AI search is reshaping how PE sponsors and platform CFOs find the firm that will build their data foundation — and in a category where almost no boutique consultancy has optimized for it, the firms that establish visibility now lock in a structural advantage. Before we run the audit, we need to make sure we're asking the right questions about the right competitors to the right buyers. This document presents what we've learned about Relic AI's market — your job is to tell us what we got right, what we got wrong, and what we missed.
Before we measure how AI platforms cite Relic AI in the PE data & AI consultancy space, these three signals tell us whether AI crawlers can reach, read, and date your content at all. All three are derived mechanically from the Layer 1 crawl of all 12 live pages on July 30, 2026.
site:relicai.co returned zero pages from the domain, while the site itself serves all 12 pages at HTTP 200 on direct fetch. Four medium and two low findings follow./resources articles and the resources index — carry a June 18, 2026 date and fall inside 90 days; none is older than 180 days. Caveat: 8 product pages with no detectable date — verify manually. With no sitemap lastmod either, those 8 pages currently emit no freshness signal of any kind./sitemap.xml and /sitemap_index.xml also 404 on both apex and www — 0 pages have a sitemap entry, and all 12 are discoverable only by following links from the homepage.The people who buy interim data leadership now run their first vendor search inside an AI assistant. 94% of B2B buyers use LLMs during the buying process (6sense, November 2025), and for a services buyer running a search they may only run once in a platform's hold period, the firms an AI names first are the firms that get the call. This is a category where almost nobody has optimized for that: PE data & AI consultancies compete on sponsor relationships and referral, not on machine-readable authority. Relic AI is early enough that the position it takes now is the one it compounds from — but "early" cuts both ways, and the audit exists to measure exactly where the starting line is.
This Foundation Review is the input layer for that measurement. It contains three things we need you to check: the competitive landscape, which determines which head-to-head matchups the audit constructs; the buyer personas, which determine whose search intent the queries imitate; and the Layer 1 technical baseline, which determines whether AI platforms can access and attribute your content at all. Nothing here is a conclusion about your visibility — that comes after the queries run. This is us showing our work on the assumptions, so you can correct them while correcting them is still cheap.
The validation call is where those assumptions get locked. It produces two kinds of decisions. First, input validation: are the right firms in the right tiers, are the right people in the persona set, and are the capability ratings honest? Every one of those answers changes the shape of the buyer query set we run across the selected AI platforms — and a wrong answer spends audit budget measuring a market you don't actually sell into. Second, engineering triage: several Layer 1 items need no decision from you at all and can start this week. Both tracks are itemized in the Pre-Call Checklist near the end of this document.
lastmod in under a day, then submit it in both consoles; it is the cheapest single lever on the indexation problem above./blog currently returns a 200 with a "Redirecting to: /resources" body, so a bot following an external link there stops at a content-free page; the fix is a config change and needs nothing from the validation call.Three things to know before you read the rest of this document.
What this is A knowledge graph of Relic AI's market — the competitors, buyers, capabilities, and buyer frustrations that will be turned into the actual queries we run against AI platforms. In the PE data & AI consultancy category, buyers ask AI assistants questions in their own words ("who can consolidate reporting across eight operating companies?"), not in yours. This document is the bridge between the two. It is not the audit — nothing here measures your visibility yet.
What we need from you Corrections, not approval. Every purple box in this document is a real question with a real consequence attached — we've told you what changes in the audit depending on how you answer. The most valuable thing you can do is tell us where we're wrong. A competitor you never see in deals, a persona who doesn't exist at your targets, or a capability we've overrated all cost the same thing: audit budget spent measuring a market you don't sell into.
How to read the badges High means the item came directly from relicai.co's own pages or your own comparison table — we read it, we didn't guess it. Medium means it was assembled from category research or inferred from the problems your site addresses; it's plausible and worth scrutiny. Low means we reasoned our way to it and need you to confirm or kill it. Focus your review time on the medium and low badges.
Everything below was read off relicai.co directly. This is the base entity record — it decides which mentions across AI platforms get counted as you.
→ Validate "Relic AI" is not a clean entity in AI training data. Web searches for the exact brand return New Relic (the observability vendor) almost exclusively, plus Relicx (AI test automation) and an antique-identifier iOS app also published as "RelicAI." Is there a legal entity name, a former brand, a founder name, or a product name buyers use that we should count as you? If yes, it goes into the graph before query generation. If no, we deliberately exclude "New Relic" from your variant list and the audit reports unaided brand recall separately from category visibility — because otherwise every visibility number in the final report is inflated by a company you have nothing to do with.
5 personas: 3 decision-makers, 2 influencers. These determine whose search intent the query set imitates when it asks AI platforms about multi-entity data consolidation.
Critical review area This is the section where your correction is worth the most. Personas drive query construction directly — each one contributes a cluster of queries phrased the way that role actually searches. A persona who doesn't exist at your target platforms doesn't just waste queries; it pulls the whole query set toward a buying conversation that never happens.
Data sourcing note Role, department, seniority, influence level, veto power, and technical level are all KG fields — they come from the buyer problems and objections relicai.co addresses on its industry and private-equity pages. Names are illustrative placeholders for the role, not real people. The role description, primary buying jobs, and query focus areas are synthesized by us from those fields plus the category — they're our reading, and they're the parts most worth arguing with. There is no G2, Capterra, or Gartner presence for Relic AI, so no persona here is sourced from third-party reviews.
→ Dana is rated technical_level=low — does she actually stay out of the architecture conversation, or does she push on Snowflake-vs-alternative and who owns the code? If she engages there, platform-comparison queries move into the CFO cluster instead of sitting with the CTO, and the audit tests them at the budget holder's altitude rather than the builder's.
→ Both Marcus and Dana are marked veto_power=true, which is unusual — does the sponsor's operating partner actually source and approve this engagement, or does the platform CFO own the decision with the sponsor merely informed? Sponsor-led buying makes portfolio-level value-creation language the top-of-funnel query cluster; CFO-led buying makes close-and-consolidation language the entry point, and the two return almost entirely different pages.
→ Many PE-backed field-services and manufacturing platforms have no CTO at all — does this role exist at the companies you actually sell into, or does that evaluation weight sit with the CFO and an outsourced MSP? If there's no CTO, we replace this persona rather than downgrade it, and the deep technical queries get re-voiced as an IT director or an incumbent MSP defending its position.
→ Does Erin run the vendor search and hand the CFO a shortlist, or does she only execute after the CFO has already chosen? If she's the one searching, her month-end-close language becomes a top-of-funnel discovery cluster; if she's downstream, it becomes validation-stage phrasing tested against a shortlist that already exists — different queries, different pages, different competitive set.
→ Is Ray a buyer of this engagement or a beneficiary of it? If operations funds branch-level utilization reporting on its own budget, separately from the finance-led data program, field-services queries become a standalone cluster with its own competitive set — likely vertical software vendors rather than the consultancies in the competitor list below.
→ Who else shows up? These roles sometimes appear in PE-platform data deals — do they show up in yours? Group Controller (if the person who owns the consolidation and the chart-of-accounts mapping is distinct from the CFO, they search in accounting language, not analytics language). Head of Corporate Development / M&A integration lead (if the per-acquisition onboarding budget sits with the deal team rather than with finance, your 1–2 day connect claim is being evaluated by someone we haven't built queries for). Incumbent MSP or outsourced IT provider (in platforms with no CTO, this is often the party that reviews and can quietly kill an outside data vendor). Who else is in the room when this gets decided?
6 primary + 5 secondary competitors identified. Tier assignments decide which firms get direct head-to-head testing and which get category-level coverage.
Why tiers matter Each primary competitor gets roughly six to eight head-to-head queries — phrasings like "Relic AI vs. Accordion for PE portfolio reporting," "best Snowflake implementation partner for a multi-entity roll-up," and "alternatives to a Big 4 data engagement for a platform company" — so these six tiers determine roughly 36–48 of the queries in the set. Only Deloitte and McKinsey (QuantumBlack) came from your own on-site comparison table; Accordion, West Monroe, OneSix, phData and Aimpoint Digital are all medium confidence, assembled from category research rather than from anything you've published. If any of those five rarely appear in real deals, moving them to secondary shifts six to eight queries each out of the head-to-head set and into category coverage.
→ Validate Three questions, in order of consequence. (1) The real alternative. You publish no "vs" pages, so this whole set except Deloitte and McKinsey was inferred from the category — in deals you actually lost, did you lose to firms like Accordion, West Monroe, and OneSix, or to an internal hire and a local dev shop? If it's the latter, roughly a third of the query budget moves off firm-vs-firm comparisons and onto build-vs-buy and cost-of-in-house-team framing. (2) Two low-confidence guesses. Alvarez & Marsal and Domo are pure inference — A&M as the interim-leadership motion sponsors already buy, Domo as the buy-a-platform escape hatch. Do either of those actually come up, or should we drop them and spend the queries elsewhere? (3) Anyone missing. Which firm shows up in your deals that isn't on this page at all — and does it belong in primary?
12 buyer-level capabilities mapped: 7 strong, 3 moderate, 2 weak. These determine which capability queries the audit tests and which ones we press competitively.
Stand up one warehouse that every operating company flows into, so we stop reconciling six ERPs by hand every month
Connect a newly acquired company's data in days instead of running a months-long integration project for every deal
Get data out of QuickBooks Desktop, an offshore-built field system, and a construction ERP with no public API that no off-the-shelf connector supports
Define each metric exactly once so finance, ops, and the board are all looking at the same number instead of arguing about whose report is right
Produce a board pack and consolidated P&L the sponsor trusts, on cadence, within two weeks of every close
Get a data leader and a build team in the chair now, without committing to a seven-figure permanent headcount we can't justify yet
Let our people ask the company anything — specs, manuals, submittals, past bids — before the veterans who know it all retire
Agents that actually do the work — quote drafting, PO reconciliation, pricing-anomaly flags, an auto-generated board pack — not another chatbot
Warn me about churn, margin slip, and utilization anomalies before they show up in the quarter, not after
Do you have prebuilt connectors, data models, and a portfolio-level platform every new acquisition plugs into, or is every engagement built from scratch?
Can this firm staff a multi-workstream program across eight operating companies at once, and what happens if the two founders get pulled elsewhere?
Who exactly have you done this for, and can I call them before I put my name on this recommendation to the board?
Prioritization needed Seven capabilities are rated Strong: Governed Multi-Entity Data Warehouse Build, Repeatable M&A Data Onboarding, Hard-to-Reach Source System Integration, Semantic / KPI Definition Layer, Board & Sponsor Reporting, Interim CDO / Embedded Data Team, and Document & Knowledge Automation (RAG). The audit tests all 12 capabilities, but competitive differentiation queries will emphasize 3. Our working pick, by how many high-severity buyer pains each one resolves: the warehouse build (5 high-severity pains), M&A onboarding (3), and board & sponsor reporting (3). Which of these best represents where Relic AI actually wins deals?
→ Validate (1) The two weak ratings. We rated Delivery Bench Depth weak because you state you intentionally limit concurrent clients and staff with founding engineers, and Named Client References weak because all six proof points on the site describe clients by shape rather than name, with no logos or callable references. Are those fair against phData and West Monroe specifically, or is there proof that exists but isn't published? The named-references rating in particular is a GEO problem as much as a sales one — models cannot cite proof they cannot see. (2) Agentic workflows. We rated it moderate because the shipped agent work we can see is concentrated in biotech and VC rather than PE platforms — has an agent gone live inside a PE-backed operator yet? If yes, it moves to a differentiation capability and we press it in competitive queries instead of treating it as a gap. (3) Merge or add. Is there a capability buyers ask about that isn't here, and should Prebuilt Accelerators collapse into M&A Onboarding, given the playbook is the accelerator?
12 pain points: 8 high, 4 medium severity. The buyer language below is literally how the audit queries will be phrased — buyers don't search in your vocabulary, they search in their frustration.
→ Validate (1) Eight of twelve are rated high — which one actually opens the deal? A severity distribution this top-heavy usually means the site addresses every pain with equal urgency, which is good positioning and bad prioritization signal. If "the board wants KPIs two weeks after close" is the sentence that gets a CFO on a call and "shadow AI" never is, we weight discovery queries toward the former and treat the rest as expansion topics. (2) Is the buyer language yours or ours? These phrasings came off your site, so they're your framing of the buyer's words — if a real CFO says "we can't close the books" rather than "the numbers don't tie," we should query the words they use. (3) What's missing? Three that show up in PE platform deals and aren't here: lender and covenant reporting (if the debt package requires reporting the current stack can't produce, that's a hard deadline, not a preference); exit and QoE readiness (if a diligence data room three years out is what actually funds this work today); and the 100-day plan clock (if the sponsor's post-close timeline is the forcing function rather than any operational pain). Do any of those come up?
All 12 live pages on relicai.co were crawled and analyzed on July 30, 2026. Eight findings: 2 high, 4 medium, 2 low. No critical blockers.
Actionable now — engineering There is no emergency here: nothing is blocking AI crawlers, and every page serves clean, fully rendered content at HTTP 200. The problem is the opposite — nothing is pointing crawlers at the site in the first place. Three items your engineering team can start this week, in priority order: (1) publish a sitemap.xml covering all 12 live URLs with accurate lastmod values, since /sitemap.xml and /sitemap_index.xml both 404 today; (2) publish a minimal robots.txt with a Sitemap: directive — robots.txt currently 404s, so nothing is blocked but there is also no map to hand a crawler; (3) convert /blog from a client-side "Redirecting" page to a server-side HTTP 301 to /resources. Separately, Marketing should register the property in Google Search Console and Bing Webmaster Tools to establish why the domain returns zero results today. Robots.txt status is confirmed, not unknown: all seven AI crawlers we check are implicitly permitted — but confirm with the client that leaving training crawlers like Google-Extended and Bytespider fully permitted is a decision, not an accident.
What we found: Searches for the bare domain (relicai.co) and for site:relicai.co returned zero pages from the domain. Results were dominated by unrelated entities that share the name — New Relic (observability), RelicAI (an antique-identifier iOS app by Zytnec LLC), and Relicx (AI test automation). The site itself is live and serves content correctly on direct fetch: all 12 pages we requested returned HTTP 200 with fully rendered body text. So this is a discovery and indexation problem, not a hosting or availability problem. It compounds with two other findings in this report: there is no sitemap.xml and no robots.txt, so crawlers have no declared entry point beyond following links from the homepage.
Why it matters: Nearly all AI answer engines either retrieve from a search index at query time (ChatGPT browse, Perplexity, Google AI Overviews) or train on crawled corpora. Content that is not in any index cannot be retrieved and cannot be cited, no matter how strong it is — and Relic AI's two integration deep-dives are genuinely strong, citable assets. The name collision makes this worse than a normal cold-start: when a model does reach for "Relic AI", the surrounding training data points at New Relic, so the brand has no clean entity to resolve to. Until the site is indexed, the audit will measure category-level and problem-level visibility only, with effectively zero unaided brand recall.
Recommended fix: Verify indexation status in Google Search Console and Bing Webmaster Tools — confirm the property is registered, check the Pages report for "Discovered - currently not indexed" or "Crawled - not indexed" states, and submit the homepage for manual indexing. Publish a sitemap.xml and submit it in both consoles (see the sitemap finding below). Separately, establish off-site entity anchors so crawlers have paths in and models have something to disambiguate against: a LinkedIn company page, a Crunchbase record, and a G2 or Clutch profile, all using the exact string "Relic AI" plus the domain. Re-check indexation 2-3 weeks after the sitemap is submitted.
What we found: /sitemap.xml returns HTTP 404 on both the apex domain and the www subdomain. /sitemap_index.xml also returns 404. Because robots.txt is also absent, there is no Sitemap: directive anywhere to point crawlers at an alternate location. Every page on the site is therefore discoverable only by following links from the homepage. We confirmed 12 live pages by crawling navigation: the homepage, /solutions, five industry pages under /solutions/, /assessment, /resources, and three articles under /resources/.
Why it matters: A sitemap is the primary mechanism for telling a crawler which URLs exist and when each was last modified. Without one, discovery depends entirely on link-following, and the lastmod signal — which several AI retrieval systems weight heavily when deciding whether content is current — is unavailable for every page on the site. This is the single most actionable contributor to the indexation problem above, and it is cheap to fix. It also matters disproportionately here because 8 of 12 pages carry no visible date anywhere in their rendered output, so lastmod in a sitemap would be the only freshness signal a crawler could read for those pages.
Recommended fix: Generate and publish a sitemap.xml at https://relicai.co/sitemap.xml covering all 12 live URLs, with an accurate <lastmod> for each. Most static-site and CMS toolchains generate this automatically — enable it rather than hand-authoring, so lastmod stays accurate as pages change. Reference it from robots.txt via a Sitemap: line, and submit it in Google Search Console and Bing Webmaster Tools. Confirm the apex/www canonical choice is consistent between the sitemap URLs and the URLs actually served.
What we found: Fetching https://relicai.co/blog returns a page with body content reading "Redirecting to: /resources" and a single link to /resources, rather than an HTTP 301 or 308 response with a Location header. The redirect is executed client-side (meta refresh or JavaScript) after a 200-level response. By contrast, genuinely absent paths on this site behave correctly — /about, /pricing, /contact, /privacy, and /llms.txt all returned clean HTTP 404s, so the site does not have a general soft-404 problem. This is specific to the /blog path.
Why it matters: Crawlers treat a 200 response as a real page. A client-side redirect means /blog can be indexed as a near-empty page containing only the word "Redirecting", and any link equity or citation pointing at /blog does not consolidate onto /resources. AI crawlers in particular often do not execute JavaScript, so a bot following an external link to /blog sees a content-free page and stops there rather than reaching the three articles. Since /blog is the conventional path a person or bot would guess for this site's article index, this is a live dead-end on the most likely entry point to the site's best content.
Recommended fix: Replace the client-side redirect with a server-side HTTP 301 from /blog to /resources, and apply the same treatment to /blog/ with a trailing slash and to any legacy /blog/{slug} paths if articles previously lived there. Verify with curl -sI https://relicai.co/blog that the response is 301 with Location: /resources.
What we found: The five industry pages under /solutions/ are built on one template and vary mainly in their opening section. Body prose excluding navigation and footer runs approximately 280-300 words on /solutions/equipment-rental-field-service, ~280 words on /solutions/professional-services, ~300 words on /solutions/manufacturing-distribution, and ~330 words on /solutions/family-owned. Only /solutions/private-equity is substantive at roughly 520 words with concrete specifics (the $1M+/yr in-house data team cost, the 14-day board KPI expectation, the 1-2 day acquisition onboarding claim). Three H3 blocks — "One source of truth", "Reporting you believe", and "AI that compounds" — plus the closing "Connect it, trust it, then automate it" section appear word-for-word on all five pages. The /solutions index (~145 words) and /resources index (~190 words) are also thin. Measured content_depth scores: /solutions/professional-services 0.35, /solutions/manufacturing-distribution 0.35, /solutions/family-owned 0.40, /solutions/equipment-rental-field-service 0.45, /solutions 0.30, /resources 0.25.
Why it matters: These pages are the site's only vertical-specific commercial surfaces, and they target three of the five KG buyer personas directly — the COO in field services, the CFO in multi-entity operators, and the FP&A director. At 280-330 words with the majority of that text shared verbatim across siblings, there is not enough distinct, specific material for a model to extract a passage that answers a vertical-specific buyer question. When a retrieval system does surface one of these pages, the near-identical body text gives it no reason to prefer the correct vertical page over its siblings. The contrast with the site's own articles is stark: /resources/quickbooks-desktop-integration and /resources/computerease-integration score 0.95 on depth because they make specific, falsifiable technical claims. The industry pages make none.
Recommended fix: Bring the four thin industry pages up to the standard /solutions/private-equity already sets. For each, add 400-600 words of vertical-specific substance the shared template cannot provide: the named source systems that vertical actually runs (for equipment rental — the dispatch, telematics, and rental-management products by name; for manufacturing — the specific ERPs), the two or three metrics that vertical fights over, and a shaped proof point with a number. Replace the three verbatim-shared H3 blocks with vertical-specific equivalents, or move that shared material to a single canonical page and link to it. Prioritize /solutions/equipment-rental-field-service, since the KG identifies field services as a live proof area and the homepage already claims a multi-branch equipment rental engagement.
What we found: /robots.txt returns HTTP 404 on both the apex domain and the www subdomain. No AI crawler is blocked — with no robots.txt present, GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, Google-Extended, Googlebot, and Bytespider are all implicitly permitted to crawl the entire site. All seven are recorded as not_mentioned in the crawler status.
Why it matters: The permission posture here is already correct, so nothing is being blocked and no visibility is being lost today. It is filed as low severity for that reason. The practical cost is twofold: there is no Sitemap: directive to point crawlers at a sitemap once one exists, and the client has made no explicit, recorded decision about AI training crawlers. Some firms deliberately allow retrieval crawlers while disallowing training crawlers such as Google-Extended; right now that choice is being made by default rather than on purpose, and it would be worth confirming that full access is what Relic AI actually wants.
Sitemap: directive exists, every crawler arriving at relicai.co has to find its own way around, which slows how quickly new PE and field-services pages become eligible to answer queries like "interim data leadership for a portfolio company."Recommended fix: Publish a minimal robots.txt at https://relicai.co/robots.txt containing User-agent: * / Allow: / and a Sitemap: https://relicai.co/sitemap.xml line once the sitemap exists. Confirm with the client that leaving all AI crawlers — including training crawlers like Google-Extended and Bytespider — fully permitted is the intended posture, and record that decision. Given the visibility position described elsewhere in this report, full access is almost certainly the right call, but it should be a decision rather than an accident.
The following items could not be assessed through our analysis method (rendered markdown). We recommend your engineering team verify these manually before the validation call.
What to check: /assessment returned only ~75 words of body prose. The page shows a single H1 ("AI maturity is capped by data maturity."), no H2 or H3 headings at all, an intro paragraph, three stat labels ("12 questions · ~3 minutes", "6 dimensions scored", "0-5 maturity tier + roadmap"), and a "Question 1 / 12" placeholder with a note reading "Auto-advances on select". The 12 assessment questions themselves and the 0-5 tier descriptions and roadmap output were not present in the rendered text. This pattern is consistent with the interactive content being injected by JavaScript after page load, though we cannot confirm that directly — our analysis reads rendered markdown, not raw HTML or a JavaScript-disabled render. Every other page on the site rendered its full body text on fetch, so this is an isolated page rather than a site-wide rendering failure.
Recommended action: Load https://relicai.co/assessment with JavaScript disabled, and separately run curl -s https://relicai.co/assessment | wc -w to see what is in the raw HTML response. If the questions and tier descriptions are missing from the server response, either server-render the assessment content or — simpler and probably better for visibility — publish the tier framework and the six scoring dimensions as static, crawlable prose on the same page beneath the interactive widget. The framework already exists in static form on the homepage and in /resources/ai-maturity-is-capped-by-data-maturity, so this is largely a matter of surfacing existing copy rather than writing new material.
What to check: Our analysis method reads rendered page text rather than raw HTML, so JSON-LD and microdata blocks are not visible to it. We therefore cannot state whether any of the 12 pages carry structured data, and schema_coverage is recorded as null for every page. What we can say is that the content on several pages maps cleanly onto specific schema types that would be straightforward to add: /resources/quickbooks-desktop-integration and /resources/computerease-integration each end with a genuine multi-question FAQ section (five and six question-answer pairs respectively), and all three articles carry a visible publication date of June 18, 2026. Organization and WebSite markup on the homepage also serve a specific purpose here — they are one of the clearest ways to assert entity identity against the New Relic name collision.
Recommended action: Run the homepage and one representative page from each template through Google's Rich Results Test and Schema.org's validator to establish what exists today. Then add, in priority order: Organization plus WebSite markup on the homepage with the legal entity name, logo, and sameAs links to any official profiles; Article markup with datePublished and dateModified on all three /resources articles; FAQPage markup on the two integration articles, whose FAQ sections are already written in the right shape; and Service markup on the five industry pages.
What to check: Meta description tags, Open Graph tags, Twitter Card tags, canonical URLs, and meta robots directives sit in the HTML head and are not present in rendered page text, so our method cannot see them. meta_description and has_og_tags are recorded as unverified for every page. One related item we could observe: each page's title string follows a consistent "{Page name} — Relic AI" pattern, which is a good sign for title-tag hygiene. Canonical tags matter more than their low severity suggests on this site, because the apex and www hostnames both resolve and a mismatch can split signals across two URL sets.
Recommended action: Check the raw HTML head of the homepage and one page per template with view-source or a crawler such as Screaming Frog. Confirm each page has a unique meta description, a self-referencing canonical pointing at the chosen apex-or-www host, and complete og:title, og:description, and og:image tags. Preview the three /resources article URLs in a social debugger to confirm they render with a title and image when shared.
Coverage caveat Page coverage is complete — all 12 discoverable pages were analyzed — but two metrics could not be scored. Schema coverage is null for all 12 pages and freshness is null for 8 of 12, because our method reads rendered page text rather than raw HTML and those 8 pages carry no visible date. Both gaps are covered by the manual verification items above; treat the schema line as "unknown," not "absent," until the Rich Results Test confirms it either way.
Why now Timing matters more in this category than in most.
• AI search adoption among B2B buyers is accelerating quarter over quarter — the discovery behavior that used to start on Google now starts in an assistant, and the shift is happening faster than most services firms are adapting to it.
• Early citations compound. Domains that AI platforms learn to trust get cited more often as retrieval and training corpora accumulate, and that advantage is self-reinforcing rather than linear.
• Being in the consideration set before the search starts is most of the game: 95% of winning vendors were already on the buyer's Day One shortlist across nearly 4,000 B2B purchase decisions (6sense, November 2025). AI answers are increasingly what builds that Day One list.
• The mechanics favor a challenger here. Brand web mentions predict AI citation far better than backlinks (r = 0.664 vs. r = 0.218 — Seer Interactive, October 2025), which means the moat isn't twenty years of domain authority.
• PE data & AI consulting is still early-innings in GEO. Almost no firm in your competitive set has optimized for this, so acting now means competing against inaction rather than against entrenched strategies.
The full audit will measure citation visibility across the buyer queries your market actually runs — "how do we consolidate reporting across eight operating companies," "interim CDO for a PE-backed platform," "get data out of QuickBooks Desktop and a construction ERP with no API," "alternatives to a Big 4 data engagement" — across the selected AI platforms. You'll see exactly which of those return Accordion, West Monroe, OneSix, phData or Deloitte and not Relic AI, which pages the engines cite when they answer, and what it would take to be in that answer instead. Fixing the sitemap, the /blog redirect, and the indexation gap before the queries run means the audit measures a site that can actually be found, rather than measuring the consequences of not having a sitemap.
45–60 minutes. We walk this document top to bottom, work the Pre-Call Checklist, and lock the competitive set, the persona set, and the capability weighting the query set is built from.
We generate buyer queries from the validated graph — discovery, comparison, capability, and problem-language clusters — and run them across the selected AI platforms, capturing every citation and mention.
Visibility analysis, competitive positioning against the validated set, and a three-layer action plan prioritized by which gaps actually cost you citations — not by which ones are easiest to spot.
Start now — no need to wait for the call Three Layer 1 items your engineering team can ship this week, plus one for marketing. (1) Publish sitemap.xml at relicai.co/sitemap.xml covering all 12 live URLs with accurate lastmod — generate it from your toolchain rather than hand-authoring, and confirm the apex/www choice matches what you actually serve. (2) Replace the /blog client-side redirect with a server-side 301 to /resources, including the trailing-slash variant, and verify with curl -sI https://relicai.co/blog. (3) Publish a minimal robots.txt with User-agent: * / Allow: / and a Sitemap: line — robots.txt currently returns 404, so nothing is blocked, but confirm internally that leaving training crawlers like Google-Extended and Bytespider permitted is a deliberate decision. (4) Marketing: register relicai.co in Google Search Console and Bing Webmaster Tools and read the Pages report — this is how we find out why the domain returns zero search results today. None of these depend on the rest of the audit, and all of them improve your baseline visibility before we even measure it.
Two jobs before we meet. The questions on the left require your judgment — no one knows your business better than you. The engineering tasks on the right don't require the call at all.
curl -sI https://relicai.co/blog. Closes a dead end on the most-guessed path to your best content. Effort: < 1 day.curl -s https://relicai.co/assessment | wc -w. Effort: 1–3 days.