AYUSH clinic WhatsApp follow-up and DPDP evidence readiness example
A transparent synthetic proof-of-work showing how AICloudStrategist maps AYUSH and wellness clinic WhatsApp, phone, Instagram, referral, web and email enquiries into owner-visible follow-up, consent-evidence and escalation queues.
Honesty label
This is a simulated internal proof-of-work asset. It uses synthetic AYUSH clinic workflow counts only. It is not a real clinic, patient, PHI dataset, production export, legal opinion, medical advice, privacy advice, security advice, DPDP compliance attestation, audit certification, booking result, revenue result, ROI result, testimonial, logo or ranking claim.
Why this asset exists
AYUSH, wellness and specialty clinic owners often depend on WhatsApp, phone calls, Instagram DMs and web forms, but cannot quickly prove who owned each enquiry, whether late enquiries were called back, whether DPDP notice and WhatsApp opt-in evidence exist, whether AI or assistant scripts stay inside non-clinical boundaries, or whether no-shows have a recovery path.
Synthetic input summary
| Metric | Result from synthetic sample | Diagnostic meaning |
|---|---|---|
| Workflow rows reviewed | 12 | Small synthetic sample for demonstrating method, not a real clinic export. |
| Synthetic enquiries represented | 1,918 | Workflow volume across WhatsApp, phone, Instagram, referral, web form and email channels. |
| Missed / after-hours enquiries | 353 | Rows needing callback SLA visibility before automation. |
| Callback coverage on missed/after-hours enquiries | 152 of 353 (43.1%) | Illustrates the owner queue AICS would expose for late and missed follow-up. |
| Booked or resolved enquiries | 581 (30.3%) | Operational outcome in the synthetic sample only; not a real booking claim. |
| Late or missing follow-up enquiries | 1,469 | Rows where 24-hour follow-up evidence would need review. |
| Owner assignment gap enquiries | 1,008 | Rows lacking visible accountable ownership. |
| Source capture gap enquiries | 975 | Rows where attribution and channel source evidence need cleanup. |
| DPDP notice evidence gap enquiries | 1,544 | Rows where notice or purpose prompt evidence would need verification. |
| WhatsApp opt-in evidence gap enquiries | 1,806 | Rows where channel-specific opt-in evidence needs review. |
| AI boundary disclosure gap enquiries | 1,544 | Rows where scripts need clearer assistant/non-clinical boundary wording. |
| No-show recovery step gap enquiries | 1,518 | Rows where recovery ownership and sequence would need definition. |
Highest-priority synthetic workflow flags
| Workflow | Service line | Enquiries | Selected flags |
|---|---|---|---|
| AYU-011 | NRI teleconsultation | 126 | DPDP notice, WhatsApp opt-in, owner assignment, source capture, clinical escalation, AI boundary and no-show recovery gaps. |
| AYU-002 | Panchakarma package inquiry | 185 | DPDP notice, WhatsApp opt-in, owner assignment, source capture, clinical escalation, AI boundary and no-show recovery gaps. |
| AYU-008 | Detox retreat inquiry | 155 | DPDP notice, WhatsApp opt-in, owner assignment, source capture, AI boundary and no-show recovery gaps. |
| AYU-005 | Women's wellness | 196 | DPDP notice, WhatsApp opt-in, owner assignment, source capture, clinical escalation and no-show recovery gaps. |
| AYU-006 | Chronic pain program | 224 | Missed/after-hours volume plus DPDP notice, WhatsApp opt-in, AI boundary and no-show recovery gaps. |
Owner-dashboard proof pack
- Channel map for WhatsApp, phone, Instagram, web form/chat, referral and email enquiries.
- Owner assignment status for every enquiry type.
- Missed-call and after-hours callback SLA queue.
- DPDP notice/purpose prompt and WhatsApp opt-in evidence checklist.
- AI/assistant disclosure wording and non-clinical boundary review.
- Clinical escalation route owned by a qualified professional.
- No-show and medicine-refill follow-up steps with explicit approval before any real case study, testimonial, logo, booking, revenue or ranking claim.
Reproducible artifact
The source model and generated markdown report are stored internally at /home/agent/.hermes/aicloudstrategist/case-studies/simulated-india-ayush-clinic-whatsapp-dpdp-followup-2026-07-13/. Inputs are explicit in sample_india_ayush_clinic_workflows.csv; calculations are deterministic and labelled as simulated.
Verification: python3 india_ayush_clinic_dpdp_followup_diagnostic.py regenerated the report with rows=12, total_enquiries=1918, after_hours=353, callback_within_4h=152, callback_coverage_pct=43.1, booked_or_resolved=581, unresolved=1337, dpdp_notice_gap_enquiries=1544, whatsapp_opt_in_gap_enquiries=1806, ai_boundary_disclosure_gap_enquiries=1544 and no_show_recovery_gap_enquiries=1518. Input SHA256: fd6d43a4685efe6450931594f290dce97a3753b84e89fd77cbaea20f35b5b74e.
Claim boundary
No real Indian AYUSH clinic, wellness clinic, doctor, practitioner, patient, client, PHI, production data, Data Protection Board filing, DPDP compliance status, audit attestation, security audit, legal advice, medical advice, privacy advice, security advice, booking lift, revenue outcome, ROI, regulator outcome, platform partnership, logo, testimonial or ranking is claimed. Real implementation would require explicit scope, data-handling approval, organisation-approved policies, payment route and written authorization before any public proof use.