Ramona Furter
See the spec
Application · Crypto.com · Product Manager, AI

I ship AI products, hands on the model and the metrics.

Twenty-one years building products and businesses, more than a decade of it in product management, and today leading AI business models at Swiss Post. I stay close to the model, the RAG pipeline and the A/B test, not the roadmap alone. This site is my application for Product Manager, AI at Crypto.com.

See an AI feature I'd ship Read the CV
AI leadAI business models, Swiss Post
LLM · RAG · agentsbuilt with, hands-on
+9% / +15%lift, via A/B testing
21 yrs10+ in product management
Portrait of Ramona Furter
Why I fit

What this role needs, and where I've done it

This role is about taking an ambiguous AI opportunity, turning it into a real product with a cross-functional squad, and proving it moved a number. That's the work I do now. Here's how it lines up with what you're after.

01

Shipping AI features to production

I'm doing this today: leading AI-driven business models from opportunity to live product, and building my own products end to end with AI workflows. I know what it takes to get a model out of a demo and into something people rely on.

Proof: AI Project Lead for business development at Swiss Post; built Pedal Peak end to end with AI workflows.

02

Hands-on with LLMs, RAG and agents

I'm a practitioner, not just a sponsor. I prompt, retrieve, ground and chain. I can sit with ML engineers and talk evaluation, hallucination, latency and cost as real constraints, not buzzwords, and make the build-vs-buy call.

Proof: build daily with Claude, GPT and n8n; the AI spec further down this page is mine, architecture and guardrails included.

03

Leading cross-functional squads

I've led engineers, designers, analysts and external partners to ship, owning the budget, the KPIs and the hard prioritisation calls. Getting a mixed team to one outcome is the part I'm best at.

Proof: led a cross-functional team and agencies at ifolor; founding team that grew WePractice to 23 people across 10 locations.

04

Ambiguity into specs and rollout plans

As a venture builder and founder I've turned vague opportunities into MVPs, then into phased rollouts that actually ship. I scope to the smallest thing that proves value, then scale what works and kill what doesn't.

Proof: ran market pilots from MVP to launch at Die Mobiliar (Smide, now BOND Mobility); built full go-to-market at Sparrow Ventures and WePractice.

05

Metrics, experiments and data intuition

I define success up front and let the experiment decide, comfortable in analytics, SQL-level questions and A/B design. I trust the evidence over the loudest opinion in the room.

Proof: +9% conversion and +15% checkout lift at ifolor through research, A/B testing and analytics on a CHF 100M+ business.

06

Fintech, compliance and senior stakeholders

Much of my career has sat inside regulated, money-moving organisations, where safety and compliance are the constraint, not an afterthought. I've made the case to C-level and investors and held the line.

Proof: owned UBS and Baloise partnerships at Brixel; ran innovation inside Die Mobiliar; reported to C-suite at ifolor; raised two rounds.

Curriculum vitae

Ramona Furter

Product and AI lead in Zurich with over twenty years of experience, more than a decade in product management, open to relocating to Dubai. I turn ambiguous problems into shipped products and measured results, increasingly with AI at the core. German and Swiss German native, English fluent, French conversational.

Jan 2026 to present

AI Project Lead, Business Development

Swiss Post, Advertising · Zurich

  • Lead AI-driven business models for Swiss Post Advertising, from sizing the opportunity to building and running the roadmap.
  • Turn AI ideas into go-to-market plans and new revenue, tracked with clear KPIs.
  • Run cross-functional work from concept to launch across product, tech, data and commercial teams.

Oct 2024 to Jul 2025

Senior Product Manager, Lead E-Commerce

Ifolor Group · Zurich

  • Owned the e-commerce ecosystem and strategy for a CHF 100M+ business, reporting to C-level.
  • Lifted conversion 9% and the checkout step rate 15% through research, A/B testing and analytics.
  • Led a cross-functional team and external agencies, owning budget, resourcing and KPIs.

Jun 2023 to Sep 2024

Lead Project Manager

Brixel · Zurich

  • Owned the partnerships with financial institutions, UBS and Baloise, that drove growth.
  • Was the main bridge between senior client stakeholders and the internal product team.

Mar 2020 to May 2023

Marketing & Growth Lead, Founding Team

WePractice · Sparrow Ventures (Migros Group) · Zurich

  • Founding team of a mental-health venture. Closed two funding rounds and grew it to 10 locations, 23 people and 170+ customers.
  • Generated 1000+ client matches in year one and built the full go-to-market on a hypothesis-and-data approach.
  • Built and led the marketing and sales team after Series B, owning budget, KPIs and growth.

Sep 2019 to Sep 2022

Growth & Venture Builder

Sparrow Ventures · Zurich

  • Built and ran growth and go-to-market for several internal startups, from early validation to scale-up.
  • Used research and experimentation to improve conversion, lower acquisition cost and raise customer lifetime value.

Jan 2017 to Aug 2019

Intrapreneur, Innovation

Die Mobiliar · Bern

  • Ran market pilots for new products (Smide, now BOND Mobility, plus XperCheck and Lizzy) from MVP to launch, inside one of Switzerland's largest insurers.
  • Coached cross-functional teams and explored new data and partnerships.
A worked example

An AI feature I'd ship for Crypto.com

Not a slide of buzzwords. A real feature taken the way I'd actually run it: frame the opportunity, make the build-vs-buy call, design the architecture with compliance built in, then ship it in phases against hard metrics. Click through the four steps, and try the pipeline.

Worked example · illustrative figures

Frame the opportunity, then the bets

Start with the user pain and one number worth moving, not the model. Then write the few hypotheses that decide whether it's worth building.

The feature

A trading copilot that answers "what's happening with my portfolio, and what should I know?" in plain language, grounded in the user's own data and compliant by design.

Hypothesis A
Support, not just chat

Most "AI assistant" value is deflecting support tickets and onboarding friction, not flashy advice. The win is resolution rate and time-to-first-trade.

Test: top support intents vs what an LLM can safely resolve.
Hypothesis B
Grounding beats fluency

Users trust answers tied to their real balances and the latest data. A fluent but ungrounded model erodes trust fast in a money app.

Test: trust and CSAT, grounded answers vs generic ones.
Hypothesis C
Compliance is the gate

In crypto the blocker isn't capability, it's safety: no advice that crosses into regulated territory, no hallucinated numbers, full auditability.

Test: red-team pass rate before any wide release.
From day one

My first 90 days

Roughly how I'd spend my first three months as an AI PM at Crypto.com: learn the ground truth, pick the highest-value safe bet, and ship something measurable.

Phase 1 Days 1 to 30

Learn the ground truth

  • Meet the ML engineers, data scientists, designers and compliance leads I'd build with.
  • Map where AI already lives in the product and where it stalls, and learn the safety and regulatory constraints cold.
  • Get into the data and the support and onboarding funnels to find the real pain.
Phase 2 Days 31 to 60

Pick the bet, write the spec

  • Choose one high-value, defensible AI feature and write the spec, metrics and build-vs-buy call.
  • Stand up the evaluation set and guardrail plan before a line of product code.
  • Align engineering, compliance and leadership on scope and the phased rollout.
Phase 3 Days 61 to 90

Ship and measure

  • Get a first version into an internal or small live test behind a flag.
  • Run the A/B test against the agreed metrics and report results honestly, wins and misses.
  • Turn what we learned into the next iteration and the start of the AI enablement playbook.