I drive product decisions for B2B and data products where APIs, analytics, and integrations shape the outcome.
From discovery and specification through launch and measurement, I turn technical constraints into testable scope, explicit trade-offs, and evidence-backed delivery.
Selected product decisions and outcomes.
B2B measurement, launch and monetization, and enterprise delivery — focused on the decisions, constraints, and evidence behind the work.
WiseTrack — B2B Mobile Measurement & Analytics
Flagship B2B product case spanning an early-stage MMP from product discovery and specification through pilot-led validation, analytical scaling, ecosystem integrations, and initial commercial adoption.
~10 pilot/onboarding apps · 1st paying client confirmed · 1.2s → 0.25s recorded API example
Sepas — From Prolonged Pre-launch to Public Release
Product and go-to-market case for moving a relationship-wellness product from prolonged pre-launch development into public release, behavioral analytics, monetization, and early market validation.
1,450 unique registered · 58 unique paying · IRR 22.1m recorded revenue
Houbad — Smart Ambulance MVP & Technical Acceptance
Enterprise product-judgment and technical-acceptance case covering a smart-ambulance MVP plus evidence-based acceptance of confidential vendor-delivered systems.
2 real ambulances in controlled EMS field test · 4 confidential systems under technical acceptance
Pain · Proof · Investment.
My default is to understand the pain first, create the smallest credible proof, and make deeper investment proportional to what the evidence supports.
- 01
Pain
Understand the customer problem before choosing the requested feature or preferred technology.
- 02
Proof
Ship the smallest credible solution that can create trustworthy behavioral or commercial evidence. Proof is evidence sufficient for the next decision, not certainty.
- 03
Investment
Use what the evidence shows — including failed attempts — to justify deeper product or technical investment.
Two guardrails: failed attempts are evidence too, and claims should stay inside the boundary of what was actually observed.
Technical fluency is most useful when it improves communication.
“Alireza's technical background made communication with the development team much easier, while he was also able to work effectively with non-technical stakeholders. During the Smart Ambulance project, he kept the team focused through unclear requirements and delivery challenges, prioritized effectively, and helped us move the project toward a concrete outcome.”
Currently at SnappPay · Translated and edited for clarity from original Persian feedback · Used with permission
See the source caseTechnical depth in support of product judgment.
Selected AI, automation, and data work demonstrates implementation depth and feasibility judgment; the professional product cases above remain the primary portfolio.
AcuLearn — AI-Powered Course Advisor
AI-powered Telegram course advisor that interviews learners, assesses knowledge and goals, recommends a personalized module sequence, preserves context, and escalates unresolved cases to human support.
~500 starts · ~300 complete interviews · 3rd place
IranArze — Automated Job Deadline Notification
End-to-end Telegram automation that extracts active opportunities, calculates Jalali deadlines, publishes a consolidated daily digest, and sends urgent reminders without routine manual content operations.
~500 members · ~2 months automated operation
Product judgment, made explicit.
Notes grounded in real cases, written to make the reasoning behind product decisions easier to inspect.
View all writingPilot Is Not Traction
Why pilot/onboarding evidence should remain distinct from paid adoption.
Data Semantics Is Product Design
Why metric meaning, attribution, and reporting definitions are part of the product.
Shipping as a Learning Strategy
How release and instrumentation can replace prolonged assumptions with behavioral evidence.
Need product judgment where technical complexity and ambiguity meet?
The case studies show the decisions; the resume shows the scope. If the problem is B2B, data-heavy, API/integration-heavy, or technically constrained, that is where my background is most useful.