Is this a real IVF or fertility clinic case study?

No. This is a simulated proof-of-method asset built with synthetic sample rows. It is not a real clinic, doctor, patient, couple, PHI, client outcome, testimonial, medical advice, privacy advice, DPDP compliance claim, revenue claim, appointment claim, pregnancy claim or live-birth claim.

What does the diagnostic demonstrate?

It demonstrates how AICS would turn fragmented fertility enquiries from Google Business Profile, WhatsApp, Instagram, paid search, doctor referrals, webinars, web forms, phone calls, camp-list reactivation and partner handoffs into evidence queues for callback SLA, counsellor ownership, source and cycle-stage capture, notice prompts, opt-in evidence, AI boundary disclosure, clinical escalation and no-show recovery.

Can this guarantee consults, treatment outcomes or compliance?

No. The numbers are synthetic arithmetic outputs only. A real pilot would need permissioned data, clinic approval and qualified legal, privacy and clinical review before publishing any outcome or compliance statement.

Simulated proof asset · India Healthcare GrowthOS

Simulated India IVF/fertility counsellor SLA + DPDP diagnostic

This no-fake-client proof asset shows how AICS can inspect fertility enquiry leakage before a clinic scales more WhatsApp, Google, referral, Instagram, webinar, paid-search or reactivation follow-up. It is synthetic only: no real clinic, doctor, patient, PHI, medical outcome, DPDP compliance, consultation, pregnancy, live-birth, revenue or ROI claim is made.

Request an IVF workflow fit check View IVF leakage checklist Back to proof hub

Important claim boundary: this page is a simulated proof-of-method demonstration. Synthetic only. No real IVF clinic, no fertility clinic, no doctor, no patient, no couple, no PHI, no client, no testimonial, no logo, no certification, no platform partnership, no DPDP compliance, no medical advice, no legal advice, no privacy advice, no security advice, no appointment outcome, no consultation outcome, no pregnancy outcome, no live-birth outcome, no revenue, no ROI, no ranking, no ad-performance and no AI accuracy claim is made.

Synthetic enquiries1,62612 synthetic workflow/channel rows

After-hours enquiries260sample events needing next-step ownership

Missed calls103sample phone leakage events

Counsellor SLA coverage proxy36.1%after-hours + missed-call events with callback in 2 hours

Consult attendance proxy71.8%399 attended from 556 synthetic booked consults

Cycle-stage capture gap889enquiries needing IVF/IUI/fertility-stage tagging

DPDP notice prompt gap1,395records requiring evidence before claims

WhatsApp opt-in gap1,558follow-up evidence rows needing repair

Diagnostic method

What an IVF or fertility clinic owner can inspect before scaling growth spend

The diagnostic converts enquiry rows into operating queues: source tagging, cycle-stage capture, counsellor ownership, consent/notice evidence, AI/admin boundary disclosure, clinical escalation routes, closure reasons and no-show recovery.

Evidence/control area	Synthetic enquiry volume with gap	Why AICS would flag it
Cycle stage captured	889	IVF, IUI, fertility assessment, donor, male-factor and reactivation follow-up should not collapse into generic nurture messages.
Source captured	777	Owners cannot compare Google, WhatsApp, paid search, doctor referrals, webinars, camp lists and partner handoffs reliably.
Counsellor owner assigned	1,184	Unowned enquiries make callbacks, sensitive context and no-show recovery hard to enforce.
DPDP notice prompt present	1,395	The workflow lacks evidence that data-use notice language was presented; this is an operational prompt, not compliance advice.
WhatsApp opt-in evidence	1,558	Follow-up templates should distinguish permissioned messages from ad hoc staff messaging.
AI boundary disclosure	842	Patients or couples should not confuse automated triage/admin assistance with clinician judgment.
Clinical escalation route	428	Administrative automation needs a documented route for clinical questions or sensitive fertility context.
No-show recovery sequence	1,395	Booked-but-not-attended consult rows need same-day/next-day recovery ownership.
Closure reason captured	1,558	Without closure reasons, owners cannot separate timing, cost, duplicate, do-not-contact and clinical-fit issues.

Before diagnostic

Reception and counsellors handle WhatsApp chats, missed-call lists, web forms, webinar signups, partner referrals and ad-platform exports separately.
The owner reviews consultation counts but cannot trace where booked-not-attended and unresolved enquiries originated.
Notice wording, opt-in evidence and AI/admin boundary prompts are inconsistent.
Clinical escalation depends on staff memory instead of a documented route.

After diagnostic operating rule

Each channel becomes a lightweight evidence row with source, cycle stage, owner, status, callback time, notice prompt and closure reason fields.
A weekly owner memo shows missed-call backlog, counsellor SLA coverage, source/stage attribution gaps and evidence prompt gaps.
Automation is constrained until WhatsApp opt-in, AI/admin boundary wording and clinical escalation routes are reviewed.
Source hygiene, two-hour callbacks, closure tagging and consult no-show recovery tasks are assigned visibly.

Evidence needed before publishing any real clinic outcome

A real pilot should collect only permissioned, minimized operational exports where possible; define counsellor SLA and owner rules; document consult status, notice/purpose prompts, WhatsApp opt-in handling, AI/admin boundary wording and clinical escalation routes; and obtain explicit clinic approval plus qualified legal/privacy/clinical review before any public patient, DPDP, consultation, pregnancy, live-birth, revenue or ROI statement.

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Reproducibility

Internal synthetic artifact: /home/agent/.hermes/aicloudstrategist/case-studies/simulated-india-ivf-fertility-counsellor-sla-dpdp-2026-08-20/. The generator regenerated this report with input SHA256 cbad9928b9a5c39d6eb4dc23495ccd10b180a34116e45aa1bd4ec6dac7998001, report SHA256 d12baddafa3e8c5e65a09fca04252dfd2f0b0a8d2da1d7efe7c08346eaa34bfd and generator SHA256 9d3b7f240dbb2a4c3a724a9d1841e934c89fb7d80010ad4d5349cfc5df5689cf.

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