Table of Contents
1. Executive Summary
The Bottom Line
On May 19, 2026, Google announced a redesigned Search experience and made Gemini 3.5 Flash the default model inside AI Mode. It did not announce AI Mode as the global default, replace ordinary Search with an entity-only ranking system, or establish a universal business impact. Normal SEO foundations remain relevant.
Key Findings
- ClickRadius joins measurement and governed delivery. The product measures five weighted AI Readiness evidence families, records separate monthly AI Presence observations from ChatGPT, Gemini, Perplexity, Claude, and Grok, prepares eligible corrections, and identifies guided off-site authority work. A supported effect is applied only with current authorization for the exact revision and a capable connection. Copilot is not currently supported.
- On-site and off-site evidence serve different jobs. A technically sound, useful website establishes first-party facts; accurate profiles, independent coverage, reviews, and citations can corroborate them. ClickRadius is designed to evaluate both evidence surfaces without assigning an unsupported universal split between them.
- Opportunity is measured, not presumed. Freeze a prompt panel and record engine, model, locale, date, mention, link, and recommendation states. A miss in that sample is not proof of universal invisibility or future market share.
- The platform is technically deep. ClickRadius combines website analysis, configured strategy selection, citation observation, entity workflows, and governed deployment paths. Customer-outcome training is not currently active. Patent Pending.
2. Google's Algorithm Revolution — What Changed
2.1 Google I/O 2026 (May 19, 2026)
Google VP of Search Elizabeth Reid called this "the biggest upgrade to our Search box in over 25 years." CEO Sundar Pichai separately described it as "our biggest upgrade to Search ever." Those descriptions establish Google's characterization, not a global rollout or outcome statistic.
AI Mode Model and Interface Changes
Google described a redesigned intelligent Search box, a flow from an AI Overview into AI Mode, and Gemini 3.5 Flash as the default model inside AI Mode. The announcement did not establish AI Mode as the global default or formally demote ordinary results, and Google said users continue to receive a range of results.
Search Box Redesigned
Google announced a redesigned Search box with AI-assisted interactions. That does not establish that autocomplete was universally replaced, that ranking became entity-only, or that a particular business will be cited.
AI Overview Coverage
Coverage varies by measurement source, query cohort and query class, collection date, locale, device, account state, and feature definition. This report does not assert a universal current percentage.
Information Agents
Google previewed Information Agents for AI Pro and Ultra subscribers with an announced summer rollout and described synthesized updates with web links. Announcement, eligibility, observed availability, task completion, displayed links, and referral behavior are separate evidence states.
2.2 The Impact — Required Measurement Record
2.3 What Can Be Inspected
Normal SEO foundations remain relevant. A broader audit can also inspect structured identity and independent public records without claiming that Google uses one universal weighting:
On-page evidence
- Crawl and index controls
- Page purpose and supported claims
- Valid structured data matching visible content
- Performance and mobile usability
- Attributable first-party evidence
Separate evidence and outcomes
- Entity records — identity, publisher, fact scope, date, and conflicts
- Independent evidence — source-controlled coverage, listings, and reviews
- Answer observations — prompt, engine, model, response, links, and mentions
- Referral evidence — classified sessions with bot filtering
- Customer outcomes — defined events and attribution windows
- Uncertainty — missing evidence remains unknown rather than zero
2.4 Citation, Referral, and Outcome Separation
A displayed citation is not itself a referral or a business outcome. A defensible record keeps these stages distinct:
- Answer: retain the raw response and displayed source or mention.
- Referral: classify a real session with a declared referrer rule and bot filtering.
- Outcome: retain a defined lead or revenue event and its attribution window.
- Causation: require an appropriate comparison design; temporal sequence alone is insufficient.
2.5 Evidence Principles
Expert commentary can motivate a hypothesis, but a person's reputation is not evidence for a private engine mechanism or a customer outcome. A reusable claim needs its exact source, wording, scope, date, and applicability; an attributed paraphrase without that record is not used here as authority.
Useful first-party content, accurate structured data, and independently controlled public records are distinct review surfaces. Whether a named engine crawls, retrieves, selects, cites, or refers traffic from any one of them requires direct observation under a declared protocol.
3. On-Site Foundations and Off-Site Corroboration
AI and search systems can encounter both first-party website content and independent information elsewhere on the web. The relative influence varies by engine, query, market, and time, so this report treats the two surfaces as complementary evidence rather than assigning them a universal percentage.
3.1 On-Site Foundation Scope
A conventional technical and content audit can inspect a publisher-controlled website: crawl directives, metadata, structured data, performance, accessibility, content, and internal links.
Those observations establish the measured site state only. They do not establish how an external engine will rank, retrieve, cite, recommend, or send traffic to the page.
3.2 The Expanded Model: Independent Sources Can Corroborate First-Party Facts
Independent public records create additional observable evidence surfaces beyond the publisher's own website. Whether a particular engine crawls, retrieves, uses, or cites any record remains provider-controlled and must be measured rather than inferred:
On-Site Optimization
Schema markup, meta tags, content quality, technical SEO, page speed, mobile optimization
Publisher-controlled inputs; external effect unproven
Off-Site Entity Authority
- Directory presence — Data Axle, Foursquare, Bing Places, and industry-specific directories
- Knowledge Graph entity — Google KG, Wikidata, and linked data sources
- Social authority signals — LinkedIn, Reddit, and platform-specific content
- Attribute consistency — NAP (Name, Address, Phone) comparison across the declared, successfully observed platforms
- Third-party mentions — press, reviews, industry publications, forums
- Cross-platform attribute comparison — agreements, conflicts, unavailable lookups, and source ownership kept distinct from identity or engine recognition
Independent profiles, editorial references, reviews, and directory records can corroborate identity and business facts found on a company's own site. Their effect cannot be reduced to one universal citation-rate comparison: ClickRadius must observe each engine, query set, and time window before reporting an outcome.
3.3 Why This Can Matter for a Local Business
Consider a local PI attorney. A bounded on-site audit might:
- Check their site's meta tags and schema markup
- Score their page speed and mobile responsiveness
- Analyze keyword density
- Generate a report of on-site fixes
But none of that tells the attorney whether:
- A dated Google Knowledge Graph lookup returns a matching record, no match, or a provider failure
- Their NAP data agrees across the specific Data Axle, Foursquare, Bing Places, and Google Business Profile records that were successfully observed
- Their Wikidata entry (if any) links to their correct sameAs profiles
- The monthly sampled responses from Claude, ChatGPT, Gemini, Perplexity and Grok mention them for the tested local-attorney questions; Microsoft Copilot is not currently supported by ClickRadius
- A declared Google AI Overview observation panel, once its collector is validated, actually displays their domain for the tested queries
ClickRadius connects these evidence surfaces. It records the website, supported outside-source observations, and monthly engine-answer samples without treating one surface as proof that another provider used it or that an external result will follow.
4. ClickRadius — Platform Overview
4.1 On-Site Capabilities
4.2 Off-Site Capabilities (the differentiator)
4.3 Intelligence & Automation
4.4 Competitive Advantages
- Entity work is represented separately from scoring. ClickRadius can prepare governed entity and distribution work without treating a higher score, an adapter call, or a provider receipt as proof of an external result.
- Monthly citation observations across five supported AI engines. ClickRadius samples ChatGPT, Gemini, Perplexity, Claude and Grok because each integration can return different answers and citation evidence. Microsoft Copilot is not currently supported, and one monthly sample is not a continuous view of every user response.
- Governed correction delivery. ClickRadius prepares eligible schema, metadata and technical corrections. A supported effect is applied only with current authorization for the exact revision and a capable connection; otherwise it remains for review or customer implementation. Provider acceptance and independent public-origin observation remain separate evidence.
- Traceable strategy selection. The current engine records selected and competing samples plus an available decision-time baseline. That is decision evidence, not a claim that the platform has learned what works.
- Explicit outcome boundaries. Missing baselines, sparse coverage, and co-occurring same-site decisions are quarantined rather than presented as causal proof.
- Patent status. ClickRadius identifies its technology as Patent Pending. Filing status, implementation state, and legal scope remain separate evidence questions.
5. ClickRadius — Technical Architecture & Specifications
This section inventories current ClickRadius source paths, configured mechanisms, and explicit hold boundaries for technical evaluation. Each item states whether it is an active measurement, a model simulation, a governed candidate path, or a held capability. Implementation, integration, production release, provider execution, and public effect are separate evidence stages; source presence is not itself a statement that a capability is live for every customer.
5.1 Scoring Engine — Five-Family Weighted Analysis
The scoring engine crawls a submitted site and runs analyzers across five weighted evidence families. The composite score (0-100) is an estimated setup-evidence index; it is not a probability that an engine will cite the site, a forecast, or a customer outcome:
Schema Engine analyzer (18% weight)
application/ld+json script blocks), Microdata (itemscope/itemprop attributes), and RDFa (typeof/property attributes). Recognizes 30+ Schema.org types across three tiers:
- Business types: LocalBusiness and 20+ subtypes — LegalService, Dentist, Restaurant, Store, MedicalBusiness, AutoDealer, RoofingContractor, Electrician, LocksmithService, MovingCompany, Hotel, ChildCare, Florist, TaxiService, and more
- Organization types: Organization, Corporation, GovernmentOrganization, NGO, EducationalOrganization, MedicalOrganization, SportsOrganization, Airline
- Content types: Article, BlogPosting, WebPage, WebSite, CreativeWork, Event, VideoObject, FAQPage, HowTo, BreadcrumbList, Product, Service
Legacy GEO Content Heuristic
This internal metric counts three page-content patterns. It does not evaluate the likelihood that an AI engine will retrieve, select, cite, or recommend the page. Its configured dimensions are:
(min(quotations, 5) / 5) × 33 +
(min(statistics, 8) / 8) × 33 +
(min(citations, 5) / 5) × 34
)
<blockquote>, <cite>, and <q> HTML elements.Statistics detection (7 regex patterns): Percentages with context (e.g., "X% of/increase/decrease"), dollar amounts with context ("$X per/in/worth"), numeric counts ("X years/clients/cases"), approximations ("over/more than X"), compound counts ("X+ years"), ratings ("rated/score X.X"), and multipliers ("Xx faster/more").
Citation detection: Text patterns ("according to", "source:", "research shows", "study found") plus external links with citation-related anchor text.
Structural bonus signals: Table presence, heading depth (H2+H3 hierarchy), FAQ section detection, ordered lists, definition lists — tracked but not scored, used for content improvement recommendations.
5.2 Citation Monitor — Five Supported Engines; Monthly Sampling
Each month, the Citation Monitor sends queries through the five supported engine integrations, analyzes the returned responses for brand mentions and URL citations, computes sentiment, and records change over time. Microsoft Copilot is not currently supported and returns an explicit not-supported response.
Supported AI Engines
Citation Score Formula
mentionRate × 0.5 +
(cited > 0 ? 30 : 0) +
avg_confidence × 20
)
citationScore = min(100, baseScore × 0.7 + weightedMentionRate × 0.3)
Configured engine coefficients: Each AI engine can have a scoring profile stored in
engine_profiles. For example, current configuration can assign different coefficients to Perplexity citation observations and Gemini schema-related inputs. These are product settings, not weights fitted to customer outcomes and not proof of how an engine actually selects sources.Sentiment analysis: 11 positive keywords (recommend, excellent, top, best, leading, trusted, great, quality, reliable, expert, outstanding) and 9 negative keywords (avoid, poor, bad, worst, scam, unreliable, expensive, overpriced, disappointing) with weighted comparison.
Citation Velocity Tracking
citation_velocity table from retained citation checks; it does not collect a new engine response. Scheduled customer answer and citation collection remains monthly on the first day at 4:00 AM ET. Rate-of-change is computed via SQL LAG() across stored periods. The labels below are configured change bands, not business outcomes:Accelerating (>5% increase) | Growing (>0%) | Stable (>-5%) | Declining (>-15%) | Dropping (<-15%)
Cross-Engine Consensus Scoring
consensusScore = consistencyScore × 0.3 + crossPlatformRate × 0.4 + sentimentAgreement × 0.3
Configured Per-Engine Guidance
- ChatGPT: inspect separately verified public identity records, including Wikipedia or Wikidata where they exist; absence remains unknown and does not predict citation
- Gemini: validate structured data against the visible page; publisher markup does not establish retrieval, selection, or citation
- Perplexity: "Use FAQ schema only when visible question-and-answer content supports it; markup does not guarantee citation"
5.3 Entity Intelligence Layer
Entity Orchestrator — Governed Build Plans and Adapters
Platform adapters:
| Platform | Function | Integration |
|---|---|---|
| Data Axle | Directory candidate work | Adapter path; authorization, terms, receipt, and readback required |
| Foursquare | Location-data candidate work | Adapter path; authorization, terms, receipt, and readback required |
| Bing Places | Business-profile candidate work | Adapter path; authorization, terms, receipt, and readback required |
| Professional-profile or content candidate work | Exact account and item authority required; publisher receipt is not public proof | |
| Community-response candidate work | Per-item approval and truthful affiliation disclosure required; no source-rank claim |
Build Plan Intelligence
| Signal Category | Configured Trigger | Candidate Work |
|---|---|---|
| Brand Visibility | Score < 40 | Prepare supported directory work for review and authorization |
| Social Authority | Score < 50 | Prepare an exact account-bound LinkedIn item |
| Earned Media | Score < 30 | Prepare an evidence-bound distribution item |
| Community Presence | Score < 40 | Prepare a disclosed response for per-item human approval |
correlation_event can preserve descriptive linkage for later analysis; it does not establish that an action happened publicly or caused a citation change.
Knowledge-Record Cross-Reference
- Google Knowledge Graph Search API — entity lookup returning name, types, description, KG ID, relevance score, image, and URL
- Wikidata API — entity search + claims extraction (P856: official website, P571: inception date, P17: country)
| Signal | Points |
|---|---|
| Google Knowledge Graph presence | +25 |
| KG has description | +10 |
| KG has detailed description | +10 |
| KG has image | +5 |
| KG has URL | +5 |
| Wikidata entity exists | +15 |
| On-site Organization/LocalBusiness schema | +15 |
| sameAs links in schema | +10 |
| Logo in schema | +5 |
| Founder/foundingDate in schema | +5 |
Entity Attribute Comparison
Social platform inventory: Facebook, LinkedIn, Twitter/X, Instagram, YouTube, Yelp, BBB, Wikipedia, Wikidata, and Crunchbase URLs can be extracted from
sameAs. An HTTP reachability response does not verify ownership, identity, credentials, or current authority.Comparison checks: Name and URL equality and profile reachability can identify records for review. A match is consistency evidence only; a mismatch requires identity-aware adjudication rather than automatic replacement.
Configured review priorities: labels such as Critical, High, and Medium are workflow configuration, not measured citation harm. Missing or unavailable records remain explicitly unknown where absence has not been proven.
5.4 Outside Signals Collector — Coverage-Bound Public Evidence
Brand Visibility: 15+ mentions = 40pts, KG panel = +30pts, any signals = +10pts, 2+ signal types = +20pts (max 100)
Reviews: Each platform = 10pts (max 40), avg rating ≥4.5 = 30pts, 100+ reviews = 30pts (max 100)
Community: Reddit observations, subreddit diversity, and upvote engagement can be recorded by the configured rubric. Any source-specific score weight is product configuration, not a cross-engine source ranking.
Media: Wikipedia presence can receive configured rubric points when it is actually observed. That score is a publisher-record signal, not evidence that every engine checked the record or that it caused a citation.
Composite:
overall = brand × 0.25 + review × 0.20 + community × 0.20 + media × 0.20 + social × 0.15
5.5 AI Overview Observation Hold & Search Agent Optimization
AI Overview Observation — Collector Held
collector_unavailable before provider spend, result persistence, or timestamp mutation. Historical rows created by the retired language-model simulation path remain available only as provenance: their outcome fields and predicted-source lists are suppressed from customer observation metrics.Scheduled state: The Wednesday 3:00 AM ET slot emits a durable held receipt with zero attempted keywords, simulations, and Google observations. It does not perform a weekly check.
Contained legacy formula: The formula below documents the earlier implementation only. While the collector is held, the current summary returns
aio_visibility_score: null and no observed rate:
citationRate × 0.6 +
(aioTriggerRate > 50 ? 20 : aioTriggerRate × 0.4) +
(clientCited > 0 ? 20 : 0)
)
Search Agent Publisher-Surface Scanner
Inventories 6 publisher-controlled dimensions with 70+ individual checks. This internal checklist does not measure Information Agent availability, eligibility, retrieval, selection, citation, task completion, or referral:
5.6 Conversational Query Analyzer
model_simulation; no search engine is contacted and observed_comparisons_completed remains zero.Process:
- Takes a traditional keyword (e.g., "personal injury lawyer chicago")
- AI generates 4 conversational reformulations with intent classification (informational, transactional, comparative, local) and complexity rating (simple, moderate, complex)
- The model returns a simulated citation estimate, position, source candidates, and suggested content signals for the original and each usable variant
- The system computes a comparison only when both the original and at least one variant produced usable model output; unavailable output remains unavailable rather than zero
- Opportunity hypothesis: The model estimated more simulated coverage for conversational variants
- Risk hypothesis: The model estimated the original but not the usable conversational variants; this is not a traffic forecast
- Modeled difference: A percentage-point difference between model-returned estimates, not measured citation lift
- Review input: Model-suggested content signals, frequency-ranked across usable variants
conversational_better, keyword_better, or similar. Those labels describe model output only; they are neither Google AI Mode observations nor forecasts of citations or traffic.
5.7 Content Engine — Governed Article-Draft Pipeline
The current source creates exact-revision article candidates through bounded generation, safety, authority, research, source-evidence, corpus, and publication gates. A generated draft is not automatically a factual, citable, published, or externally observed article.
held_contract_required; it does not generate or deliver a featured image until applicability, rights, accessibility, connector, approval, receipt, readback, and withdrawal contracts clear. An approved exact article revision can publish only with current authority and a capable connection; provider receipt and public-origin observation remain separatePrompt and HTML defenses: Selected prompt-bound strings are normalized and length-limited, untrusted-data boundaries are repeated in system/user prompts, and generated HTML passes an allowlist inspection. These are defense-in-depth controls, not a guarantee that all possible input or model output is safe.
RSS feed: Public endpoint per site serving RSS 2.0 with Atom self-link, up to 50 published articles.
5.8 Strategy Engine — Bayesian Selection, Outcome Training Inactive
The Strategy Engine currently uses Thompson Sampling over stored Beta-Binomial arm state to order eligible auto-fix strategies. It records each selection and an available grounded citation baseline. The implemented selection mechanism must not be confused with a causally validated customer-learning loop; customer-outcome training is not currently active.
- Active optimization strategies are stored by category and represented as arms with configured Beta(α, β) state
- When deciding what to optimize, the engine samples from each arm's Beta distribution using Marsaglia-Tsang Gamma sampling + Box-Muller normal distribution
- The batch auto-fix path selects the top three samples, writes strategy-decision records, and captures one grounded baseline when available
- Those simultaneous same-site decisions are treated as confounded by the scheduled outcome observer and are recorded without training
- A shared causal-eligibility and exactly-once update contract, including the admin path, must close before customer-outcome training can be activated
Informative Priors by Category
| Category | Prior (α, β) | Implied Success Rate | Rationale |
|---|---|---|---|
| Schema | (3, 2) | 60% | Configured initialization; not an observed outcome rate |
| Meta | (3, 2) | 60% | Configured initialization; not an observed outcome rate |
| GEO | (2, 2) | 50% | Configured initialization; not an observed outcome rate |
| Content | (2, 2) | 50% | Configured initialization; not an observed outcome rate |
| Technical | (2.5, 1.5) | 63% | Configured initialization; not an observed outcome rate |
| Entity | (1, 1) | 50% | Configured initialization; not an observed outcome rate |
Stored-State Blending
effectiveAlpha = blendFactor × alpha + (1 - blendFactor) × parent_alpha
effectiveBeta = blendFactor × beta + (1 - blendFactor) × parent_beta
total_pulls / observations_needed. Reward propagation exists in source, but the normal batch path is currently quarantined as confounded and the admin path does not yet share the full causal and exactly-once gate. This is why the blending mechanism is documented without claiming active customer learning.
5.9 Outcome Observation — Current Evidence Boundary
The current scheduler compares grounded citation observations before and after a strategy decision. That comparison can support a descriptive history record; by itself it cannot establish that an optimization caused the change.
- Capture a trailing grounded citation rate, or
nullwhen no grounded baseline exists, at decision time - Re-observe eligible pending decisions after 14 days and before they age beyond the current 28-day query window
- Leave decisions pending when grounded after-data is unavailable
- Record but withhold from training when the baseline is missing, fewer than 10 grounded samples exist, or multiple same-site decisions are confounded
- Keep customer-outcome training inactive until treatment identity, measurement windows, causal eligibility, and exactly-once updates are enforced across every writer
grounded baseline present ∧ grounded after-data present ∧ samples ≥ 10 ∧ no co-occurring same-site decision
Launch boundary:
descriptive observation ≠ causal attribution ≠ training eligibility
5.10 Auto-Fix Engine — Prepare, Govern, Observe, Revert Where Supported
Post-delivery observation: After a connector attempt, the system waits and independently re-reads the public page. A provider receipt and the returned public-origin HTML are separate evidence. Server-rendered changes may be observed in that response; client-side JavaScript-snippet effects are absent from raw origin HTML and remain not verifiable at the public origin rather than being promoted to verified. Records
verified_at and verified_ok; neither field proves that an external engine observed the effect.Revert capability: Effects deployed through a connector that declares a bound revert path store their prior value, and revert restores the original through the same deployment channel. Where a connector declares no reversal capability for an effect, the change is recorded and recovery is operator-owned rather than one-click.
Schema lock protection: Sites with
schema_locked = true are blocked from all auto-fix operations. This prevents automated changes from overwriting carefully crafted custom schema.
5.11 Automation — Selected Cataloged Operations
The current source catalog defines 57 named scheduler jobs on cadences from minutes to monthly; the table below is a selected functional view, not the complete denominator. Registration, a lock attempt, and source implementation do not prove a job ran successfully in production. Completion requires a run-specific heartbeat or outcome receipt.
Retry and recovery: Some material workflows use durable retry or reconciliation records, including
scheduler_retry_queue where that contract applies. Other jobs record failures and alerts without sharing that queue. No blanket automatic-retry claim applies to every scheduled job.Run evidence: Activity, heartbeat, population accounting, alert, and effect receipts vary by job. Release validation compares the expected job denominator with those exact records; source registration or a dashboard timestamp alone does not prove successful work.
5.12 White-Label & Reseller Infrastructure
White-Label Branding
- Company name — resolved by supported portal and report consumers; complete coverage requires rendered-path proof
- Logo URL — available to supported portal/report consumers, subject to asset and accessibility validation
- Primary color — available to supported themed components; contrast and complete-surface coverage remain separate checks
- Portal title — configurable where the consumer implements the branding contract
Reseller Management System
pending_payment status until billing is configured.Dashboard: 5 parallel aggregation queries showing client count, site count with health distribution (healthy/warning/critical), scan history, recent activity, and top-performing sites by score.
Onboarding source path: When an authorized reseller adds a client site, the implementation can request several downstream steps. Each step needs its own durable state and completion evidence:
- Triggers a full analysis scan in the background
- Generates content prompts for the site's business type
- Picks the top 3 keywords from auto-generated citation queries
- Generates 3 draft articles (1,200+ words, authoritative tone)
Branded reports: Monthly report generation and delivery paths can consume reseller branding. Exact PDF/email identity, current evidence coverage, delivery receipt, and customer access require run-specific proof.
Billing: Subscription, payment, cancellation, and access-control workflows exist in source. Live processor settlement, agreement binding, term calculation, entitlement change, and end-of-term access require reconciled commercial and production evidence.
Conclusion
Google's May 2026 announcement warrants direct observation, not panic or an invented universal funnel. Teams can preserve ordinary SEO foundations while adding explicit answer, referral, and outcome measurements.
ClickRadius was built for this moment. The platform connects configured strategy selection, citation observation, entity workflows, a multi-step content pipeline, and governed auto-fix preparation. An exact supported site effect is applied only with current authorization for the exact revision and a connection proven capable of that effect. Each capability still has to pass its own evidence and release gate; this report does not turn implementation presence into an outcome claim.
The on-site + off-site evidence model is the differentiator. Website diagnostics answer one class of questions; independent identity, presence, and citation observations answer another. ClickRadius is designed to connect those classes without representing an external platform action or an unobserved AI response as a measured outcome.
Opportunity is specific to a business, market, prompt panel, engine, model, locale, and date. Establish that baseline before making a timing or investment claim.
The Platform
Five AI engines are sampled monthly for answer and citation observations: ChatGPT, Claude, Gemini, Perplexity and Grok. Microsoft Copilot is not currently supported. Configured strategy selection, decision-evidence recording, governed deployment paths and white-label workflows retain their separate evidence and authorization boundaries. Patent Pending.
ClickRadius is an AI-search evidence and governed-delivery platform. Each capability and public claim remains at its evidenced release stage.
Primary Authorities and Claim Status
- Google Search I/O 2026: the first-party announcement supports the redesigned Search experience and Gemini 3.5 Flash as the default model inside AI Mode. It does not establish AI Mode as the global default.
- Google Search Central: current guidance says normal SEO foundations remain relevant to AI features and that no special optimization is required merely to appear in them.
- Third-party funnel and ROI figures: the earlier page did not retain accessible source links and independently reviewable claim packets for the displayed numbers. They have been removed rather than presented as current universal facts.
- Future quantitative claims: require source, cohort, method, query class, collection date, locale, device, model or feature version, applicability, uncertainty, and expiry before publication.