent purchase, and industry research consistently puts around 70 percent of the buying journey in channels a vendor cannot see, where the shortlist is built and preferences are set before a single form is filled. By the time a buying committee, which now averages roughly thirteen people, reaches a sales team, the vendors worth considering have usually already been decided.

That decision no longer happens only on a Google results page. A buyer opens ChatGPT, Perplexity or Gemini and asks which vendors solve their problem, and an answer comes back naming three or four firms with a sentence on each. If you are missing from that answer, you were not outsold. You were never on the list the committee started from. Bain's 2026 research put the average B2B buyer at seventeen AI search queries a week, so this is not an edge case, it is the new front door to the category.

The harder part for B2B is where those answers come from. Analyses of B2B AI citations in 2026 found the large majority of citations for broad category questions come from third-party sources, not the vendor's own website, with review platforms like G2 and Capterra and practitioner threads on Reddit carrying weight far beyond their traffic. A beautifully written product page is often the source the answer trusts least. The category conversation happening about you, without you in it, is the one the engine reads.

So the real problem is rarely your website or your ad budget. It is that the classic-search terms, the AI answers, the third-party proof and the technical signals that decide whether a committee ever considers you are scattered across teams and vendors who never coordinate, and nobody is engineering them as one thing against a measured baseline.

How it works

The mechanism, made checkable

  1. 01

    Read the buying surface first, against the questions a committee actually asks

    We build a frozen panel of the real solution and category questions your buyers investigate, phrased the way a committee phrases them, from problem-framing queries to head-to-head vendor comparisons. We run the Machine-Readiness Score against it with weight on the pillars that decide a considered B2B purchase, then score your named competitors on the identical panel. We set scope in writing from this reading before anything is touched, so you can see which surface is carrying your visibility and which is quietly losing you the shortlist.

  2. 02

    Lock you in as a category entity engines can resolve

    We make your company resolve to one unambiguous entity: a consistent name and description everywhere it matters, Organization and Product schema on your site, sameAs links to verified profiles, and a clean knowledge-graph footprint. Industry analyses in 2026 found pages with Organization, Product and FAQ schema markup were meaningfully more likely to be cited by browsing-mode engines. An engine will not confidently name a vendor it cannot confidently identify and describe, and for B2B the description matters as much as the name.

  3. 03

    Engineer the classic-search and solution-content layer

    We engineer the pages that own your category and solution terms the way a technical buyer reads them: clear problem framing, direct comparison content, use-case and integration depth, and the structured, quotable answers that both classic search and AI engines lift. This is the owned surface you control, built to be retrievable and citable, not a blog calendar that ranks for nothing a buyer would ever ask.

  4. 04

    Build the AI-answer and Share-of-Answer position

    We measure how often you are named across ChatGPT, Perplexity, Gemini, Copilot and Google AI Overviews on the frozen category panel, sampled many times per engine and reported as an appearance rate with a confidence band. Then we engineer the signals that legitimately move it: entity clarity, quotable on-site answers, and the off-site corroboration these engines lean on. We show Share-of-Answer as a range with the engine, locale and date stamped, because the answer is a range, never a promised citation.

  5. 05

    Strengthen the third-party proof the answer trusts

    Because the majority of B2B category citations come from third-party sources, we target the surfaces the answer actually reads: an accurate, complete and current presence on the review platforms your buyers trust, corroborated facts across analyst and directory listings, and a legitimate footprint in the industry conversations that get cited. We earn reviews and references from real customers and real coverage only, never fabricated, incentivized for positivity, or manufactured, in line with FTC rules and platform policy.

  6. 06

    Support the authority layer that carries a considered purchase

    For B2B, industry expertise is repeatedly the top-ranked vendor-selection factor, ahead of price. We build and support the earned-authority surface that proves it: your executive and practitioner perspectives, comparison and category content, and the digital-PR and coverage angles that both a human committee and an AI answer read as credibility. We shape and place it as visibility work; nothing is fabricated, no quote, statistic or coverage, and every claim is substantiated.

  7. 07

    Measure, hold and compound

    We re-read the Machine-Readiness Score on an agreed cadence, track Share-of-Answer and classic-search position over time with variance, and hold the position, because AI answers are volatile and undocumented, competitors keep publishing, and the third-party conversation moves. A B2B category position is one to defend across a long sales cycle, not a launch to shelve.

What is included

What is delivered

  • B2B Machine-Readiness Score read across all four pillars, weighted for a considered purchase, with your named competitors scored on the identical category-question panel.
  • A frozen panel of your real solution, category and head-to-head comparison questions, locked for the run so the reading is repeatable and the competitor comparison is fair.
  • Entity and schema engineering: Organization and Product markup, consistent naming and descriptions, sameAs links and knowledge-graph footprint so engines can resolve and describe you.
  • Classic-search and solution-content engineering across the category and comparison terms your buyers investigate, built to be retrievable and quotable.
  • AI-answer Share-of-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.
  • Third-party proof work on the review platforms, analyst listings and directories that buyers and their engines trust, prioritized for authority and accuracy, with reviews earned from real customers only under FTC and platform rules.
  • Earned-authority and digital-PR support for executive and practitioner content, comparison assets and coverage angles, substantiated and on-brand, with no fabricated quotes, statistics or coverage.
  • Technical Foundation checks so engines can crawl, render and parse your site: Core Web Vitals, indexation and crawl hygiene, canonical consistency and schema parsing.
  • A ranked fix list you keep, and, on the retainer, a specialist-reviewed report on the agreed cadence tracking Machine-Readiness Score and Share-of-Answer movement with variance.

The outcome

What it moves

  • A firm that resolves to one clear category entity, with the schema, descriptions and profile consistency that let an engine confidently name and describe what you do.
  • A measured Share-of-Answer across ChatGPT, Perplexity, Gemini, Copilot and Google AI Overviews on your real category and comparison questions, tracked as an appearance rate with a confidence band and stamped with engine, locale and date.
  • Ownership of the classic-search terms your buyers investigate, built as retrievable, quotable solution and comparison content rather than a blog that ranks for nothing.
  • A stronger third-party proof layer on the review sites, analyst listings and industry conversations that B2B AI answers actually cite, earned from real customers and real coverage only.
  • An earned-authority surface that reads as genuine industry expertise to both a thirteen-person buying committee and the AI answer summarizing the category.
  • A measured starting position and a ranked list of the corrections that move it most, so your budget goes to the gap that changes the shortlist rather than to guesswork.
  • A surface that stays current on cadence across a long buying cycle, because your entity, content, answers and third-party proof are maintained rather than abandoned after launch.

What you get

What you get, and how it is priced

The B2B Visibility System runs at two levels: a one-time Category Foundation build that fixes and engineers the whole surface across all four pillars, and an ongoing Category Authority retainer that holds and compounds the position, because AI answers shift month to month, competitors publish, and the third-party conversation keeps moving. Both are scoped against the Machine-Readiness Score before any work is committed. Below is what each level covers, how the outcome is produced, and the deliverables inside it.

Category Foundation (one-time build). The full B2B surface, fixed and engineered once. Machine-Readiness Score read against your category panel, entity and schema work, classic-search and solution-content engineering, the initial Share-of-Answer baseline, third-party proof cleanup and the stand-up of the earned-authority surface. You finish with a resolvable category entity, an engineered owned surface, a measured AI-answer baseline and a ranked fix list you keep. Best when the surface has never been engineered as one thing and needs to be put right before it is maintained. Scoped in writing against your Machine-Readiness Score.Quoted
Category Authority (ongoing retainer). The standing engagement that holds and compounds the position across a long sales cycle. Continuous entity, content and technical maintenance, ongoing Share-of-Answer tracking across the engines, sustained third-party proof and earned-authority work, and a Machine-Readiness Score re-read with a specialist-reviewed report on an agreed cadence. Month to month, no lock-in, cancellable in the same number of steps it took to start. Best when your category is competitive and the surface needs someone defending it. Scoped in writing.Quoted

You see the full deliverables and cadence first, then a price built for your business, confirmed in writing.

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Straight answers

Questions about B2B Visibility System

You are based overseas. How can you do B2B visibility for a US-serving firm?

This is RavenGroup Global Tech Private Limited, billed in USD. B2B visibility work is not about geography, it is about engineering a resolvable entity, category content, third-party proof and the AI-answer surface, all measured against real US buyer questions at the stated locale and against named US competitors. Every engagement is directed by a technical specialist and reviewed before delivery, wherever the specialist sits, and every reading is stamped with the exact engine set and locale it was taken against.

Our buyers are a committee doing months of quiet research. How does this reach them?

That is exactly what the system is built for. Industry research in 2026 put roughly 70 percent of the B2B buying journey in invisible, self-directed channels, with a committee averaging around thirteen people building a shortlist before contacting anyone. Those individuals are not visible to you. What we can engineer is the surface they read while they research: the category search terms, the AI answers naming vendors, and the third-party proof those answers cite. The work makes you present and credible where the shortlist is actually formed.

Why so much focus on review sites and outside conversations instead of our own website?

Because that is where the AI answers your buyers read get their information. Analyses of B2B AI citations in 2026 found the majority of citations for broad category questions came from third-party sources rather than vendor websites, with G2 reported as one of ChatGPT's most-cited sources and Reddit accounting for a large share of external citations in discovery queries. Your site still matters and we engineer it as part of the work, but a vendor with a polished page and no third-party corroboration is often the source the answer trusts least. We work both.

Who directs the content, and what stops a quote or statistic from being made up?

A specialist directs the entity decisions, the content, the schema, the third-party work and every authority angle, and checks the delivery before it ships. Every quote, statistic and piece of coverage is substantiated, and every review comes from a real customer. A B2B vendor caught with fabricated proof loses the trust of both committees and engines. This work is built to earn that trust instead.

Can you guarantee we get cited in ChatGPT or rank for our category?

No. AI-answer selection is undocumented and volatile, engine behavior changes week to week, and search results personalize. Our commitment is to engineer every signal that can legitimately move, to sample your Share-of-Answer across engines, and to report movement with variance, including where it is flat. We do not promise a citation, ranking or pipeline number: those depend on engines and buyers no firm controls.

How do you measure whether it is working across such a long sales cycle?

We re-read the Machine-Readiness Score on an agreed cadence and track your position across a frozen panel of real category and comparison questions, with Share-of-Answer reported as an appearance rate and a confidence band, each reading stamped with engine, locale and date. Because answer engines are not deterministic, sampling runs many times per engine and reports a range, not a single confident number. Attributing a signed deal to any one touch across a months-long committee purchase would be a fiction, so what we measure on your surface is visibility and movement, not invented attribution.

Why is this scoped instead of a fixed price?

Because a B2B visibility surface is never standard. One firm owns its category terms but is invisible in every AI answer and has three reviews on G2, another has strong analyst coverage and a site engines cannot parse. Publishing one number for both would misrepresent the work, and pricing by how large your company looks ignores what the work actually requires. We publish the full deliverables and cadence here, read your surface, then agree the exact figure directly in writing. The substance is visible before any number.

We already invest in SEO, PR and LinkedIn. How is this different?

Most B2B firms run those as separate efforts by separate teams and vendors who never coordinate, measured by different numbers or none. The B2B Visibility System engineers classic search, AI answers, third-party proof and the technical foundation as one build against a single metric, the Machine-Readiness Score, so the entity the content builds, the proof the PR earns and the answers engines give all reinforce each other. It is not a replacement for your existing team; it gives your whole surface one owner and one measured baseline.

Why should we trust you with something this important?

Judge the method. This page states plainly how the work is produced, which signals get engineered, what gets measured, and what we refuse, including that reviews come from real customers only and coverage is never fabricated. The work is directed by a technical specialist with years of hands-on work in search and AI visibility, the evidence is the Machine-Readiness Score and Share-of-Answer movement we can show you, and the fix list stays with you. The place to start is a scored read of exactly where you stand, before any retainer.

Related

Where this connects

AI Answer & GEO Engine

The pillar that decides which vendors an AI answer names for your category. When Share-of-Answer is the gap, this is the focused program that engineers the entity, quotable content and third-party proof those engines cite.

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Surface Intelligence Audit

Most B2B engagements start here: a scored read of exactly where you stand across all four pillars, with your named competitors scored on the same category questions and a ranked fix list that tells you which surface is losing the shortlist.

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The Machine-Readiness Score

The 0 to 100 metric your B2B work is scoped against and measured by, built from four disclosed pillars including AI Answers and Share-of-Answer, and reported with variance.

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Provenance

Sources

  • Forrester, 2026 Buyers' Journey Survey of 18,000 global business buyers, as reported April 2026: 94 percent of B2B buyers used AI during their most recent purchase. Cited as direction, not guarantee.
  • Bain & Company, 2026 B2B buyer research, as reported 2026: average of 17 AI search queries per buyer per week during vendor research.
  • Valasys, 85% of B2B AI Search Citations Come From Review Sites, Not Brand Websites, 2026, https://valasys.com/b2b-ai-search-citations-third-party-sites/ : the majority of citations for broad B2B category queries come from third-party sources rather than vendor websites.
  • Foundation Inc, Reddit AI Citations research and G2 AI-trust analysis, 2026, https://foundationinc.co/lab/reddit-ai-citations : Reddit accounts for roughly a fifth of external citations overall and a larger share in unbranded discovery queries; G2 reported among ChatGPT's most-cited sources.
  • LinkedIn B2B Institute and industry buying-committee research, 2026: buying committees average roughly thirteen people and industry expertise ranks as the top vendor-selection factor ahead of price.
  • Aggarwal and colleagues, GEO: Generative Engine Optimization, KDD 2024, arXiv:2311.09735 (peer-reviewed): entity and content signals as levers for inclusion in generated answers, applied here as direction, not guarantee.
  • Google Search Central, AI features and your website (no special markup required for AI Overviews), official platform documentation, accessed July 2026.

Begin with where the business stands.

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No obligation. The deliverable is a measured starting position and the corrections that move it most.