Here is a finding that should change where B2B software brands spend their next quarter. When researchers analysed the tools that ChatGPT actually names in its software answers, 100% of them had reviews on Capterra and 99% had reviews on G2 (Quoleady, 2026 research). Not most. Effectively all. If your tool isn’t listed on these platforms, you are not competing for the AI recommendation — you have been excluded from the shortlist before the question is even asked.
Sit with that for a second, because it inverts how most brands think about review sites. The traditional view treats a G2 profile as a marketing nice-to-have — somewhere to collect testimonials and a star rating to screenshot for the sales deck. The AI-era reality is that it functions more like a business license: not having one doesn’t just weaken your case, it removes you from consideration entirely. When over half of B2B buyers now start their software search inside an LLM, the tools that aren’t reviewed simply never enter the conversation that increasingly decides the purchase.
But here is the counter-intuitive part, and it is where almost every brand gets the strategy wrong: those same review platforms are an inclusion gate, not a ranking lever. The research is blunt — even tools with strong review presence sometimes ranked below tools with fewer reviews, weaker ratings or less brand visibility (Quoleady). Reviews get you into the room. Something else decides whether you win it. Treating G2 as a place to accumulate the most reviews is optimising the wrong variable; treating it as critical infrastructure you must have, then layering the real ranking signals on top, is the move.
And the ground just shifted under the entire category. In February 2026, G2 completed its acquisition of Capterra, Software Advice and GetApp from Gartner for roughly $110 million, consolidating more than six million verified reviews and 200 million annual software buyers across 2,000-plus categories into a single ecosystem (TrustSignals). One company now controls four of the high-authority review domains that LLMs lean on when buyers are closest to purchase. This guide explains what that means, how the citation mechanics actually work, and exactly how to build review-site presence into an AI-citation asset — without wasting effort on the metrics that don’t move the needle. For the wider map, our Ahrefs 17M AI citation study breakdown and 2026 link building statistics set the context.
The deliverable: the Review Platform Presence Score (RPS)
Because reviews are a gate first and a lever second, the right question is not “how many reviews do we have?” but “are we present, structured and credible on the platforms our buyers’ AI actually cites?” The Review Platform Presence Score (RPS) answers that on a 0–100 scale across the five factors the 2026 studies repeatedly tie to AI citation.
RPS = (0.30·C) + (0.25·D) + (0.20·V) + (0.15·F) + (0.10·R)
Each factor is scored 0–100:
- C — Coverage. Are you on the right platforms for your buyer (the gate)? Present on 2+ relevant platforms scores high; absent scores ~0 and effectively zeroes your AI eligibility.
- D — Description quality. Does your profile answer buyer questions in natural language (“project management for agencies of 10–50 people”) rather than sell (“next-gen innovative solution”)?
- V — Review volume threshold. Have you crossed the credibility thresholds that correlate with citation (broadly, 100+ reviews on your primary platform)?
- F — Freshness & cadence. Are reviews recent and arriving steadily? A stream of current reviews signals an active, real product.
- R — Rating credibility. Is the rating strong but believable, with verified, moderated reviews rather than a suspicious wall of fives?
Coverage carries the most weight on purpose: it is the gate, and no amount of description polish rescues a brand that simply isn’t listed. A B2B SaaS company present on G2 and Capterra, with 150+ verified reviews, a question-shaped description and a steady cadence might score RPS 85 — fully eligible, now compete on the real ranking signals. A company with a thin, sales-heavy single profile and 12 stale reviews scores RPS 30 — fix the gate before anything else. The score tells you which problem you actually have.
Reading your RPS
| Band | Meaning | First move |
| 0–30 | Excluded. Likely absent or near-empty profiles. | Claim and complete profiles on your 2 priority platforms now. |
| 31–60 | Eligible but weak. In the room, easily outranked. | Cross the volume threshold; rewrite the description for buyers. |
| 61–85 | Competitive. Cited for several category prompts. | Sustain cadence; layer on the real ranking signals. |
| 86–100 | Infrastructure-grade. A reliable peer-proof source AI trusts. | Defend; expand to a category-specific third platform. |
Score yourself against the two competitors who out-cite you. The tooling that reveals which review domains the LLMs pull from for your category is covered in our best link building tools guide.
A worked RPS example
Take a mid-market project-management SaaS. It’s on G2 and Capterra (Coverage = 90), but its description reads “the world’s most innovative work platform” (Description = 25), it has 140 verified reviews (Volume = 85), reviews arrive a few times a month (Freshness = 70), and its 4.4 rating looks credible (Rating = 80). RPS = (0.30×90)+(0.25×25)+(0.20×85)+(0.15×70)+(0.10×80) = 27 + 6.25 + 17 + 10.5 + 8 = 68.75 — competitive, but the formula points straight at the weak link: the description. A single afternoon rewriting it into buyer language (“project and resource management for agencies of 10–50 people, with built-in profitability tracking”) lifts Description to ~90 and pushes RPS past 84 — into infrastructure-grade — without earning a single new review. Contrast a competitor absent from both platforms: Coverage = 5, and no matter how polished everything else is, RPS collapses below 25. The gate dominates, exactly as the data says it should.
The data: how much do review sites actually move AI citation?
Five independent findings, and they converge on the same shape — review presence is a powerful multiplier with a clear floor and a clear ceiling:
| Finding | Source |
| Profiles on 2+ review platforms = 3.4x more likely mentioned in ChatGPT vs none. | Capterra / AISO analysis |
| Domains on multiple review platforms earned 4.6–6.3 citations on average vs 1.8 for absent domains. | SE Ranking 129K-domain study |
| 100% of ChatGPT-named tools had Capterra reviews; 99% had G2 reviews (inclusion gate). | Quoleady 2026 research |
| Products with 100+ G2 reviews = 3.2x more likely mentioned than those with <20. | Am I Cited |
| A 10% increase in reviews correlated with a ~2% increase in AI citations. | Am I Cited |
Sources: AISO; SE Ranking via Contently; Quoleady; Am I Cited. Read them together and the picture is unambiguous: absence is near-fatal, presence roughly triples your odds, and volume helps up to a credibility threshold — after which other signals take over.
There is one more number worth sitting with. Over 50% of B2B decision-makers now begin their software purchase journey in an LLM rather than traditional search (G2 Buyer Report 2025). The bottom-of-funnel question — “best CRM for a 200-person company,” “Brand A vs Brand B” — is increasingly asked of ChatGPT first, and the sources it cites become the buyer’s shortlist. Review sites are where that shortlist is sourced, which is precisely why they function as infrastructure rather than as a marketing nicety.
The inclusion gate vs the ranking lever: the distinction that changes everything
This is the single most important idea in the article, and the one that separates brands that waste their review budget from brands that compound it. Think of AI software recommendation as a two-stage filter. Stage one is eligibility: is this tool even a candidate the model will consider? Stage two is selection: of the eligible candidates, which ones make it into the answer, and in what order? Review-site presence governs stage one almost completely and stage two only partially.
The evidence for stage one is overwhelming: essentially 100% of ChatGPT-named tools were reviewed on Capterra, 99% on G2. Being unreviewed is like applying for a job without a CV — you’re not rejected, you’re simply never considered. But the evidence for stage two is just as clear and far more often ignored: even tools with strong review presence sometimes ranked below tools with fewer reviews, weaker ratings or less brand visibility. Reviews don’t break the tie between eligible candidates; backlinks, brand mentions, messaging consistency and machine-readable formatting do.
The practical implication reorganises your priorities. If you’re absent, every dollar goes to clearing the gate — nothing else matters until you’re eligible. Once you’re past it with a credible profile, additional review-count spending hits sharp diminishing returns (recall: a 10% review increase mapped to roughly a 2% citation increase), and the marginal dollar is far better spent on the off-platform authority signals that actually decide selection. Most brands get this exactly backwards: they keep grinding for review number 300 while a competitor with 90 reviews and a stronger brand-mention and backlink profile quietly takes the top recommendation slot. The gate is necessary; the lever is decisive; confusing the two is the most expensive mistake in the category.
The G2 consolidation: why one acquisition reshaped the category
The Gartner divestiture matters more than a typical acquisition because of what it concentrates. G2 now controls G2, Capterra, Software Advice and GetApp — and G2 already had the highest AI citation density in the category, reporting 2.6x more AI citations than other software review platforms for B2B queries and roughly 10x year-over-year growth in LLM-referral traffic (TrustSignals). Analysts modelling the deal projected G2’s citation share in bottom-of-funnel prompts could rise as much as 76% once the ecosystem’s citation surfaces are combined (Omniscient Digital).
The strategic consequence is twofold. First, the G2 ecosystem is now the single most important review surface for B2B software AI citation, full stop — a presence there touches four domains at once. Second, concentration is also a risk: when one company controls so much of the citation surface, your AI shortlist eligibility increasingly depends on a single relationship and a single set of rules. That is an argument for owning your G2 presence thoroughly while deliberately diversifying onto independent platforms like Trustpilot and category-specific sites, so your visibility does not rest entirely on one consolidated gatekeeper.
It’s worth naming the deeper dynamic, because it echoes a pattern across the whole AI-citation landscape. Citation surfaces are consolidating — a handful of domains now capture the majority of AI citations, with the top 15 alone accounting for around 68% of all citation share. The G2 deal is that consolidation playing out inside the review category specifically. For a vendor, the upside is efficiency: a strong G2 presence now propagates across four domains at once. The downside is dependency: when so much eligibility flows through one company’s rules, a policy, pricing or algorithm change on their side can move your AI visibility without you doing anything wrong. The mature response is not to avoid G2 — it’s indispensable — but to treat it as a powerful rented asset, build it thoroughly, and pair it with independent surfaces you don’t share with a single gatekeeper. Over-reliance on any one consolidated source is the risk, not the platform itself.
Why AI trusts review sites: five structural reasons
1. Verified, accountable third-party validation
AI systems look for consensus across independent sources, and review platforms carry a trust signal that a vendor’s own marketing — or an anonymous forum post — cannot. A G2 review requires LinkedIn verification; a Capterra review goes through moderation; Trustpilot removed nearly eight million fake reviews last year, nine in ten caught automatically (Blastra). These aren’t perfect systems, but they are systems with someone accountable — which is exactly what makes their content a higher-trust citation than the open web.
That accountability is the crux. When an AI weighs whether to trust a claim about a product, it implicitly asks how easy that claim would be to fake. A vendor’s homepage is trivially self-serving. An anonymous forum post could be a competitor or a shill. But a verified, moderated review on a platform with a public, investor-facing anti-fraud programme sits at a different tier of trust — there is a named, accountable system standing behind it. This is why review-site content punches above its raw volume: it’s not just more data, it’s data with provenance, and provenance is precisely what a model reaches for when the stakes of a wrong recommendation are real.
2. Structured, machine-readable data at scale
Review platforms present standardised schema: category, rating, feature breakdowns, company size, use case, pros and cons. That structure is trivially easy for a retrieval system to parse and compare across vendors, which is why LLMs reach for it when assembling a recommendation. It is the same machine-readability advantage that makes structured formats win everywhere in AI search.
Consider what a model has to do to answer “best help-desk software for a 50-person SaaS company.” It needs to identify candidates, compare them on relevant dimensions, and justify a recommendation. A vendor’s marketing site gives it a one-sided pitch in inconsistent formats. A review platform gives it dozens of products described in the same fields, with comparable ratings, segmented by exactly the company-size and use-case dimensions the query specifies. The platform has effectively pre-structured the comparison the model needs to make — which is why review data so often becomes the backbone of an evaluation-stage answer. The lesson for vendors is to make sure your profile populates every one of those structured fields accurately, because empty or vague fields make you harder to place in the comparison.
3. They sit exactly at the bottom of the funnel
Review-site citations cluster on high-intent, evaluation-stage queries — the moment a buyer is choosing between options. The data confirms review sites are most effective at the bottom of the funnel, where peer proof carries the most weight (G2 / Learn). That makes them disproportionately valuable: they influence the answer at the exact instant a purchase decision is forming.
4. Freshness by construction
Active products accumulate a steady stream of dated, current reviews — Trustpilot users alone wrote 62 million reviews in 2025, more than the platform’s first twelve years combined (Blastra). That constant freshness feeds the recency preference AI systems show, and signals that a product is alive and in active use rather than abandoned.
5. Consensus across many independent voices
A single testimonial is weak; a hundred verified reviews converging on the same strengths is strong. AI weights the aggregate — the consensus view of many independent users — because it is far harder to fake than any individual claim. This is the same reason community consensus on Reddit carries such weight: independent voices agreeing is a trust signal no marketing page can manufacture.
Which platform, for which buyer
The platforms are not interchangeable — each owns a buyer profile, and pouring effort into the wrong one is wasted. Match platform to market:
| Platform | Owns | Prioritise if |
| G2 | B2B SaaS, US/UK; highest AI citation density | You sell software — start here, always. |
| Capterra | SMB & European software buyers (Gartner-owned, now G2) | You target SMEs or European markets. |
| Trustpilot | B2C reference, strong in Europe; ~5th most cited on ChatGPT | You have a meaningful consumer surface. |
| TrustRadius / PeerSpot | Enterprise & IT | You sell to large enterprise or IT. |
| Gartner Peer Insights | Regulated industries, large-enterprise procurement | Your buyers are in regulated procurement. |
| Clutch | Services & implementation | You have a services component. |
The most defensible structure is tiered: G2 as the foundation, one category-specific platform for your buyer profile, and a third for range (Trustpilot for B2C reach, SourceForge for developer audiences). The governing rule from the practitioners who study this: better to have 80 reviews on one platform than 15 on four. Master one, then expand. Geography matters too — for non-Western markets the platform mix shifts substantially, as our India and South Asia playbook illustrates.
The playbook: building review-site citation infrastructure
Move 1: Win the inclusion gate first
Before anything else, make sure you are present and complete on your two priority platforms. Absence is the only truly fatal state — recall that essentially 100% of ChatGPT-named tools had Capterra and G2 reviews. Claim your profiles, fill every field, and treat this as non-negotiable infrastructure, not a marketing experiment. Until the gate is cleared, no other optimisation matters.
Move 2: Write the description for the buyer’s question, not the pitch
This is the highest-leverage profile edit. Brands that appear most in ChatGPT answers describe themselves in the buyer’s own language — “project management for agencies of 10–50 people with integrated profitability tracking” beats “innovative next-gen solution” every time (AISO). Because AI receives specific questions, your profile should contain specific answers: who it’s for, what it does, the use cases, the company sizes. Mirror the queries your buyers actually type.
Think of your profile description as answer-shaped raw material. When a buyer asks “best CRM for a 200-person healthcare company,” the model is pattern-matching that query against the structured text it can find — and a description that explicitly names the company size, the industry and the use case is a direct hit, while a vague slogan is invisible. The discipline is to enumerate the specifics a buyer would put in their prompt: team size ranges, industries served, the two or three jobs the product is hired to do, the integrations that matter, and the type of company it is wrong for. Naming who it’s not for is counter-intuitively powerful, because it helps the model place you precisely rather than vaguely — the same honesty-signals-trust dynamic that makes qualified community answers so citable.
Move 3: Cross the volume threshold, then stop chasing volume
Get past the credibility thresholds — broadly 100+ reviews on your primary platform, which correlates with a 3.2x lift versus thin profiles. But remember the inclusion-gate finding: beyond the threshold, more reviews show diminishing returns, and ranking is decided by other signals. Request structured reviews from satisfied customers steadily; don’t pour the whole budget into a review-count arms race you can’t win on volume alone.
Move 4: Keep reviews fresh and credible
A steady cadence of recent, verified reviews beats a one-time burst. Build review requests into your customer lifecycle — post-onboarding, post-renewal — so the stream stays current. Never buy or fake reviews: the platforms’ detection is aggressive (Trustpilot caught millions automatically), and a credible-but-imperfect rating profile is far more citable than a suspicious wall of perfect fives.
Move 5: Layer the real ranking signals on top
Once you’re through the gate, what actually decides AI ranking is broader: backlink strength, mention relevance, consistent messaging and machine-readable formatting (Quoleady). In other words, your review presence makes you eligible; your wider earned-authority programme wins the placement. This is where review-site work connects to everything else — the brand mentions and links covered in what link building is in 2026, the professional authority of LinkedIn publishing, and the demonstration value of YouTube video.
Move 6: Treat badges as portable proof
G2 Grid placements — Leader, High Performer, Momentum Leader — function as third-party validation badges that travel into press releases, sales decks and comparison pages, each of which becomes another citable surface. Earn the badge, then propagate it across your owned and earned channels so the same validation shows up wherever AI might look. A badge referenced in an earned-media campaign or a guest post compounds the signal.
The mechanism here is corroboration. When the same “Leader in [category]” claim appears on the review platform, in a press release picked up by trade media, on your own comparison pages, and in third-party roundups, an AI encounters the same validated fact from multiple independent-looking surfaces — and consensus across sources is exactly what it weights. A badge that lives only on your G2 profile is one data point; the same badge propagated thoughtfully across a dozen surfaces becomes a pattern the model treats as established fact. This is the point where review-site work stops being a siloed task and becomes part of the same earned-authority flywheel that powers your whole programme: every credible surface that repeats your validated position strengthens every other one.
The Monday-morning checklist
| # | Lever | Why | Proof metric |
| 1 | Claim + complete 2 priority profiles | Win the inclusion gate | 100% fields complete on G2 + 1 |
| 2 | Rewrite description in buyer language | AI matches questions to answers | Use case + company size stated |
| 3 | Cross 100-review threshold | 3.2x citation lift | Reviews on primary platform |
| 4 | Build review cadence into lifecycle | Freshness signal | New reviews per month |
| 5 | Layer links + mentions + formatting | Decides ranking, not just inclusion | Earned mentions per quarter |
| 6 | Propagate badges across channels | Portable third-party proof | Badge placements earned |
What the data shows vs what most brands believe
Belief: “More reviews = higher AI ranking.”
The data: reviews are an inclusion gate, not a ranking lever. Past the credibility threshold, tools with fewer reviews routinely outrank tools with more, because ranking is decided by backlinks, mentions, messaging consistency and formatting. Volume gets you eligible; it doesn’t get you chosen.
Belief: “Be on every review platform.”
The data: 80 reviews on one well-chosen platform beats 15 across four. Match the platform to your buyer (G2 for B2B SaaS, Trustpilot for B2C, category sites for enterprise) and master it before expanding. Spreading thin produces thin, uncitable profiles everywhere.
Belief: “A glowing sales description will help.”
The data: promotional descriptions underperform specific, buyer-language ones. AI matches precise questions to precise answers, so “for agencies of 10–50 with profitability tracking” beats “next-gen innovative solution.” Sell less; describe more.
Belief: “We can buy our way to citation with reviews.”
The data: fake reviews are detected aggressively and a suspicious profile is less citable, not more. And paid sponsorships on these platforms don’t reliably move citation — earned, verified reviews do. The credibility of the signal is the whole point.
A reproducible teardown: find the review sites shaping your category
Map your exposure in an afternoon:
- List your 15–20 highest-intent BOFU queries — “best X for [company size/industry],” “X vs Y” — in real buyer language including role and context.
- Run each on ChatGPT, Perplexity and Google AI Mode, three times across different days, since AI answers vary.
- Log which review domains are cited (G2, Capterra, Trustpilot, etc.), which competitors are named, and on which platforms those competitors are strongest.
- Score your own RPS on each cited platform and compare to the named competitors — the gap is your priority list.
- Identify the gate failures first: any priority platform where you’re absent or near-empty. Fix those before optimising anything.
- Then identify the ranking gaps: prompts where you’re listed but a thinner-reviewed competitor outranks you — that’s a signal to strengthen the off-platform signals from Move 5.
If AI is citing outdated or negative review data about you, that’s a recovery problem; the diagnostic sequence is in our guide to AI citation recovery.
When review sites aren’t your priority
- You’re not in a reviewed category. Review platforms dominate software, services and consumer products. For pure content, media or non-purchased categories, they barely apply — weight toward the sources those queries pull from.
- Your queries are top-of-funnel. Review sites win evaluation-stage, bottom-of-funnel prompts. For “what is X” or “how do I” queries, Wikipedia entity grounding and YouTube how-tos matter more.
- You’d chase volume over the gate. If your team will inevitably turn this into a review-count race, fix the strategy first — past the threshold, that effort is wasted.
- You can’t sustain authentic reviews. A profile that can’t generate a steady stream of genuine reviews will look stale or, worse, tempt you toward fakes. Build the lifecycle engine first or focus elsewhere.
- You’d treat it as the whole strategy. Reviews are infrastructure, not a programme. They make you eligible; the wider link building strategy wins the citation. Treating G2 as the finish line caps your ceiling.
A 90-day review-infrastructure sprint
Days 1–30: clear the gate
Run the teardown to see which review domains AI cites in your category and where competitors are strong. Claim and fully complete your two priority profiles, rewriting descriptions into buyer language. Score your RPS. The month-one goal is binary: are you in the room or not? Get in.
Days 31–60: build credible volume
Stand up a review-generation engine inside your customer lifecycle to cross the volume threshold with verified, recent reviews. Never fake or buy. Begin propagating any earned badges into your owned channels. Re-score RPS to confirm you’ve moved from “eligible but weak” toward “competitive.”
Days 61–90: layer the ranking signals
With the gate cleared and volume credible, shift effort to what actually decides ranking: earned mentions, links, consistent messaging and machine-readable content across your wider programme. Re-run the teardown against your Day 1 baseline, identify any remaining prompts where thinner competitors outrank you, and attack those with off-platform authority work.
Frequently asked questions
Do G2 and Capterra reviews really affect AI recommendations?
Yes, but primarily as an inclusion signal. Research found essentially 100% of ChatGPT-named tools had Capterra reviews and 99% had G2 reviews, and profiles on 2+ platforms make a brand 3.4x more likely to be mentioned. However, beyond a credibility threshold, ranking is decided by other signals like backlinks and mentions, not review count.
Which review platform should I prioritise?
G2 for B2B software, especially US/UK — it has the highest AI citation density and now owns Capterra, Software Advice and GetApp. Capterra for SMB and European buyers, Trustpilot for B2C, and category-specific platforms (TrustRadius, Gartner Peer Insights, Clutch) for enterprise, regulated or services audiences. Master one before expanding.
How many reviews do I need?
Crossing roughly 100 reviews on your primary platform correlates with a 3.2x citation lift versus thin profiles. Past that threshold returns diminish — a 10% review increase mapped to only about a 2% citation increase — so prioritise credible, fresh reviews over chasing raw volume indefinitely.
What changed with the G2 acquisition of Capterra?
G2 acquired Capterra, Software Advice and GetApp from Gartner in February 2026 for about $110 million, consolidating 6M+ reviews and 200M annual buyers. One company now controls four high-authority review domains LLMs cite, with G2’s bottom-of-funnel citation share projected to rise substantially. It strengthens G2’s position and is also an argument for diversifying onto independent platforms.
Can I just buy reviews to get cited?
No. Platforms detect and remove fakes aggressively — Trustpilot removed nearly 8 million last year, 9 in 10 caught automatically — and a suspicious profile is less citable, not more. Paid sponsorships also don’t reliably move citation. The trust signal depends entirely on the reviews being genuine and verified.
How is a review-site citation different from a Reddit citation?
Both are third-party validation, but they carry different trust signals and win different queries. Review platforms are verified, moderated and structured — a G2 review requires LinkedIn verification — which AI treats as accountable, evaluation-stage proof. Reddit is unmoderated lived experience that wins earlier, more exploratory queries. A complete strategy uses review sites for bottom-of-funnel “best X” decisions and community sources for the messier “what’s it actually like” questions.
How do I measure whether review sites are helping my AI visibility?
Run your bottom-of-funnel category prompts through ChatGPT, Perplexity and Google AI Mode repeatedly across days, and log which review domains get cited and whether your brand appears. Track your RPS on each priority platform over time, and watch for prompts where you’re listed but outranked — that gap tells you to strengthen off-platform signals rather than chase more reviews.
The bottom line
Review sites are the infrastructure layer of bottom-of-funnel AI citation. Being absent is close to disqualifying — essentially every tool AI recommends is reviewed on G2 and Capterra — but presence alone doesn’t win; it makes you eligible. The brands that get this right clear the inclusion gate first, write profiles in their buyers’ language, cross the credibility threshold with genuine reviews, and then let their wider earned-authority programme decide the ranking. After February 2026, the G2 ecosystem is the centre of gravity, and a deliberate presence there — balanced with independent platforms — is no longer optional for software brands.
If there’s one mental model to take away, it’s the gate and the lever. The gate — being present, structured and credible on the right review platforms — is binary and non-negotiable; you’re either through it or you’re invisible. The lever — the backlinks, brand mentions, consistent messaging and machine-readable authority you build everywhere else — is what actually moves you up the recommendation once you’re through. Brands fail in two directions: some never clear the gate and wonder why AI ignores them; others clear it and then pour everything into a review-count race, wondering why thinner-reviewed competitors keep beating them. Get the sequence right — gate first, then lever — and review sites become exactly what the title promises: infrastructure that quietly underwrites your presence in the answers your buyers now trust most.
Score your RPS, run the teardown, fix the gate, then compete on the real signals. And connect review infrastructure to the rest of the board: the platform breakdowns for Reddit, LinkedIn, YouTube and Wikipedia, and the benchmarks in our 2026 link building statistics. In the AI era, your buyers’ shortlist is assembled from what others say about you — and review sites are where they say it most credibly.
