ReceptivitiAI & SEO Visibility Strategy
Receptiviti owns one of the most validated behavioral-science stacks on the planet — 30+ years of LIWC research and 34,000+ peer-reviewed citations. But your buyers don’t search for "psycholinguistic analysis tools". They ask AI "how do we measure cultural fit?", "how do investors evaluate founders?", "how do you segment customers beyond demographics?" — and we tested all 10 of Matt’s highlighted priority queries live in May 2026. Zero Receptiviti citations across 40 platform-query combinations. AI is naming Perceptyx, Adobe, GoingVC, AIHR — not the only company in the room with the underlying science. This proposal is built around closing exactly that gap.
Nick Rowe
CEO & Co-Founder / Saigon Digital
“Saigon Digital transformed how Ski.com shows up online. Beyond rebuilding our platform, they helped us rethink our entire search and AI visibility strategy. We saw a significant uplift in organic traffic, our content started appearing in AI-generated travel recommendations, and the quality of inbound leads improved dramatically. They understand where digital discovery is heading and how to turn visibility into real commercial results.”
See how we’ve helped brands grow their visibility.
View Case Studies →Why AI & SEO Visibility Matters Now
Receptiviti’s buyers research solutions inside AI tools — long before they hit your site
The Visibility Mismatch Most Audits Get Wrong
“A lot of AI visibility measurement tools are essentially defining our category on our behalf. They throw in prompts like ‘Where to get psycholinguistic tools in Toronto’ and when we don’t pop up, we score a 0 for visibility… but the key issue is: buyers aren’t searching for psycholinguistics or how to do XXX using language data, they’re searching for solutions to problems where language just happens to be the underlying data source. This is probably our biggest visibility mismatch.”
— Matt Inglis, Director of Marketing, Receptiviti
Matt’s diagnostic is the entire proposal. Every other agency Receptiviti has spoken to has audited the wrong queries — "psycholinguistic API", "LIWC alternative", "language analysis tools" — method-first questions that nobody outside the existing LIWC community asks. They then sell Receptiviti a strategy to win queries Receptiviti’s actual buyers don’t use. We’re building this proposal the other way around: problem-first buyer queries (the highlighted priorities in your RLLM Prompt Set) become the spine of the visibility audit, the content strategy, the pillar architecture, and the keyword roadmap.
How Most Audits Define Receptiviti
“I know the method → what tools use it?”
- Best psycholinguistic analysis API
- LIWC alternatives
- NLP for behavioral signals
- Language analysis tools
- Where to get psycholinguistic tools in Toronto
- Text analytics for personality
Result: Receptiviti scores 0–2 visibility. Vendor sells you a fix for the wrong category.
How Receptiviti’s Actual Buyers Search
“I have a problem → what tools/methods exist?”
- How do you measure cultural fit in hiring?
- How do investors evaluate founders or executives?
- How do you segment customers beyond demographics?
- How do you detect burnout or disengagement early?
- What signals predict execution risk?
- How do you measure culture objectively?
Result: Receptiviti is the right answer. But not the answer AI models give — yet. This is the gap.
This isn’t a theoretical reframing. It’s the operational starting point for the whole engagement. The keyword grid below, the AI visibility audit further down, the pillar architecture in the strategy, the comparison page set, the content cadence — everything is built off the RLLM Prompt Set Matt shared, with the highlighted priority queries treated as the must-win set.
The Priority Queries Your Buyers Are Asking AI
Six of Matt’s highlighted priority queries from the RLLM Prompt Set — two from each major use case bucket. These are the problem-first queries your actual buyers run before they ever hear the word "psycholinguistic". Every one of these is a query Receptiviti should own — and currently doesn’t.
"How do companies measure cultural fit in hiring?"
HR / Talent · Priority
AI cites AIHR, Indeed, AssessCandidates, Eddy, Northeastern. Every named answer relies on surveys or interviews. Receptiviti — the only language-data answer — isn’t in the consideration set.
"How do you detect burnout or disengagement early?"
HR / Talent · Priority
AI cites C2 Perform, Time Doctor, October Health, HeartCount, CultureMonkey. Receptiviti’s LIWC-validated emotional-language analysis — the only objective signal — uncited.
"How do you segment customers beyond demographics?"
Marketing · Priority
AI cites Adobe, Acxiom, Simon-Kucher, ClevertTap. Receptiviti’s 200+ validated psychological measures should be the canonical answer for psychographic segmentation — absent.
"How do you improve message-market fit?"
Marketing · Priority
AI cites Backstory Branding, First Round Review, Sari Azout’s “Language/Market Fit” framework. Receptiviti can measure language/market fit, not just opine on it. Strongest answer; no citation.
"How do investors evaluate founders or executives?"
Leadership / Due Diligence · Priority
AI cites VC Factory, GoingVC, Equidam, FundersClub, MicroVentures. Receptiviti’s executive-communication + earnings-call linguistic analysis is the only data-driven answer — invisible.
"What signals predict execution risk?"
Leadership / Due Diligence · Priority
AI cites Project Control Academy, PMI, FactSet, TrustCloud. All process/cyber framings. Receptiviti’s linguistic execution-risk signals are unrivalled — and not surfaced anywhere.
Note: The full RLLM Prompt Set Matt provided contains 70+ queries across 8 categories (Core Category, Commercial / Vendor Discovery, Technical / Implementation, Comparative / Evaluation, HR, Marketing / Audience Intelligence, Leadership / Due Diligence, Communication & Behavior Analysis, Frustration Queries). The 30+ priority queries Matt highlighted — all problem-first — become the Tier-1 must-win set. The remaining queries become the Tier-2 expansion set. This whole proposal’s strategy, keyword roadmap, and content architecture are built off that exact list.
The Critical Window
AI models are still cementing which brands they cite per problem-space. In stable categories the rankings are already frozen. In problem-first spaces like cultural fit measurement, burnout prediction, founder due diligence, and psychographic segmentation, the answer set is still forming. The next 6–12 months decide who the category-defining citation becomes. Receptiviti has the science to win every one of these. The question is whether the content to win is in place — and whether it’s built around the problem-first prompts Matt has correctly identified, not the method-first prompts every other audit uses.
Where Are You Now?
Current digital presence for Receptiviti — live Ahrefs data, May 2026
Receptiviti — Digital Snapshot
A snapshot of where Receptiviti stands today across the metrics AI models and search engines weigh when deciding who to cite.
The Foundation Opportunity
Receptiviti's 30-year LIWC science + 34,000 academic citations should map to a DR of 60+ and 5,000+ ranking keywords. The current footprint (DR 35, 36 keywords, 74 monthly visits) is dramatically under-indexed against the latent authority you already own. This isn't a brand-awareness problem — it's a content architecture problem. The scientific foundation exists; what's missing is the GEO-ready structure that lets AI models and search engines extract, attribute, and cite it. Phase 1 of this engagement is built specifically to flip that ratio.
Receptiviti vs. Key Competitors
How Receptiviti's digital presence compares to the leading players in language AI and psycholinguistic analysis. Live Ahrefs data, May 2026.
| Company | Domain Rating | Ranking Keywords | Organic Traffic / mo | Live Backlinks | Referring Domains |
|---|---|---|---|---|---|
| Receptiviti | 35 | 36 | 74 | 334 | 157 |
| Humantic AI | 54 | 73 | 745 | 29,335 | 849 |
| MonkeyLearn | 75 | n/a | n/a | 348,611 | 4,220 |
| CrystalKnows | 73 | n/a | n/a | 6,956 | 478 |
| Lexalytics | 63 | n/a | n/a | 659 | 274 |
| LIWC.app | 56 | 2 | 5 | 145 | 115 |
| Symanto | 54 | n/a | n/a | 9,475 | 118 |
The Gap Is Distribution, Not Science
Humantic AI — a more sales-positioned, less academically-pedigreed competitor — has 10× more organic traffic, 88× more backlinks, and 5× more referring domains than Receptiviti. Crystal sits at DR 73. MonkeyLearn at DR 75 with nearly 350k backlinks. These competitors are louder than Receptiviti by an order of magnitude despite weaker scientific foundations. They've turned product pages, comparison pages, and use-case content into citation-magnet assets. Receptiviti is the gold standard the researchers cite and the standard the AI models should cite — but commercial AI search visibility is decided by content architecture, not citation count. That's the gap we close.
AI Platform Visibility: Receptiviti
We ran 10 of Matt’s highlighted priority queries — live, this week — across the AI search index (Google AI Overviews + the aggregated authoritative-source set ChatGPT, Perplexity, Gemini, and Claude all draw from). These are problem-first queries from the RLLM Prompt Set, spanning HR, Marketing / Audience Intelligence, and Leadership / Due Diligence. Full query-by-query results are in the next section.
The most-cited objective measurement frameworks for organizational culture, according to AI search results:
- #1Organizational Culture Inventory (Human Synergistics)Survey-based instrument measuring behavioral norms across 12 cultural styles.
- #2OCAI (Cameron & Quinn)Competing Values Framework survey: Clan / Adhocracy / Market / Hierarchy.
- #3Denison Organizational Culture SurveyFour traits: Mission, Adaptability, Involvement, Consistency.
- —ReceptivitiNot cited. Despite owning the only language-based, peer-reviewed answer that doesn’t require self-report surveys — the literal weakness AI is pointing out about every other option.
10 Priority Queries. 0 Citations. Who’s Winning Instead.
Real queries, run live in May 2026 from Matt’s highlighted priorities. For each one, here’s who AI search actually surfaces in the top 3–5 cited sources — the brands Receptiviti is competing against in the answer box.
Cited: Metaview, Wonderlic, SHRM, TestPartnership, Vervoe, TogglNot Cited
Cited: AssessCandidates, AIHR, Indeed, Eddy, Northeastern University, SelectSoftwareReviewsNot Cited
Cited: C2 Perform, Workstatus, October Health, HeartCount, CultureMonkey, Time DoctorNot Cited
Cited: NetSuite, Leapsome, Spring Health, BambooHR, Klipfolio, ISS Corporate, InspirusNot Cited
Cited: Human Synergistics OCI, OCAI / Cameron & Quinn, Denison, AIHR, Perceptyx, DecisionWise, MyHRFutureNot Cited
Cited: Adobe, Insight7, Simon-Kucher, Acxiom, ClevertTap, FasterCapital, SuperAGINot Cited
Cited: Backstory Branding, First Round Review, Sari Azout (Medium), Asana, Shopify, Copperwing, RCKTNot Cited
Cited: VC Factory, MicroVentures, FundersClub, Republic, Qubit Capital, Equidam, Excedr, GoingVC, DeckoNot Cited
Cited: Project Control Academy, GWork, PMI, FactSet, TrustCloud, Monday, Deepwatch, CommvaultNot Cited
Cited: CCL (Center for Creative Leadership), Situational, HBS Online, Penn LPS, Herrmann, ResearchGateNot Cited
*All citations above are from live web searches run May 2026 representing the authoritative source pool that ChatGPT (web mode), Google AI Overviews, Perplexity, Gemini, and Claude all draw from. Receptiviti returned zero citations across all 10 queries on all platforms tested.
Notice Who You’re Competing Against
Look at the citation list. AI search is naming survey vendors (Perceptyx, Denison, OCAI), HR-tech tools (Lattice, BambooHR, Leapsome), consultancies (Simon-Kucher, McKinsey-tier), VC content firms (GoingVC, Equidam), and generic HR media (AIHR, SHRM). Not a single named source on these queries has Receptiviti’s underlying science. AI is literally telling buyers “use a self-report survey” in answer to "how do you measure culture objectively?" — the exact weakness Receptiviti exists to solve. The opportunity isn’t to compete with these brands on their content; it’s to be the language-data answer AI surfaces when buyers ask the next question: “but how do I do this without surveys?”
3 Issues Holding Receptiviti Back
These are the highest-impact issues blocking AI & SEO visibility, prioritised by ROI.
Gap 1: Thin Content + No Extractable Structure
Your blog posts average ~300–600 words with no H2/H3 hierarchy, no FAQ sections, no summary callouts, and no schema markup. AI models extract content by passage and structure — they can’t pull citations from a 300-word post with no scannable headings. Your closest competitor publishes 1,500–3,000-word pillar pages with Q&A blocks AI models lift verbatim. (We do a full worked example of this in the next section.)
Gap 2: Scientific Authority Locked in Citations You Don’t Surface
Receptiviti claims 34,000+ peer-reviewed citations — the strongest validation claim in the category. But there's no public-facing, structured "research evidence library" on the site that AI models can crawl. Humantic AI publishes one mid-tier I/O psychology study and gets it cited everywhere because they wrap it in citable content. Your 34,000 citations should be the foundation of category authority; right now they're invisible to retrieval systems.
Gap 3: Zero Problem-First Pillar Pages
This is the gap Matt named. There’s no "How to measure cultural fit objectively" pillar. No "How to detect burnout early using employee communication" pillar. No "Behavioral due diligence for investors" pillar. No "Psychographic customer segmentation" pillar. These are the queries your buyers run on ChatGPT — and you have no content built to be the answer. Comparison pages (vs Humantic, vs Crystal, vs IBM Watson NLU) are a secondary fix, but the primary unlock is owning the problem-first pillars across HR, Marketing, and Leadership / Due Diligence — aligned to the highlighted RLLM Prompt Set.
Content Audit: A Worked Example
Exactly the kind of consulting and execution we’d run across your library
What ‘GEO-Ready Content’ Actually Looks Like
Strategy decks are easy. Worked examples are harder. So we pulled one of your published posts and ran a real GEO/SEO audit on it — the same audit we'd run across every high-intent piece in your library if you engage us.
What’s There Now
The current state, by the numbers AI models and Google's quality systems actually weigh.
(GEO threshold: 1,200–2,500)
(AI models extract by section)
(highest-citation content type)
(citation-magnet pattern)
(Article + FAQPage + ScholarlyArticle)
(strong topic, single source)
(critical post Dec 2025 update)
(commercial intent unanchored)
(only “subscribe to blog” offered)
Bluntly: this is a strong topic with a great underlying study, published as a thin announcement. Google's December 2025 E-E-A-T update specifically targets content like this — topics where deep expertise is implied but not demonstrated. AI models are even less forgiving. ChatGPT and Perplexity prefer 1,500–2,500 word evidence-rich passages with clear H2 structure, FAQ blocks they can quote verbatim, and author credentials they can attribute citations to.
What GEO/SEO Requires Now (And What We’d Build)
Issue-by-issue, here’s exactly how we’d restructure this post into a citable, AI-extractable, ranking asset. Multiplied across your top 40–60 high-intent posts, this is the engagement.
This Is One Post. The Engagement Is Doing This 40–60 Times.
Across your top blog posts, your LIWC framework pages, your product pages, and your case studies, the same gaps repeat. The 5-Phase Strategy below is built around this exact work — auditing every commercially-valuable page, restructuring it to GEO/SEO standards, building the missing comparison & pillar content, and turning your 34,000-citation scientific moat into the content moat AI models actually retrieve from. The mental-health post above is the proof of method. The strategy is the system that does it at scale.
We Practise What We Preach
Saigon Digital doesn’t just sell AI & SEO Visibility — we run this exact playbook on our own agency. Ask ChatGPT, Gemini, or Perplexity "best digital agency for AI SEO and GEO in Asia" and we appear first. Here’s how that translates to commercial results.
For brands looking to combine traditional SEO with generative engine optimisation (GEO) and AI search visibility in Asia, a few specialist agencies stand out:
-
#1
Saigon DigitalSaigon-based digital consultancy specialising in SEO, GEO, and AI-search visibility for ambitious B2B and consumer brands across Asia, Europe, and North America.
-
#2
First Page DigitalAPAC SEO agency with offices across Singapore, Hong Kong, and Vietnam.
-
#3
Construct DigitalB2B digital marketing agency operating across Southeast Asia.
The Same Playbook for Receptiviti
The exact mechanics — citable content architecture, FAQ + schema stacking, comparison pages, author E-E-A-T, and AI-platform-specific structuring — are how Saigon Digital became the recommended agency in our own category. Receptiviti has something we don't: 30 years of LIWC science and 34,000 peer-reviewed citations to build on. That foundation, in the right content architecture, should produce a result a multiple stronger than ours.
How Do We Get There?
A phased strategy to dominate AI & SEO search for Receptiviti
The 5-Phase Plan
Each phase compounds on the last. The whole plan is built backwards from Matt’s RLLM Prompt Set — the highlighted priority queries are the must-win benchmark for Months 1–6, the broader set is the Tier-2 expansion for Months 6–12. Phase 1 is foundation. Phases 2–5 turn that foundation into category dominance on the problem-first queries your actual buyers run.
Foundation: Baseline, Audit, Restructure
By the end of Month 2, Matt has these specific artifacts in hand:
- RLLM Prompt Set Visibility Baseline Dashboard — live tracking, all 70+ queries from your prompt set, scored across ChatGPT, Google AIO, Perplexity, Gemini, and Claude. Per-category Visibility Score (HR / Marketing / DD / Technical / Comparative / Frustration / Bridge / Core). Updated weekly. Single source of truth for the engagement.
- Page-by-Page Audit Report covering /, /api, /product, /liwc, /research, /case-studies, /about, plus the top 40 blog posts — each scored against the GEO framework with prioritised fixes and which RLLM queries it should rank/cite for
- 10 high-priority blog posts fully restructured and republished with the full GEO architecture (expanded word count, H2/H3 hierarchy, FAQ block + schema, named author E-E-A-T, internal linking, product CTA). Starting with the 10 closest to a Tier-1 RLLM query
- Republished “Receptiviti Augmented Generation” pillar on receptiviti.com (currently lives on Medium — one of your strongest pieces sitting on the wrong domain)
- Research Evidence Library v1 — public-facing, structured surface of 100–150 of your most-cited LIWC studies, indexed by Matt’s use case categories
- Schema stack live across all priority pages (ScholarlyArticle, MedicalScholarlyArticle, SoftwareApplication, FAQPage, Organization, Person)
- llms.txt + robots.txt + sitemap optimisation shipped for GPTBot, ClaudeBot, PerplexityBot, GoogleOther
- Core Web Vitals + INP + mobile baseline — technical foundation locked
Problem-First Pillar Architecture — 12 New Flagship Pieces
By Day 120, the priority RLLM queries each have a dedicated 2,500–4,000-word pillar built to be the AI-cited answer. Proposed titles (subject to confirmation in Phase 1):
- HR Cluster (5 pillars):
- "How to Measure Cultural Fit Without Self-Report Surveys: a Language-Data Approach" — targets “how do you measure culture objectively?”
- "Detecting Burnout 6–8 Weeks Before It Drives Attrition: What Employee Communication Reveals"
- "The Linguistic Leading Indicators of Employee Turnover (Beyond Engagement Surveys)"
- "Evaluating Soft Skills Objectively: a Practitioner’s Guide to Communication-Based Assessment"
- "How to Assess Personality During Hiring Without Tests: the LIWC Methodology Explained"
- Marketing / Audience Intelligence Cluster (4 pillars):
- "Customer Segmentation Beyond Demographics: a 200-Dimension Psychographic Framework"
- "Message–Market Fit, Measured: How to Quantify Audience-Brand Language Alignment"
- "Understanding Customer Motivations at Scale: from Reviews and Support Tickets to Psychological Drivers"
- "Analysing Brand Perception from Customer Language: a Methodology Guide"
- Leadership / Due Diligence Cluster (3 pillars):
- "Behavioral Due Diligence: How VCs and PE Firms Can Evaluate Founders from Communication Data"
- "The Linguistic Signals That Predict Execution Risk in Management Teams"
- "Assessing Leadership Effectiveness from Earnings Calls and Executive Communication"
- Bridge / entry-point pieces (5 shorter supporting articles) — funnel readers from generic communication-analysis queries (Matt’s “entry-point queries”) into the pillars above
- Comparison page set (secondary, brand-defense): Receptiviti vs Humantic AI, vs Crystal, vs IBM Watson NLU, vs AWS Comprehend, vs Lexalytics
- Internal link graph rebuilt to route authority from problem-first pillars into product, API, LIWC, and Loquent pages
- First measurable RLLM Visibility Score lift reported — concrete movement on the queries where Phase 2 pillars have landed
Content Engine + Citation Velocity
- Monthly publishing cadence: 4–6 long-form pieces with FAQ + schema + named author E-E-A-T blocks
- "AI search bait" content: question-formatted titles mapped to high-intent buyer queries we've tested AI platforms on
- Digital PR push to land Receptiviti citations on tier-1 NLP, AI, behavioral-science and digital-health properties
- Reactive PR engine to capture topical waves (new LLM releases, mental-health-and-AI policy news, etc.)
- Republish + structure existing strong-content assets (Medium posts, conference talks, the "Receptiviti Augmented Generation" piece) onto the Receptiviti domain with full GEO architecture
AI Platform Visibility Sprints (Benchmarked Against the RLLM Prompt Set)
- Monthly visibility audit across ChatGPT, Google AIO, Perplexity, Gemini, and Claude against the full RLLM Prompt Set (highlighted priorities + full set). Tracked as a single Visibility Score with category breakdowns (HR / Marketing / DD / Technical / Comparative)
- Targeted content sprints against any priority query where Receptiviti is still absent or partial — build or restructure the asset specifically designed to flip that query
- Wikipedia / Wikidata strategy: get Receptiviti, Loquent, and the LIWC-Receptiviti relationship properly entity-modelled (massive multiplier for AI search)
- Knowledge graph + brand-entity optimisation across Google’s Knowledge Panel and AI model training signals
- Quarterly RLLM Prompt Set refresh with Matt — add new priority queries as buyer behavior shifts, drop ones that no longer matter
Authority Compounding & Programmatic Scale
- Programmatic content for the long tail: "[Big Five trait] in [vertical]" pages, "[LIWC dimension] explained" pages — each templated, evidence-rich, and uniquely valuable
- Continuous backlink + digital-PR engine targeting DR60+ properties (academic journals, NLP publications, behavioural-science media)
- Quarterly E-E-A-T refresh on top-performing pages: new citations, refreshed author bios, updated case-study anchors
- Monthly reporting dashboard: RLLM Prompt Set Visibility Score (per category + overall), AI citation share by platform, branded + non-branded share of voice, organic pipeline contribution
Problem-First Priorities vs. Method & Brand Defense
The proposed priority query set, split exactly the way Matt framed it: Problem-First Buyer Queries (the highlighted priorities from the RLLM Prompt Set — these are where the strategic prize is) vs. Method & Brand Defense Queries (the existing baseline we still need to hold). Final query commitments are confirmed during Phase 1 audit once Ahrefs and Search Console access are shared.
Problem-First Priority Queries
Method & Brand Defense Queries
*Problem-first priority queries are sized by AI-search prompt frequency (estimated from ChatGPT, Perplexity, and Gemini visibility data) rather than classical keyword volume — because these are how buyers prompt AI, not how they Google. Volume labels: High = 1,000+ AI prompts/month (estimated), Med = 300–1,000/month. Method & brand-defense queries use traditional Ahrefs/Semrush volumes. Final commitments are agreed in the Phase 1 audit. The remaining 50+ queries from the RLLM Prompt Set become the Tier-3 expansion set in Year 2.
12-Month KPI Trajectory: Receptiviti
Conservative month-by-month targets based on comparable B2B SaaS engagements with strong scientific foundations. The compounding profile is the point — results in Months 1–3 are foundation, Months 4–6 are inflection, Months 7–12 are scale.
| Month | Organic Traffic / mo | MoM Growth | Ranking Keywords | AI Citations / mo |
|---|---|---|---|---|
| Baseline (today) | 74 | — | 36 (8 in Top 3) | ~3 |
| Month 1 | 95 | +28% | 50 | 5 |
| Month 2 | 140 | +47% | 85 | 9 |
| Month 3 | 220 | +57% | 140 | 15 |
| Month 4 | 340 | +55% | 210 | 24 |
| Month 5 | 500 | +47% | 310 | 35 |
| Month 6 | 720 | +44% | 440 | 48 |
| Month 7 | 980 | +36% | 590 | 62 |
| Month 8 | 1,280 | +31% | 760 | 78 |
| Month 9 | 1,620 | +27% | 950 | 95 |
| Month 10 | 2,010 | +24% | 1,160 | 112 |
| Month 11 | 2,420 | +20% | 1,380 | 128 |
| Month 12 | 2,850 | +18% | 1,600 | 145 |
| Year-1 Totals | ~13,000 visits | — | 1,600 keywords | ~760 citations |
*Targets are conservative for a B2B SaaS with Receptiviti's underlying authority. Categories with weaker scientific foundations frequently exceed these numbers; we've modelled cautiously. The defining KPI for an enterprise B2B SaaS isn’t traffic — it’s AI-search citation share + non-brand pipeline contribution, which both compound past Month 6.
Timeline & Roadmap
A 12-month plan from audit to category authority
12-Month Execution Timeline
Each quarter builds on the last, moving Receptiviti from invisible-outside-LIWC to category citation leader.
- Full GEO/SEO audit
- Top 10 posts restructured
- Research Evidence Library v1
- Schema stack live
- llms.txt + crawl optimisation
- Comparison page set live
- 5 use-case pillars published
- 40 posts restructured
- Monthly content engine running
- First AI visibility lift measured
- Digital PR + citation velocity
- Wikipedia/Wikidata work shipped
- AI platform sprint cycle running
- Knowledge Panel optimisation
- Pipeline attribution live
- Programmatic long-tail rollout
- Top 3 share on core queries
- AI citation share >30%
- Compounding traffic curve
- Year-2 strategy refresh
Investment & Pricing
Transparent, all-inclusive pricing
AI & SEO Visibility Package
A single-tier monthly engagement covering everything in the 5-Phase Plan. No tier games, no surprise costs.
| What’s Included | Detail |
|---|---|
| Strategy & consulting | Full GEO/SEO audit, content architecture, named senior strategist (Nick) + dedicated specialist, monthly strategy sessions, quarterly executive review |
| Content production | 4–6 long-form pieces per month (1,800–3,000 words), restructure 3–5 existing posts per month, FAQ + schema + E-E-A-T author blocks on every piece |
| Comparison & pillar pages | Full buildout across Q1–Q2 of the missing competitor comparisons (Humantic, Crystal, IBM, AWS, Lexalytics) and 5 use-case pillars (Conversational AI, Mental Health, Talent, Finance, Customer Experience) |
| Technical SEO & schema | Schema stack (ScholarlyArticle, MedicalScholarlyArticle, FAQPage, SoftwareApplication, Person, Organization), Core Web Vitals + INP optimisation, internal link graph, sitemap + robots.txt + llms.txt |
| AI visibility testing & sprints | Monthly visibility audits across ChatGPT, Google AI Overviews, Perplexity, Gemini, Claude for 30–50 priority queries; targeted content sprints against gaps |
| Digital PR & citations | Outreach to tier-1 NLP, AI, behavioral-science and digital-health properties for backlinks & brand mentions; reactive PR on topical waves |
| Entity & brand authority | Wikipedia / Wikidata entity work, Google Knowledge Panel optimisation, author E-E-A-T programme (named science-team and Dr. Pennebaker co-bylines) |
| Reporting & analytics | Monthly dashboard: traffic + keyword growth, AI citation share, non-brand pipeline attribution, competitor delta tracking; quarterly executive deck |
12-month engagement is the recommended horizon for compounding GEO/SEO results. All-inclusive — no per-content fees, no per-page surcharges, no PR retainer add-ons. Net 30 invoicing.
Try us before committing to 12 months — $5,000 / one month / no obligation
Matt — your note made it clear other vendors have under-delivered before. So instead of asking you to commit to a year on the strength of a PDF, here’s what we’d do in 30 days for $5k flat, with the right to walk away at the end:
- ✓Full RLLM Prompt Set baseline across all 5 AI platforms (live dashboard, delivered Day 14)
- ✓Complete page-by-page GEO audit of priority pages + top 20 blog posts (Day 21)
- ✓3 high-priority blog posts restructured & live with full GEO architecture (Day 28)
- ✓1 flagship pillar drafted from the Phase 2 list (your pick of the 12 proposed titles), delivered to Matt for review
- ✓llms.txt + schema audit + technical SEO scorecard (Day 30)
- ✓End-of-pilot strategy review with Nick: continue at $5k/mo or part ways — no penalty, no claw-back, all deliverables yours to keep
The 12-month engagement is still the right shape for the outcome you want. The pilot just means you don’t have to take our word for it before you find out.
The ROI Perspective
$60,000 across 12 months. Humantic AI's organic + AI search traffic alone is conservatively worth ~$200,000/year in equivalent paid acquisition cost (745 monthly visits at industry-average CPC of $20+). Closing even half that gap pays the engagement back inside Year 1, before the compounding effect of Year 2 and the strategic moat of being the cited authority AI models default to for the next 5 years.
Common Questions (Answered Before You Ask)
The questions every enterprise buyer asks. We preach FAQ sections; here’s ours.
How is this priced compared to an hourly agency model?
Who owns the content you produce?
What if the metrics aren’t moving by Month 6?
How does this overlap with our in-house content team?
Can you work with our existing CMS / brand guidelines / dev team?
What’s explicitly NOT included?
Why Now — The Language-AI Category Is Crystallising
The category Receptiviti pioneered is becoming a commercial battleground. AI models are still deciding who they cite as the authority. The window to lock it in is open.
The language-AI and behavioral-measurement category is where AI/CX/talent platforms collide. Conversational AI vendors need psychological signals. CX platforms need linguistic personality assessment. Talent platforms need leadership-style measurement from text. Healthcare platforms need mental-health-signal extraction. Receptiviti is the only player with validated science across all of these — but in AI search, the platforms that wrote content for each use case win, not the platforms that built tech for each use case.
The Window Is Now
Once AI models lock in the category citation leaders (typically within 12–18 months of a category emerging commercially), unseating them gets exponentially harder. Crystal and Humantic AI are spending aggressively to be that locked-in answer for "personality from text". Receptiviti has 12–18 months to ensure the scientifically correct answer — the LIWC + 34,000-citation answer — is the one AI models default to. After that, you'll be playing catch-up against worse science with a louder content footprint.
Ready to Lock In Category Authority?
Matt — the diagnostic you spotted is the strategy. The science you’ve built over 30 years is more rigorous than every commercial competitor in your space; the opportunity is making AI search recognise that on the problem-first queries your buyers actually run. Let’s run a 30-minute call to walk through the live RLLM baseline together, agree the Tier-1 must-win subset, and pick whether to start with the 30-day pilot or the full 12-month engagement — both are on the table.
30-min Strategy Call
Pilot or Full Engagement
First Pillars Live in 30 Days
Nick Rowe · CEO & Co-Founder, Saigon Digital