01
Market Signal
Find the pressure point.
Claims leakage, access gaps, referral friction, policy shifts, provider demand, and buyer intent.
From complexity to intelligence
Convert healthcare complexity into commercial intelligence.
Market signal, payer economics, operating motion, proof loops, and board-ready decisions across provider networks, value-based care, specialty pharmacy, behavioral health, dialysis, and AI-enabled RevOps.
Bring me fragmented demand, payer complexity, and messy handoffs.
Build the OS SignalEconomicsOperating PlanProofDecision
$125.9M
Leakage identified
$8M+
ARR built from zero
1,200+
Clinicians onboarded
~4,000
Patients activated
$13M+
Referral revenue
1,300+
Provider relationships
Core purpose
Make healthcare complexity investable, launchable, and measurable.
This portfolio is a decision environment for founders, CEOs, CFOs, and operating partners. It shows how raw healthcare signal becomes an economic thesis, an operating motion, a proof cadence, and a board-ready decision path.
The question is not whether Azis has done strategy. The question is whether he can turn messy demand, reimbursement, provider behavior, and RevOps data into a system leaders can fund and scale.
Context Engine
Signal to Scale
A layered map for leadership decisions.
02
Economic Context
Show why it matters financially.
Revenue quality, payer economics, contribution margin, payback, risk adjustment, and value proof.
03
Operating Motion
Turn the thesis into work.
ICP, account targeting, CRM lifecycle, provider workflow, implementation gates, and field cadence.
04
Proof Cadence
Make progress auditable.
Leading indicators, pilot evidence, handoff quality, activation, utilization, retention, and expansion triggers.
05
Decision Path
Tell leadership what to do next.
Where to invest, what to sequence, which accounts deserve executive attention, and what must be killed.
Where is complexity hiding revenue quality?
Which signal is strong enough to fund?
What proof would convince the CFO?
What operating system makes the motion repeat?
Executive decision layer
From market signal to board-ready decision.
The portfolio has one job: help leadership see where the healthcare signal is real, what the economics imply, which motion can operate, what proof is required, and what decision should follow.
CEO/CFO
Commercial OS
REVRevenue quality
LKLeakage capture
PAYPayer economics
PRVProvider activation
IMPImplementation risk
ROIValue proof
Prioritize capital
De-risk launch
Prove payback
- 01
Market Signal
Identify where demand, leakage, policy, referral density, and payer pressure create a real commercial opening.
- 02
Economics
Translate the opening into revenue, margin, payback, contracting, and implementation assumptions a CFO can test.
- 03
Operating Motion
Connect sales, partnerships, RevOps, provider workflow, and implementation handoffs into one accountable cadence.
- 04
Proof
Define the evidence that shows the motion is working: activation, utilization, payback, launch readiness, and value proof.
- 05
Decision
Show what to fund, sequence, kill, staff, or expand before capital and executive attention get diluted.
CEO/CFO view
Vertical signal stacks for revenue quality.
A board-ready view of how market signal, payer economics, provider activation, RevOps control, and value proof stack into decisions a CEO and CFO can actually use.
$125.9M
Leakage opportunity
Claims and referral signal translated into targetable growth lanes.
$8M+
Built ARR
Regulated specialty pharmacy platform built from zero to strategic exit.
$13M+
Referral revenue
Annualized referral revenue across a dialysis network growth motion.
18%+
EBITDA discipline
Margin discipline maintained in a founder-to-exit build.
Confidential source materials stay private. The public layer shows the executive operating logic, proof signals, and decision architecture behind the work.
Operator evidence
Proof that the system works.
From market signal to operating proof to board-ready decision. The metrics below show where signal became economics, operating motion, proof, and executive decision support.
Leakage identified
$125.9M
Referral and claims leakage surfaced through Databricks claims forensics across 3B+ records.
ARR built from zero
$8M+
Specialty pharmacy built from $0 with 18%+ EBITDA and strategic exit to Rite Aid.
Clinicians onboarded
1,200+
Psychiatry, therapy, and addiction recovery clinicians onboarded in 90 days.
Patients activated
~4,000
YTD patients activated through referral, network, and payer-aligned growth systems.
Referral revenue
$13M+
Annualized referral revenue across a 13-facility New York dialysis network.
Provider relationships
1,300+
Provider relationships built through referral mapping and specialty prioritization.
Founder signal
Rare founder-to-exit signal, applied to healthcare GTM.
Co-founded and scaled a specialty pharmacy platform from zero to exit, with the unglamorous healthcare details intact: payer/PBM contracting, 340B, URAC, inventory, ScriptPro automation, compliance, and buyer diligence.
0
Start from zero
Co-founded specialty pharmacy platform across 340B, specialty therapeutics, payer/PBM contracting, compliance, and patient access.
Build
Build the operating model
Created licensing, compliance, payer contracting, inventory, dispensing, patient access, and provider-partnership workflows.
Scale
Scale revenue quality
$0 to $8M+ ARR with full P&L ownership and 18%+ EBITDA through drug mix, payer contract, and throughput discipline.
Accredit
De-risk the asset
Secured URAC specialty pharmacy accreditation and expanded complex therapy access to 5,000+ covered lives.
Automate
Operationalize throughput
Implemented ScriptPro robotics, improving throughput 150% while maintaining 99.97% accuracy.
Exit
Exit with diligence readiness
Orchestrated strategic exit to Rite Aid, including audit readiness, buyer diligence, integration planning, and data cleanup.
Commercial architecture
The Azis Dabas Operating System
A five-layer model for turning market signal, economics, operating motion, proof, and decision logic into a system founders can operate, measure, and scale.
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.
$125.9M leakage identified through claims forensics and service-line opportunity mapping.
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.
1,362 provider relationships and ~4,000 activated patients through 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.
$13M+ annualized dialysis referral revenue and 32% referral cycle-time compression.
Pilot designValue reviewsPayer dashboardsValue realization
RCM, payer workflow, and agentic orchestration
Design the healthcare AI operating layer before buying another point solution.
Healthcare AI is moving from demos to governed workflow systems: prior authorization, denials, RCM, payer portals, claims intelligence, access, referral handoffs, CRM, and value proof. The design problem is not whether an agent can answer a question. It is whether the system can assemble context, route work, preserve human judgment, act inside the right tool, and prove financial or operational lift.
Block-level agentic orchestration
From signal to evidence to action, with humans in the control loop.
AI recommends, assembles, routes, drafts, scores, and monitors. Humans approve clinical, financial, payer, and patient-impact decisions.
01
Capture the work signal.
Signal intake
Claims, referral leakage, prior-auth requests, EHR tasks, CRM activity, payer portal events, denial codes, care gaps, eligibility flags, and patient access friction.
02
Build the evidence packet.
Context assembly
FHIR/API data, policy rules, plan requirements, chart notes, diagnosis/procedure context, benefits, prior history, payer correspondence, and document lineage.
03
Run bounded task agents.
Agentic work blocks
Eligibility, authorization packeting, coding review, denial triage, appeal drafting, underpayment detection, referral routing, patient outreach, and CRM next-best action.
04
Preserve judgment and accountability.
Human review control
Approval queues, exception handling, clinical signoff, financial thresholds, payer escalation, compliance policy, audit logs, and stop conditions.
05
Move inside the operating stack.
System action
Create CRM tasks, update work queues, prepare payer API submissions, assemble portal-ready packets, trigger follow-up, and document what changed.
06
Measure whether the system works.
Proof loop
Auth cycle time, clean-claim rate, denial prevention, appeal win rate, A/R days, underpayment recovery, staff hours saved, patient access, and revenue quality.
CFO, VP Revenue Cycle, COO
RCM and denial prevention
Problem
Denials, documentation gaps, payer-specific rules, coding defects, and underpayment signals are discovered too late.
Design pattern
Pre-submission review, medical-necessity checks, denial-risk scoring, appeal packet drafting, underpayment triage, and work queue governance.
Proof loop
Clean-claim rate, denial rate, preventable denial dollars, appeal yield, A/R days, and staff hours redirected.
COO, clinical ops, access, payer operations
Prior authorization orchestration
Problem
Authorizations live across portals, APIs, payer rules, chart evidence, clinical documentation, and manual follow-up.
Design pattern
Eligibility verification, benefits context, policy-aware evidence packets, API/portal routing, status monitoring, and human approval gates.
Proof loop
Authorization cycle time, avoidable delays, first-pass approvals, peer-to-peer reduction, abandonment risk, and escalation accuracy.
CEO, CFO, strategy, network, service-line leaders
Claims intelligence and leakage capture
Problem
Claims and referral data do not naturally become account strategy, service-line focus, or provider-network action.
Design pattern
Leakage maps, payer/service-line segmentation, target scoring, referral corridor intelligence, and field-motion triggers.
Proof loop
Addressable leakage, activated patients, referral conversion, downstream value, and account-priority accuracy.
Governance posture
No autonomous clinical decisions.
No autonomous denial, referral, or patient-impact action without human review.
Every agent has a bounded job, owner, evidence source, escalation path, and audit trail.
Use APIs and structured data where the market supports them; design exception handling for the rest.
Measure value in operating metrics leaders already care about, not model novelty.
Design the system before the stack.
The best AI healthcare work starts with workflow ownership, evidence sources, exception handling, and proof metrics. Then the models, agents, integrations, and CRM/RCM tools can be selected around the operating truth.
Open solutions designDeep proof
Case studies built for founder diligence.
Problem, operator moves, founder takeaway, what I would do again, and what the work proves.
$0 -> $8M+ ARR -> Rite Aid 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
$125.9M leakage surfaced through claims forensics
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
1,200+ clinicians onboarded in 90 days
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
Proof grid
Six proof lanes, one operating pattern.
Each proof lane connects healthcare complexity to the decision leaders need next: focus, fund, fix, scale, or stop.
Claims Forensics
Databricks claims analysis converted raw leakage into provider network, specialty, and service-line opportunity.
$125.9M
Leakage identified
DatabricksClaimsReferral leakage
Provider Network Growth
Referral, network, and payer-aligned growth systems that created measurable patient activation and downstream value.
1,300+
Provider relationships
~4,000
Patients activated
~$22M
Downstream LTV
Provider corridorsService-line growthPayer alignment
Behavioral Health GTM
HubSpot lifecycle architecture, AI-assisted scoring, attribution, and cohort economics for same-day care access.
1,200+
Clinicians onboarded
HubSpotCAC/LTVAccess capacity
Dialysis / CKD Growth
Referral architecture, intake redesign, payer dashboards, and facility cadence across a 13-site dialysis network.
$13M+
Referral revenue
DialysisCKDReferral ops
Specialty Pharmacy Build
Founder-to-exit operating platform across 340B, specialty workflows, PBM contracting, URAC, and ScriptPro robotics.
$8M+
ARR built from zero
99.97%
Specialty accuracy
Founder-to-exitURACP&L
Value-Based / Payer Strategy
HEDIS/Stars, RAF/HCC, CMS-HCC V28, Medicaid, Medicare Advantage, CKCC/KCC, and care-gap economics.
$3.2M+
VBC contracts
Payer strategyVBCCare gaps
Published voice
Signal Vault for reputation and proof.
A public layer for the work behind the brand: daily market reads, operator essays, case proof, AI RevOps, payer strategy, claims intelligence, and founder-to-exit credibility.
AI
Architecture layer
Published signal
AI Solutions Design
RCM friction -> governed AI operating layer
A high-level design module for healthcare teams that need AI workflow architecture before buying another point solution.
HP
Published market brief
Published signal
Healthtech Pulse
Daily signal -> operator read
A daily public-facing brief that turns healthcare and healthtech news into market judgment for founders, CEOs, CFOs, and commercial leaders.
OA
Public writing archive
Published signal
Operator Essays
Public thesis -> reputation
A consolidated archive of Azis's public writing, built to show a durable point of view on healthcare commercialization and operating complexity.
AI
Operating system lane
Published signal
Healthcare AI RevOps
Workflow signal -> revenue quality
The practical AI layer: not hype, but the operating infrastructure that helps commercial teams know what to pursue, prove, and scale.
VB
Commercial strategy lane
Published signal
Payer and VBC Strategy
Reimbursement pressure -> GTM wedge
A strategy lane for companies that need to sell into healthcare economics, not just pitch software around the edge of the system.
CI
Data-to-GTM lane
Published signal
Claims Intelligence
Leakage -> account strategy
The analytics layer that proves Azis can connect healthcare data to market selection, revenue architecture, and executive decision-making.
Pulse and blog
Healthcare market intelligence, in public.
Pulse tracks the daily healthcare market. Blog holds the deeper essays. Together they show how payer pressure, AI adoption, access constraints, interoperability, and value-based care become operating judgment.
Healthcare lanes
Domains where the pattern repeats.
Behavioral Health
Specialty Pharmacy
Dialysis / CKD
Multispecialty Care
Payer / VBC
Healthcare AI
Care Navigation
Provider Networks
Tools and methods
Operator stack for revenue quality.
DatabricksSQLPythonHubSpotClaims forensicsLTV/CAC modelingAI-assisted lead scoringAttributionPayer dashboardsCRM hygienePipeline governanceValue-realization modeling
Build the wedge. Prove the motion. Scale what repeats.
For Series A/B teams that need sales, partnerships, implementation, payer logic, and revenue intelligence to become one operating system.