Your people,
in the AI answer.
When clients ask ChatGPT, Perplexity, or Google AI to find a financial advisor, consultant, physician, or attorney — the right answer is one of yours. RANKARA gives enterprise organizations a managed platform to make that happen, at scale, with compliance built in.
The problem
competitors by name.
When someone asks an AI platform to find a specialist in their area — a financial advisor, physician, attorney, or consultant — they get a list of specific people. Your practitioners need to be on that list.
You cannot manage this manually at scale
Tracking AI citations for dozens or hundreds of practitioners across four platforms, generating monthly content for each one, and routing it through firm review is operationally impossible without a purpose-built system.
Generic firm content does not get practitioners cited
AI platforms cite specific, locally-grounded content about individual people — their credentials, their specialty, their city, their clients. Firm-level marketing copy does not produce practitioner-level citations.
Regulated industries need structured review
Content that bypasses compliance review creates regulatory and reputational risk. Content that gets blocked entirely creates nothing. RANKARA is built around structured review as a first principle, not an afterthought.
How it works
Per practitioner. Every month.
RANKARA runs a complete intelligence and content cycle for every practitioner on your roster — checking citations, generating content, routing it through your review team, and learning from what works.
Citation intelligence across 4 platforms
RANKARA checks ChatGPT, Perplexity, Google AI Overview, and Gemini for 25+ search scenarios per practitioner — each tested in three natural query variations. Results weighted by platform importance so you know where to focus.
Content generated in each practitioner’s voice
Monthly article, FAQ entries, and location-specific posts generated to target the highest-priority citation gaps. Enriched with local market intelligence, organization knowledge, and each practitioner’s own voice profile.
Eight humanization passes before review
AI detection, specificity injection, local grounding, rhythm variation, jargon replacement, grammar check, punctuation cleanup, and citation engineering. Content arrives at review sounding like a person wrote it.
Structured review, approval, and publishing
Review team gets a notification and works through a structured queue — approving, editing inline, or rejecting with reason. Every action logged by name and timestamp. Marketing publishes from the approved queue.
The platform learns from what works
When published content produces citation wins, the pattern is extracted and applied to future content — for that practitioner and across your entire roster. The platform builds proprietary intelligence about what drives citations in your industry.
Platform capabilities
AI citations at scale.
From citation checking to content generation to compliance workflow to cross-organization learning — RANKARA is a complete intelligence platform, not a collection of tools.
Multi-Platform Citation Checking
Direct API checks on ChatGPT, Perplexity, Google AI Overview, and Gemini. Each scenario tested in three query variations. Weighted by platform importance — a ChatGPT citation counts for more than a smaller platform.
Voice-Profiled Content Generation
Monthly content generated in each practitioner’s own voice — using their questionnaire responses, local market data, organization knowledge, and industry regulatory calendar. Eight humanization passes before review.
Structured Compliance Workflow
Review queue with inline editing, staged approval pipeline, rejection with reason, and full audit trail. Regulatory pre-screen flags issues automatically before reviewers see them. Every approval logged by reviewer name.
Entity Authority Building
Person schema with sameAs links connecting each practitioner’s identity across authoritative sources — regulatory databases, professional directories, organization profile, and personal website. NAP consistency monitored monthly.
Competitor and Market Monitoring
Monthly competitor citation tracking, hallucination monitoring, and market intelligence per practitioner city. Automatic correction content generated when AI platforms surface inaccurate information.
Cross-Practitioner Learning
When content produces citation wins, the pattern is stored and applied across your roster. Compliance edits are analyzed and learned from. The platform builds proprietary intelligence about what works in your industry and markets.
Industries served
individual expertise gets clients.
RANKARA is tailored to each deployment — the organization’s knowledge base, review requirements, regulatory context, and practitioner specializations are built in before the first run.
Financial Services
Wealth management firms, broker-dealers, RIAs, and insurance networks — where individual advisors need to appear when clients search for specific financial expertise in their city.
Healthcare
Health systems, specialty practices, and provider groups — where patients ask AI platforms to find a cardiologist, orthopedic surgeon, or primary care physician in their area.
Legal
Law firms and legal networks where clients search for estate attorneys, corporate counsel, employment lawyers, or immigration specialists by name, specialty, and location.
Higher Education
Universities and colleges where prospective students ask AI platforms about faculty expertise, research specializations, and academic programs — driving enrollment and grant opportunities.
Professional Services
Accounting firms, management consultancies, technology advisory groups, and engineering firms — where individual expertise and specialization drive client acquisition.
Government & Public Sector
Public agencies, nonprofits, and mission-driven organizations where individual program leaders and specialists need recognized authority in their field to attract partners and funding.
Compliance-first
built for regulated industries.
AI-generated content in regulated industries is not optional — it requires structured review, documented approval, and an audit trail. RANKARA is built around that reality from the ground up.
Regulatory pre-screen on every piece
Content is automatically scanned for compliance red flags before it reaches reviewers. Issues are surfaced with specific language flagged — not just a pass/fail score.
Organization knowledge baked into generation
Your approved brand language, disclaimer requirements, and compliance rules are loaded into the content generation prompt — content starts closer to compliant before any human review.
Review edits improve future content
When reviewers edit content, RANKARA analyzes what changed and extracts patterns. Next month’s generation automatically applies those learnings — reducing review burden over time.
Individual reviewer accounts and full audit trail
Every review team member gets their own login. Every approval and rejection logged with name, timestamp, and notes. Complete documentation for every piece ever reviewed.
Content generated and pre-screened
Regulatory pre-screen runs automatically. Flags attached before reviewer sees the item.
Review team notified
Email notification with item count and direct dashboard link. No manual checking required.
Reviewer approves or edits inline
Full content editable in the review interface. AI check runs on edits to catch new issues.
Marketing publishes from queue
Approved content moves to publishing queue. Marked live when published. Full pipeline tracked.
Edits feed next month’s generation
Review changes analyzed. Patterns saved. Next month’s content incorporates them automatically.
Platform intelligence
Across your entire roster.
RANKARA does not run the same playbook every month. It builds proprietary intelligence from what actually works — for your practitioners, in your markets, in your industry.
Firm-level performance insights
Which content types are driving citation wins across your roster. Which markets are most competitive. Which use case categories produce the most citations. One view for leadership without checking individual dashboards.
Cross-practitioner pattern library
When content works for one practitioner, that pattern is applied to others in similar markets and specializations. The more practitioners on the platform, the smarter the content strategy becomes for each one.
Content performance attribution
Every published piece is tracked for citation impact — before and after. The system measures what changed, records which content produced wins, and feeds that data back into next month’s strategy.
Hallucination monitoring and correction
AI platforms sometimes say incorrect things about practitioners. RANKARA monitors for inaccurate information and automatically generates correction content to address it before it affects client decisions.
Ready to see your people
in the AI answer?
Request a demo and we will show you exactly where your practitioners stand across ChatGPT, Perplexity, and Google AI — and what it takes to close the gap.