Azis R. Dabas / healthcare GTM operator
I build the commercial operating systems healthtech startups need after product-market fit.
Founder-to-exit operator across payer strategy, provider networks, value-based care, claims intelligence, AI-enabled RevOps, and revenue-quality systems built for CEOs, CFOs, and Series A/B founders.
Operator CoreStrategy + Systems + Proof
Payers
Providers
Data + AI
RevOps
Claims
Proof
Founder-to-exit signal
Regulated healthcare assets built for diligence, not demo day.
Start from zero
Co-founded specialty pharmacy platform across 340B, specialty therapeutics, payer/PBM contracting, compliance, and patient access.
Build the operating model
Created licensing, compliance, payer contracting, inventory, dispensing, patient access, and provider-partnership workflows.
Scale revenue quality
Scaled revenue quality with full P&L ownership, margin discipline, payer-contract logic, and throughput control.
De-risk the asset
Secured URAC specialty pharmacy accreditation and expanded access through payer and specialty workflows.
Operationalize throughput
Implemented ScriptPro robotics and regulated workflow automation to improve throughput and operating reliability.
Exit with diligence readiness
Orchestrated strategic exit to Rite Aid, including audit readiness, buyer diligence, integration planning, and data cleanup.
The GTM operating system
One stack for market signal, growth motion, revenue systems, enablement, data, and trust.
01
Market Map
Translate policy, reimbursement, provider density, referral friction, and buyer economics into a practical territory and segment map.
Founders get a sharper wedge and stop treating every interested account as equal.
Claims leakage translated into service-line opportunity mapping and account focus.
Claims forensicsPayer economicsMarket mapsProvider corridors
02
ICP / Segmentation
Define who can buy, who can implement, who creates utilization, and who can become a repeatable reference account.
Revenue quality improves because pipeline is organized around launchability, not vanity interest.
Dementia-care wedge logic: clinical credibility plus senior-heavy PCP scale.
ICP scoringReferral densityImplementation readinessClinical adjacency
03
GTM Motion
Build the wedge, prove the motion, operationalize the handoff, then scale what repeats.
Sales, partnerships, implementation, and customer success become one operating cadence.
Provider relationships and patient activation translated into referral and payer-aligned growth systems.
Named-account strategyPartner motionsService-line GTMField cadence
04
RevOps Intelligence
Turn CRM, claims, referral, CAC/LTV, and cohort signals into a commercial control layer.
The company sees what is working before the board meeting and fixes handoffs before revenue leaks.
HubSpot RevOps built from zero with AI-assisted scoring, attribution, cohort segmentation, and LTV modeling.
HubSpotDatabricksSQLAttributionLTV/CAC
05
Launch / Expansion Engine
Convert pilots into repeatable operating systems: account plans, implementation gates, value proof, and expansion triggers.
Deals close with a launch plan, not just a signature.
Dialysis referral architecture improved revenue quality, referral cycle time, and care handoffs.
Pilot designValue reviewsPayer dashboardsValue realization
Operating chapters
Every click should feel like a decision path.
01Commercial OS
Commercial operating systems for healthtech companies after product-market fit
Most healthcare startups do not need more pitch polish. They need a connected operating layer across buyer signal, payer economics, implementation, proof, and expansion.
Commercial OS02Claims IntelligenceClaims leakage becomes a map for where to focus, sell, and scale
Claims and referral data become useful only when they turn into a commercial thesis: which accounts matter, which corridors leak, and what proof the buyer needs.
Claims-to-GTM intelligence03AI + RCMAgentic orchestration for prior auth, denials, access, and revenue quality
The point is not autonomous AI theater. It is governed workflow: signal intake, context assembly, bounded agents, human review, action, and proof.
Governed AI workflow04Provider / Payer FieldProvider networks, payer logic, and field cadence in one operating system
Referral corridors, care gaps, VBC economics, and provider activation become one field motion when the system explains who to pursue and what to prove.
Provider-payer field architecture05Founder-to-ExitRegulated healthcare assets built for diligence, not just demos
Founder-to-exit work means the unglamorous infrastructure is real: accreditation, contracting, workflow, automation, compliance, buyer diligence, and operating discipline.
Diligence-ready buildCase studies
Proof maps, not isolated wins.
Build -> scale -> diligence-ready exit
Founder-to-Exit Specialty Pharmacy
Rare founder-to-exit signal, applied to regulated healthcare GTM.
This was not a pharmacy growth story in isolation. It was a regulated healthcare ecosystem build where prescriber trust, patient access, payer/PBM rules, accreditation, inventory control, automation, margin, and buyer diligence all had to become one operating system.
DemandWhere does provider and patient need concentrate?
ReimbursementCan the revenue survive payer and PBM economics?
OperationsCan the platform fulfill accurately at scale?
Founder-to-exitSpecialty pharmacyURACP&L
Claims signal -> account strategy
Databricks Claims Forensics / Referral Leakage
Claims intelligence translated into service-line, provider-network, and account-target decisions.
The leakage work was not a data exercise. It was an ecosystem translation problem: claims data had to become market structure, provider behavior, payer logic, service-line priority, and field action.
Data SignalWhere is leakage occurring?
Market MapWhich leakage is addressable?
GTM TranslationWho should the team pursue?
DatabricksClaims forensicsReferral leakageProvider network
Access demand -> lifecycle intelligence
Behavioral Health Growth / AI RevOps
Commercial infrastructure for access, capacity, attribution, and patient-acquisition quality.
Behavioral health growth was an ecosystem problem: patient demand, clinician supply, reimbursement fit, acquisition quality, care access, CRM hygiene, and lifecycle operations had to move together.
AccessWhere does demand need care now?
SupplyCan clinician capacity absorb demand?
RevOpsCan the system see what is happening?
Behavioral healthHubSpotAI RevOpsCAC/LTV
Signal vault
Public voice, private proof discipline.
AIArchitecture layer
AI Solutions Design
Agentic orchestrationRCM friction -> governed AI operating layer
A high-level design module for healthcare teams that need AI workflow architecture before buying another point solution.
Reputation use
Shows how Azis translates healthcare AI trends into system architecture for RCM, prior auth, claims, access, payer workflow, and RevOps.
What it proves
Signal intake, context assembly, bounded agents, human review, system action, and proof loops.
AI solutionsRCMAgentic workflow
Open signal
HPPublished market brief
Healthtech Pulse
Published voiceDaily signal -> operator read
A daily public-facing brief that turns healthcare and healthtech news into market judgment for founders, CEOs, CFOs, and commercial leaders.
Reputation use
Shows how Azis reads healthcare AI, payer pressure, access, policy, and provider economics in public.
What it proves
Source-linked market intelligence, founder implications, and operating questions executives can act on.
Healthtech newsMarket signalPublished voice
Open signal
OAPublic writing archive
Operator Essays
Thought leadershipPublic thesis -> reputation
A consolidated archive of Azis's public writing, built to show a durable point of view on healthcare commercialization and operating complexity.
Reputation use
Gives hiring teams and founders a deeper read on Azis's healthcare market judgment beyond a resume.
What it proves
Essays on payer strategy, VBC, interoperability, healthcare AI, New York market structure, and operating systems.
EssaysHealthcare strategyPublic archive
Open signal
AIOperating system lane
Healthcare AI RevOps
AI operating depthWorkflow signal -> revenue quality
The practical AI layer: not hype, but the operating infrastructure that helps commercial teams know what to pursue, prove, and scale.
Reputation use
Positions Azis as an operator who can make AI practical inside CRM, attribution, lifecycle, claims, and launch motions.
What it proves
AI-assisted lead scoring, HubSpot lifecycle design, LTV/CAC modeling, attribution, and workflow automation.
AI RevOpsHubSpotWorkflow automation
Open signal
Build the wedge. Prove the motion. Scale what repeats.