Omer Abramovich
Product leader & builder
Product judgment. Builder instinct.
Strategy ↔ Systems ↔ Experience

I turn messy problems into products that move the business.

Product leader. 0→1 builder. AI practitioner.I work from strategy and user behavior all the way to prototypes, systems, experiments and measurable outcomes.

Explore my work Let’s talk
ProductAIAutomationGrowthUX
THE WORK, IN ONE PICTURE
From a messy problem to a clear product systemUsers, friction, data and ideas converge into product judgment, followed by building, testing and measuring an outcome. Users Data Friction Ideas Productjudgment Something that works.BUILD → TEST → MEASURE Find the leverage point.
CLARITY IS THE PRODUCT.
01 / What I love

Hard, slightly
messy problems.

The ones where people, business economics, data and technology collide. I enjoy turning something vague into something people can see, test and use.

01

Find the real problem.

Observe behavior and workflows before prescribing technology.

02

Build to learn.

Make flows, interfaces and AI behavior tangible. Then test the assumptions.

03

Prove the value.

A feature, model or agent only matters when it changes an outcome.

02 / Selected work

Six problems.
Six different ways in.

What needed to change. The product decision that mattered. And what it made possible.

↓ 06 STORIES
01 / Agentic financial insights

Less manual analysis.
Faster action on financial performance.

Agentic AI investigates KPI anomalies and prepares escalations—giving analysts time back.

Financial report
Sample data
MetricActualvs. last month
Revenue MTD€4.31M+3.2%
Daily actives91.2K+4.2%
ARPU€46.7−6.1%
Registrations18.4K↑ 27%
Activation 21.3%↓ 9.6 pp
The insight

More signups.
Less valuable traffic.

A new partner channel accounts for 62% of registration growth, but only 17% of its signups activate.

Inside the thinking

Review financial and operational KPIs, prioritize meaningful anomalies, and investigate the data behind them. Turn the finding into a proposed action and an evidence-backed escalation for the right team. This acquisition example illustrates a broader capability—not an acquisition-only tool.

Business KPIs to improve

  • Analyst hours saved
  • Time to detect anomalies
  • Time to identify root causes
  • Time to escalation
  • Issue-resolution time
02 / AI business analystArchitecture

Less BI backlog.
Faster answers for the business.

An agentic AI analyst designed to answer business questions across 113 tables and 15 views—reducing routine query-writing and data preparation, so BI analysts can focus on deeper investigation.

Choose a question / explore the analysis
“How does revenue compare across markets?”
Relevant dataChecked queryExplained finding
CONTEXT

Revenue data, market definitions and comparison period.

CONTROLS

Read-only access, business rules and query limits.

Retrieve the relevant schema, check the query, and show the calculation behind the answer.

Agentic AIRAGIntent routingGuardrailsEvalsObservability
KPIs to measure

Analyst hours savedTime to verified answerAnswers accepted without rework

Inside the thinking

The problem

Routine business questions create repetitive query-writing and data preparation. Product, finance and operations wait for answers while BI analysts handle work that could be made self-service.

The insight

This is an on-demand investigation tool, not a daily report summary. A useful answer needs the right data, the correct business definitions and a calculation that can be inspected.

The decision

Design a natural-language analyst over 113 tables and 15 views. Route the question, retrieve relevant schema and examples, generate SQL, apply deterministic checks, then execute with read-only permissions and explain the result.

The quality loop

Evaluate against representative questions and expected results. Trace retrieval, query generation and execution; feed analyst corrections back into the evaluation set. Measure hours saved and time to a verified answer alongside correctness.

Give analysts back the routine work. Keep the evidence behind every answer.

03 / Disengagement detectionProof of concept

A potential 1–3-minute head start on disengagement.

A behavioral study of 5.1 million events from 4,544 customers, testing early warning signs before session exit—building the evidence for better-timed retention decisions.

5.1M

events
analyzed

~50%exit-zone
events detected
~15%active play
also triggered
4,544customers
analyzed
Behavioral analyticsFeature engineeringExperimentation
Next validation

Incremental retention liftNext-day returnIntervention cost

Inside the thinking

The problem

Session exits were visible after the customer had already gone. The research question was whether measurable behavioral changes appeared early enough to support a useful product decision.

The evidence

The study analyzed 5.1 million events from 4,544 customers. Pauses were roughly 3× longer and balance pressure roughly 15× higher near exit. These were observed behavioral patterns, not an LLM prediction.

The decision

Combine four observable signals in a rolling score and examine the trade-off between detection and unnecessary triggers. At the tested threshold, roughly 50% of exit-zone events were detected, while about 15% of active play also triggered.

The next test

The findings indicated a possible 1–3-minute lead time. Test whether a well-timed response creates incremental retention value, while monitoring customer safety, experience and intervention cost.

First prove the signal. Then test whether acting on it creates value.

04 / AI campaign managerConcept & architecture

Less campaign-planning time.
Better returns on retention spend.

An agentic AI campaign-manager concept bringing audience recommendations, expected returns and budget controls into a 3-step planning and approval workflow—designed to save CRM teams time and improve spending decisions.

Agentic workflowsCampaign planningML + LLMUpliftBudget guardrails
KPIs to measure

Planning hours per campaignIncremental campaign ROI7-day retention

From performance signal to reviewed campaign
Performance changeRelevant audienceRecommendation
01 / BASICSSet the goalObjective
+ audience
02 / CONFIGUREBuild the planCampaign
+ budget
03 / REVIEWCheck & approveExpected return
+ controls
Approved campaignMeasured outcome
↳ Feed results into the next recommendation ↲
Expected ROIBudget limitsManager control
Inside the thinking

The problem

CRM teams move between performance reports, audience selection, campaign planning and budget checks. Manual handoffs consume time and make it harder to assess which actions deserve investment.

The insight

The goal is not more campaigns. It is a relevant recommendation, a clear case for spending and a way to measure the incremental result.

The decision

Design a three-step workflow for campaign basics, advanced configuration and review. Use predictive models for quantitative estimates, an LLM for interpretation and composition, and deterministic rules for budgets, eligibility and responsible-use constraints.

The intended value

Reduce manual planning and improve targeting and budget decisions. Evaluate planning hours per campaign, incremental ROI and 7-day retention before treating the concept’s expected benefits as realized outcomes.

A campaign is only worth launching when the expected value justifies the spend.

05 / Workflow automationLaunched product

100,000+ work-hours saved.
More time for work that matters.

Workflow discovery identifies repetitive tasks worth automating. The launched RPA product removed manual effort across business systems, saved 100,000+ work-hours and generated $15M in new business.

Human activity → recurring patterns
Repeated workflow foundVariant made visible
Observe → Discover → Prioritize → Automate
RPA product outcomes
$15Mnew business
100K+ hrswork saved
Inside the thinking

The problem

Organizations wanted to automate, but documented processes did not always reflect how people actually worked across applications.

The insight

Structured desktop events reveal repeated sequences and workflow variants. Discovering the right opportunity can be as valuable as executing the automation.

The decision

Use behavioral pattern discovery to identify repetition and inefficiency, with people interpreting the business task. This was classical statistical learning and sequence mining, not modern LLM semantics.

The value

Led task-mining capabilities and launched an automation product that generated $15M in new business and saved 100,000+ work-hours. Workflow discovery helped reveal repetition and inefficiency; the saved hours and new business are outcomes of the launched RPA product.

Understand the work before deciding what software should take over.

06 / Customer journey & UXLaunched products

A better customer journey.
A 50% uplift in lifetime value.

Launched 2 consumer products across web and native apps and led a full UX overhaul—connecting activation, engagement and retention improvements with commercial performance, and contributing to a 50% increase in LTV.

Customer experience → commercial value
+50%

Lifetime value
increase contributed to

2consumer products
Web + nativeone connected lifecycle
ActivateEngageRetainGrow value
GrowthUXLifecycleExperimentation

Product and UX changes tied to a measurable business outcome.

Inside the thinking

The problem

A fragmented customer experience needed a joined-up product direction, rather than isolated improvements to individual screens.

The insight

Acquisition, activation, engagement, retention and monetization form one lifecycle. Design the experience and its economics together.

The decision

Launch two consumer products across web and native apps and lead a full UX/UI overhaul. Use funnel analytics and experimentation to identify friction and prioritize improvements throughout the lifecycle.

The outcome

The combined product and UX transformation contributed to a 50% increase in customer lifetime value. Connect improvements in activation and retention to the commercial result, rather than treating visual redesign as the end goal.

Better journeys should create better experiences—and better business results.

03 / How I think

Technology is
a choice.
An outcome
is the point.

A few principles that connect the product decision to the system underneath it.

Judgment before novelty.
01

Model ≠ product.

The product is the decision, action or experience the model enables. Start there, then choose the technology.

02

Probabilistic intelligence.
Deterministic boundaries.

Let AI reason where ambiguity exists. Enforce what must always be true outside the model’s discretion.

03

Build before debating.

A working flow or interactive prototype turns assumptions into something we can test. Learn what matters before scaling the investment.

04

Eval is regression
testing for AI.

Prompts and models are product behavior. Evaluate representative cases, inspect failures and feed what you learn back into the next iteration.

04 / I still build

I don’t stop
at the PRD.

From user flows and rapid mockups to working prototypes, agentic workflows and 0→1 products.

Build

Replit · Claude Code · OpenAI Codex

Design

Figma · Claude Design · UI/UX · interactive prototypes

AI systems

RAG · routing · context engineering · agentic workflows · structured outputs

Quality

Evals · observability · guardrails · human-in-the-loop

Connect

Langflow · APIs · MCP · workflow automation

I don’t claim to replace engineering or design.
I remove distance between an idea and evidence.

THE WORKBENCH / IDEA → EVIDENCE
A FLOW YOU CAN SEE AND QUESTION
Make the next
step obvious.
WORKSPACEOne task.
Next step →
01 / Relevant context
02 / Clear action
03 / Visible feedback
Design: make the interaction testable.
05 / A little about me

Product has always
been my way of
understanding systems.

I started close to the code, moved into automation and product, built companies and platforms, and found myself close to the code again.

Now, AI lets me test ideas at a speed I always wanted as a product leader. What hasn’t changed is the part I enjoy most: finding the leverage point.

15+years across product
& engineering
3patents
2innovation awards
06 / Let’s talk

Have a hard problem?
I’m interested.

Full-time product & AI leadership.
Selective fractional and advisory work, too.

Explore a leadership role Talk fractional & advisory
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