Services
What RANKARA delivers
every single month.
A complete breakdown of the intelligence cycle, content pipeline, review workflow, and analytics that run for every practitioner on your roster — month after month.
For each practitioner, RANKARA builds a library of 25+ search scenarios — the specific questions their potential clients ask AI platforms. Every month, each scenario is checked across multiple platforms to see whether that practitioner is being cited, how prominently, and how positively.
Four platforms checked directly
ChatGPT via OpenAI API, Perplexity via direct API, Google AI Overview via ValueSERP, and Google Gemini via Gemini API. Each platform checked independently — not one platform estimating another’s results.
Three query variations per scenario
Each scenario is tested in three natural phrasings — the way someone might type it, the way someone might say it conversationally, and a third variation. A citation win on any variation counts. This dramatically improves detection accuracy.
Weighted platform scoring
Not all platforms are equal. Citations are weighted by platform importance — ChatGPT carries the most weight, followed by Google AI Overview, Perplexity, and Gemini. Your weighted citation score reflects real-world impact, not raw counts.
Sentiment and prominence scoring
Every citation is scored for sentiment (strong positive, positive, neutral, negative) and prominence (first recommendation, secondary mention, passing reference). A strong positive citation that leads the answer counts for more than a passing mention at the end.
Each month, RANKARA generates a package of content for each practitioner targeting their highest-priority citation gaps. Content is not templated — it is built around each person’s voice, their local market, their organization’s brand, and the current regulatory calendar.
Monthly article with embedded FAQ
A 1,100–1,300 word article targeting the highest-value uncited scenario, with a FAQ section embedded naturally at the bottom targeting additional search scenarios. Written to answer the exact questions AI platforms are asked about this type of practitioner in this city.
Google Business Profile posts
Three to five location-specific posts targeting local search intent — educational, timely, and naturally referencing the practitioner’s expertise and city. Humanized and ready for direct publishing.
Schema markup and meta updates
Person schema with sameAs links connecting the practitioner’s identity across authoritative sources. Meta title and description recommendations for key pages. FAQPage schema attached to the monthly article.
Voice profile enrichment
Each practitioner completes a one-time questionnaire about their communication style, specialty, local connection, and preferred language. Those responses inform every piece of content generated for them going forward — making output sound like them, not a generic template.
Every piece of content runs through an eight-pass humanization pipeline before it reaches your review team. Content that arrives at compliance already reads like a person wrote it — not like it came out of an AI tool.
Pass 1 — AI detection and rewrite
Scans for generic AI patterns — “in today’s complex financial landscape,” “navigate your journey,” “comprehensive solutions” — and rewrites them into direct, specific language.
Pass 2 — Specificity injection
Finds vague statements and forces local specificity. “Wisconsin residents” becomes “state employees retiring from Gundersen or Trane Technologies in La Crosse.” Specific content gets cited. Vague content does not.
Pass 3 — Sentence rhythm variation
AI writes in uniform sentence lengths. This pass deliberately breaks the pattern — mixing short punchy sentences with longer explanatory ones — the way a real person writes.
Pass 4 — Local grounding
Ensures the article has meaningful local references — specific employers, local landmarks, city-specific context — not just generic state or regional mentions.
Pass 5 — First-person voice
Adds 1–2 first-person advisor observations where they fit naturally. Skipped if the practitioner has completed their voice profile, which injects their specific voice at the generation stage instead.
Pass 6 — Jargon replacement
Financial, legal, and medical jargon is replaced with plain language on first use. “Asset allocation” becomes “how your money is divided between investments.” Makes content accessible and readable.
Pass 7 — Punctuation cleanup and grammar check
Em dashes — which AI overuses — are split into two sentences. Ellipsis removed. Grammar and spelling errors fixed. Light touch — does not change meaning or structure.
Pass 8 — Citation engineering
Identifies 2–3 passages that are close to being citable and strengthens them — making them factual, specific, locally-grounded, and structured in a way AI platforms extract and quote directly.
Nothing publishes without structured review. RANKARA routes every piece of content through your review team with automatic pre-screening, inline editing, staged approval, and a complete audit trail.
Automatic compliance pre-screen
Every piece is scanned against your industry’s regulatory red flags before your reviewers see it. High-severity issues are flagged prominently — guaranteed returns language, superlatives, specific performance claims, and other patterns that require attention.
Individual reviewer accounts
Every member of your review team gets their own login. Compliance sees the compliance queue. Marketing sees the publishing queue. Operators see everything. Role-based access, not shared passwords.
Inline editing with AI check
Reviewers can edit content directly in the queue. As they type, an AI check runs in the background and flags if an edit introduces a new compliance issue — catching problems at the edit stage, not after approval.
Full audit trail
Every approval and rejection logged with reviewer name, timestamp, and optional notes. Complete documentation for every piece of content ever reviewed. Built for regulatory environments that require it.
Learning from every edit
When reviewers edit content, RANKARA analyzes what changed and extracts avoid/prefer/require patterns. Those patterns are automatically applied to next month’s content generation — reducing review burden over time as the system learns your standards.
AI platforms do not just read content — they verify entity identity by cross-referencing authoritative sources. RANKARA builds and monitors the web of connections that establish each practitioner as a verified, credible entity.
Person schema with sameAs links
Person schema generated with sameAs links connecting each practitioner to their regulatory database profile, LinkedIn, organization profile, professional directories, and personal website. These cross-references are how AI platforms confirm entity identity before citing someone.
NAP consistency monitoring
Monthly check that each practitioner’s name, address, and contact information matches exactly across regulatory databases, Google Business Profile, organization profile, and directory listings. Inconsistencies are flagged — they reduce AI citation confidence.
Hallucination monitoring and correction
AI platforms sometimes say incorrect things about practitioners — wrong credentials, wrong location, wrong services. RANKARA monitors for these errors monthly and automatically generates targeted correction content to address them.
Competitor citation tracking
When a competitor is being cited for a scenario you are not winning, RANKARA builds a profile of what they are doing and generates counter-content strategy targeting their gaps. Knowing who is winning and why is as important as knowing your own citations.
Beyond individual practitioner dashboards, RANKARA gives leadership a consolidated view of what is working across the entire roster — and builds proprietary intelligence that makes the platform smarter every month.
Performance insights across all practitioners
Which content types are driving citation wins. Which markets are most competitive. Which use case categories produce the most citations. Month-over-month trend across the entire roster — one view for leadership.
Content performance attribution
Every published piece is tracked for citation impact — before and after publication. The system measures what changed, records which content produced wins, and feeds that data back into next month’s content strategy.
Cross-practitioner pattern library
When content works for one practitioner, the pattern is extracted and applied to others in similar markets and specializations. The more practitioners on the platform, the smarter the content strategy becomes for each one.
Market intelligence per city
Each practitioner city gets a market profile — major employers, demographic patterns, industry mix, seasonal financial planning trends. Built once, reused every month, automatically generated for new markets when new practitioners are added.
The monthly cycle
Everything runs on a single
monthly cadence.
Every practitioner gets the same complete cycle, every month, without manual intervention.
Citation checking
25+ scenarios checked across ChatGPT, Perplexity, Google AI Overview, and Gemini. Three query variations each. Results scored and weighted.
Intelligence gathering
Competitor profiles updated. Market intelligence refreshed. Regulatory calendar events loaded. Hallucination monitoring run. NAP consistency checked.
Content generation
Article, FAQ, GBP posts, schema, and meta generated. All enrichment context injected — voice profile, market intelligence, firm knowledge, compliance patterns.
Humanization and pre-screen
Eight humanization passes run. Compliance pre-screen executed. Quality notes attached. Content queued for review with all flags surfaced.
Review, publish, learn
Review team notified. Content approved and published. Citation impact measured. Patterns extracted. Next month’s strategy updated automatically.
Four views
Every role sees exactly what they need.
RANKARA includes role-specific dashboards for operators, compliance reviewers, marketing teams, and individual practitioners — all from one platform.
Operator
Full intelligence view across all practitioners. Trigger runs, view citations and scores, access competitor data, manage users, and onboard new practitioners. Complete platform control.
Compliance
Structured review queue organized by run. See pending items, pre-screen flags, edit inline, approve or reject with reason. Full audit trail of every decision made.
Marketing
Advisor performance table, publishing queue of approved content, copy-to-publish workflow, and firm-level performance insights showing what content types are winning across the roster.
Practitioner
Plain-language view showing where they appear across AI platforms, which searches they are winning, which platforms still to win, and their weighted AI visibility score over time.
See RANKARA running
for your organization.
Request a demo and we will walk through the full platform — from citation checking to content review to firm-level analytics.