Linear Health wins on ChatGPT.
Not Basata.
20-query audit across ChatGPT, Gemini & Claude. Basata is cited on 0 of 60 responses (0%). Despite $24.5M in funding, 500K patients served, and TechCrunch coverage six weeks ago, no AI platform mentions Basata when specialty practices search for referral automation.
This report was generated using Xtrusio, an AI visibility and demand intelligence platform that analyzes how companies appear across modern AI systems such as ChatGPT, Gemini, Claude, Perplexity, and other generative engines.
The insights in this page are generated using Xtrusio’s proprietary research and content intelligence framework.
Basata does not exist in AI search.
When a practice administrator asks ChatGPT, Claude, or Gemini for AI referral automation, the answer is Linear Health, Tennr, Assort Health, or Notable Health. Never Basata. This isn’t a training-data lag — the Series A closed six weeks ago with TechCrunch coverage. The problem is structural: Basata has zero presence in the comparison pages, vendor roundups, and specialty-practice guides that AI systems cite. Linear Health — with far less funding — dominates ChatGPT because it saturated exactly those content formats.
Platform Scorecard
Basata citation rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total citations across all platforms
AI Positioning Audit
20 buyer-intent queries — click any row to see the exact question
Each query was written from the perspective of a real decision-maker researching AI-powered administrative automation for their specialty practice. These personas represent the buyers whose AI search results determine whether Basata gets discovered — or stays invisible.
| # | Query Topic | Product Line | ChatGPT | Claude | Gemini |
|---|---|---|---|---|---|
| 1 | End-to-end referral + scheduling (one tool) | End-to-End | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Our cardiology practice gets hundreds of faxed referrals a week and we’re losing patients in the backlog. Is there an AI tool that can both read the referrals and call patients to schedule them, instead of buying two separate systems?” | |||||
| 2 | AI built for specialty practices | End-to-End | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “I run a urology group. Are there AI platforms built specifically for specialty practices like ours rather than generic healthcare automation tools?” | |||||
| 3 | Same-day scheduling without adding staff | Scheduling | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What’s the fastest way to get a referred patient scheduled — ideally the same day the referral arrives — without adding front-desk staff?” | |||||
| 4 | AI voice agent for patient scheduling | Voice Agent | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Is there an AI voice agent that can automatically call referred patients and book their appointments based on our practice’s scheduling rules?” | |||||
| 5 | One vendor: docs + calls together | End-to-End | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We want one vendor that handles the whole journey from an incoming referral to a booked appointment — document processing and the patient phone calls together. Does that exist?” | |||||
| 6 | Specialty EMR integration | EHR Integration | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We use a gastroenterology-specific EMR. Are there AI administrative tools that actually integrate with specialty EMRs and not just the big hospital systems?” | |||||
| 7 | Auto-create patient chart from referral | EHR Integration | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Can AI automatically pull the clinical details out of an incoming referral and create the patient chart in our EHR for a cardiology practice?” | |||||
| 8 | Convert more referrals to booked visits | Scheduling | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We’re losing new patients because we can’t process referrals fast enough. What AI solutions help specialty practices convert more referrals into booked visits?” | |||||
| 9 | AI reads faxed referrals | Fax Intake | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What AI tools can read and process incoming faxed referrals automatically for a medical practice?” | |||||
| 10 | Clear referral/records backlog | Fax Intake | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Our practice has a huge backlog of unprocessed referrals and records requests. What software can help us clear it?” | |||||
| 11 | 24/7 AI phone answering | Voice Agent | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We can’t answer every patient call during business hours. Is there an AI phone system that can answer patient calls 24/7 for a medical office?” | |||||
| 12 | Reduce front-office workload, no new hires | Fax Intake | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “How can a specialty practice reduce the administrative workload on its front-office team without hiring more people?” | |||||
| 13 | Automate intake / stop manual data entry | EHR Integration | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Is there a way to automate patient intake and data entry so my staff isn’t manually retyping information from faxes into the EHR?” | |||||
| 14 | HIPAA-compliant back-office AI | Compliance | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What HIPAA-compliant AI tools exist for automating back-office work in a medical practice?” | |||||
| 15 | AI handles renewals & reschedules | Voice Agent | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Can AI handle routine patient phone requests like prescription renewal questions and appointment changes for a clinic?” | |||||
| 16 | Plugs into existing EHR, low IT lift | EHR Integration | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We don’t want to replace our current EHR. Are there AI automation tools that plug into our existing system with minimal IT lift?” | |||||
| 17 | Most accurate document reading at volume | Fax Intake | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Which AI platform has the most accurate document-reading technology for high volumes of messy, multi-page faxed referrals?” | |||||
| 18 | Prior authorization automation | Prior Auth | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Is there an AI tool that automates prior authorization and payer requirements for specialty referrals?” | |||||
| 19 | Insurance eligibility verification | Prior Auth | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What software automatically verifies insurance eligibility and benefits when a new referral comes in?” | |||||
| 20 | Enterprise multi-site patient access | Enterprise | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We’re a large multi-site health system. What enterprise AI platform automates patient access and intake across many locations and specialties?” | |||||
| TOTAL | 0/20 (0%) | 0/20 (0%) | 0/20 (0%) | ||
Total AI Blackout
Zero citations on every platform, every query, every product line
When a practice administrator types any of these 20 buyer-intent queries into ChatGPT, Claude, or Gemini, the AI responds with a curated list of vendors to evaluate. Basata is never on that list. Not once. Not on any platform. Not for any query.
“Our cardiology practice gets hundreds of faxed referrals a week and we’re losing patients in the backlog. Is there an AI tool that can both read the referrals and call patients to schedule them?”
“We want one vendor that handles the whole journey from an incoming referral to a booked appointment — document processing and the patient phone calls together. Does that exist?”
“I run a urology group. Are there AI platforms built specifically for specialty practices like ours?”
Basata has processed referrals for 500,000 patients. 70% of new deals come through word of mouth. Southwest Cardiovascular went from a 500-referral backlog to zero. But none of this is in the content formats AI systems cite. The TechCrunch article exists. The customer outcomes are real. They’re just not embedded in comparison pages, vendor roundups, or specialty-practice guides — the exact content that Linear Health, Tennr, and Assort Health have saturated.
AI Topic Authority Map
Query heatmap — product line × platform
| Topic | AI Leader | Basata Status |
|---|---|---|
| End-to-end referral + scheduling | Linear Health | INVISIBLE (0/3) |
| Specialty-built AI platform | Assort Health | INVISIBLE (0/3) |
| Same-day scheduling | Linear Health | INVISIBLE (0/3) |
| AI voice scheduling agent | Assort Health | INVISIBLE (0/3) |
| Faxed referral processing | Tennr | INVISIBLE (0/3) |
| Auto chart creation from referral | Linear Health | INVISIBLE (0/3) |
| Prior auth automation | Tennr / Waystar | INVISIBLE (0/3) |
| Enterprise multi-site access | Notable Health | INVISIBLE (0/3) |
3 queries
2 queries
3 queries
4 queries
4 queries
2 queries
2 queries
▷ Every product line is at 0% across every platform. This is a total content blackout — not a product gap.
Methodology
How we conducted this Xtrusio AEO/GEO Audit
This research is based on Xtrusio’s proprietary AI visibility analysis framework.
Recommendations
Prioritized actions to break the Total AI Blackout
- Publish head-to-head comparison pages: Basata vs Tennr, Basata vs Assort Health, Basata vs Linear Health — using the exact query language from this audit
- Create specialty-vertical landing pages for each target specialty: AI referral automation for cardiology, urology, GI, and ophthalmology practices
- Syndicate the Southwest Cardiovascular case study (500+ backlog to zero, 18% conversion lift) as a standalone article on health tech publications
- Get Basata included in existing vendor roundup articles and listicles about AI referral automation, AI voice agents for healthcare, and specialty-practice AI tools
- Publish workflow guide content answering the exact 20 questions in this audit, positioned as educational resources with Basata naturally embedded as a solution
- Target HIT Consultant, Fierce Healthcare, Becker’s, and specialty-practice trade publications for contributed articles featuring Basata’s Forward-Deployed Engineering methodology
- Build a structured data layer (schema markup, FAQ pages, product comparison tables) that AI crawlers can parse and cite directly
- Launch customer stories from each specialty vertical (cardiology, urology, GI) with quantified outcomes in formats that match how AI systems extract vendor recommendations
- Quarterly Xtrusio re‑audits to track gap closure and measure citation-rate improvement
Zero to Cited: Let’s Fix This
Basata has the product. The customers. The outcomes. Now it needs the content.
This research report was generated using the Xtrusio Company Intelligence Module.
