I turn complex infrastructure into clear, trustworthy product experiences.
I’m a Senior Product Manager who bridges backend systems, data, engineering, design, and customer experience. Across fintech and telecom, I’ve rebuilt legacy infrastructure, led data migrations, established product analytics foundations, and taken products from MVP to production — including money transfers and global calling experiences.
With a background in artistic photography and lighting design, I bring a trained eye for composition, clarity, and the details that shape how people experience products.
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Detailed Case Studies
Scenario-based portfolio cases built on real industry experience — synthetic data, real reasoning.
Case 1 — “Why They Leave”
Synthesis of 1,500 reviews across remittance and calling apps
This case analyzes 1,500 simulated App Store and Google Play reviews across international calling and remittance apps to pinpoint why customers churn when transactions get stuck 'in transit'.
Why this case: It demonstrates rigorous qualitative discovery with AI: tagging this volume across 4 languages manually is unfeasible, whereas an LLM with a structured codebook transforms messy text into prioritized themes backed by validation trails (human-model agreement, failure galleries, decision logs).
Privacy guarantee: The reviews are not real customer records; they were synthetically generated using LLMs from observed public complaint patterns, ensuring full transparency without NDA or data privacy concerns.
Background: A real pain point observed in user complaints: delayed transfers, withheld refunds, and eroded trust—friction that product teams often address with anecdotal fixes rather than quantified impact sizing.
Explore discovery pipeline & sizingCase 2 — “Where is my money?”
Support agent for payments in transit with asymmetric release gates
Design and prototyping of an AI support agent that handles simulated money transfer tickets by classifying intent, extracting entities, and delivering state + next steps + timelines, backed by strict guardrails and human handoff protocols.
Why this case: It serves as the portfolio's functional centerpiece: a clickable prototype that hooks hiring teams in 20 seconds and shows you can validate AI ideas independently.
Privacy guarantee: Tickets are synthetically generated from Case 1 pain points, demonstrating a working, production-style prototype without exposing proprietary internal data.
Background: Directly derived from Case 1, where transaction uncertainty was the #1 churn driver. This forms an unbroken loop: discovery → technical spec → build → evaluation, with structured testing across 3 models and realistic deflection math.
Launch interactive demo & view release gateCase 3 — Student Feedback to Roadmap
Translating student feedback into a prioritized digital roadmap
Transforming simulated feedback from meditation students (surveys, direct messages, and comments) into a prioritized roadmap for a mindfulness app, using RICE, sensitivity stress testing, and documented trade-offs.
Why this case: It's the only case grounded in a domain I personally own: I'm a certified meditation teacher through the Buddhist Studies Institute (founded by Pema Khandro Rinpoche), so I apply discovery and prioritization to a mindfulness product I teach first-hand. That keeps the portfolio out of lab-exercise territory and answers 'is this PM framework transferable?' with applied evidence.
Privacy guarantee: Data points are synthetically generated based on real-world objections and feedback patterns in meditation teaching, protecting student confidentiality while demonstrating transferable PM frameworks.
Background: A common product hurdle: students finish a live session with no continuous support between live classes and generic apps, causing practice to drop off after 10 days without retention or sustainable recurring revenue.
Explore RICE sensitivity test & trade-offsCase 4 — Retention Signals
Detecting trust-risk before churn
This case builds an explainable dashboard that turns emerging customer feedback into trust-risk alerts and product decisions, on a hypothetical international calling, subscriptions, top-up, and remittances service. The focus scenario: unexpected auto-top-up charges after a subscription-flow change.
Why this case: It shows a senior-PM operating model: detect a signal, inspect its distribution and evidence, validate it with appropriate internal data, contain potential harm, and convert verified learning into a product decision—without mistaking correlation for causality.
Privacy guarantee: All names, events, metrics, app versions, and telemetry are fictional or simulated. Qualitative categories are informed by public app-review patterns; event-level product analytics are simulated for demonstration. It does not use confidential data from any real company.
Background: Teams often react to the loudest recent complaint, missing whether a topic is genuinely worsening over time and whether it signals a high-severity loss of financial trust that requires immediate intervention.
Open interactive dashboard & decision recordCase 5 — Show the value before entering the number
Should product discovery come before number verification?
This case designs a narrow, product-first MVP for international mobile top-ups (US → Colombia) and an A/B experiment that tests whether showing illustrative recipient benefits before asking for the number increases completed purchases—without causing offer mismatches after verification.
Why this case: It demonstrates experimentation and investment rigor: a hypothesis, a metric tree with guardrails, sample-size planning, contribution-margin break-even, and a commercial-success rule that separates click lift from real business value.
Privacy guarantee: All operators, products, prices, exchange rates, promotions, experiment results and economics are fictional or simulated. No confidential employer or supplier information is used, and no real numbers or personal data are collected.
Background: Today the phone number unlocks the catalogue. Building a global pre-number catalogue is expensive; the experiment seeks evidence of commercial value before committing to that integration and operational complexity.
Explore the experiment design & A/B demoAbout me
Cintia Henriksson
Senior Product Manager with 8+ years leading consumer fintech and telecom products and over a decade in tech. From resolving 100+ complex daily technical tickets and querying databases with SQL, to scaling multi-PSP infrastructure, launching a US global operator MVP from Miami to production in under 3 months, and designing governed AI systems with release gates.