From Lviv to Chicago: Kateryna Babiy on building AI healthcare at scale and breaking into US tech

"Don't rush, relax a bit." How a Lviv engineer built AI-enabled healthcare systems in the US - and what product teams can copy

Kateryna Babiy is a 36-year-old full-stack and systems engineer from Lviv working in Chicago's healthcare insurance ecosystem. Her edge: solving hard architecture problems that speed up delivery and make products stable at national scale.

She grew from frontend to full-stack to architecture by saying yes to unfamiliar work - backend, cloud, DevOps - and learning fast. Today she operates across frontend, backend, and platform decisions, with a clear bias for measurable outcomes.

Why product leaders should care

• She turned a monolith into a microfrontend platform that cut release friction and boosted feature velocity.
• She rolled out AI-assisted development, reducing cycle time 20-30% and post-release bugs by 15%.
• She institutionalized standards and mentoring so new teams ramp fast without dragging seniors into rework.

From monolith drag to microfrontend speed

On joining a national health insurance product, Kateryna found a single monolith serving millions of users. Releases collided, integrations were brittle, and scaling was slow. She proposed a switch to microfrontends - independent modules owned by autonomous teams - and led the transition.

Impact was clear: teams shipped in parallel, conflicts dropped, and integrations became predictable. Release cycles fell from weeks to days, key module load speed improved by over 40%, and the platform scaled to new markets faster. One new feature even won a company award for its user impact.

If you're evaluating this approach, start with a vertical slice that covers routing, shared design tokens, and a single critical domain. Align on versioning, CI/CD, and ownership boundaries before you scale. For more background, see the microfrontends primer on martinfowler.com.

Product takeaways

• Define team boundaries by business capability, not by layers. Autonomy is the goal.
• Standardize contracts early: shared UI kits, event schemas, and API gateways.
• Measure the right lagging and leading indicators: time to release, defect escape rate, p95/p99 load times.

AI-assisted development that actually moves the needle

Kateryna's team went from "autocomplete only" to full AI-assisted coding with GitHub Copilot in a few months. Seniors still own architecture and quality, but AI removes grunt work: templates, tests, refactors, and quick spikes.

Results: 20-30% faster feature development and 15% fewer bugs after release. AI didn't replace judgment; it amplified it. Policies and reviews kept the bar high while throughput increased. If you're exploring this, start with clear usage norms and pair AI with code reviews and automated checks. Tool info: GitHub Copilot.

How to roll out AI without chaos

• Define "green zones" for AI: scaffolding, unit tests, boilerplate, data transforms. Keep critical logic human-owned.
• Add guardrails: license scanning, prompt hygiene, and mandatory reviews on sensitive modules.
• Track metrics: cycle time per story, rework rate, coverage, and escaped defects.
• Upskill continuously. If your team needs structured paths by role, see curated options for product and engineering at Complete AI Training.

Architecture decisions that deliver business value

Kateryna focuses on end-to-end outcomes: faster access to information, stable services, secure data, and less friction for users and operations. She targets changes that create compounding effects: platform standards, reusable modules, and shared knowledge.

One example: reducing critical page load times from 5 seconds to under 2 seconds. That single shift increased daily throughput without extra infrastructure and improved satisfaction across patients, providers, and admins.

What product should demand from engineering

• Every initiative tied to a measurable goal: time-to-release, performance p95, error budgets, or activation rates.
• Architecture that accelerates independent delivery (not just "clean code").
• Shared playbooks: coding standards, ADRs, and onboarding packs to compress ramp time.

Mentoring as a force multiplier

Kateryna mentors through programs like Women Go Tech, HackYeah, and UIC Engineering Expo, and inside her company. It takes longer at first - a one-hour task can become four - but the payoff is exponential once mentees own complex features and lead small teams.

Her approach is simple: pair on real work, push for ownership, and review outcomes against clear standards. The result is a bench that raises team quality without bottlenecking seniors.

Hackathons as product discovery

She treats hackathons as a fast lab for tech and ideas. Two current projects: QuitQly (quit smoking help) and Snovyda (record and explore dreams). The speed and constraints reveal what's worth maturing into real features or products.

For product teams, this is a low-cost way to test assumptions, meet users early, and recruit motivated builders. Keep a tight loop: a weekend prototype, a week of user validation, and a go/no-go decision.

Relocation, scale, and the US healthcare context

Adapting to US healthcare meant higher stakes: more teams, stricter security, and unfamiliar insurance workflows. Kateryna leaned on broad technical range, fast learning, and clear communication to integrate quickly.

The feedback loop helped: early wins, visible impact, and proactive proposals. That built trust and unlocked larger initiatives.

Your product playbook, distilled

• Break work by business capability. Give each team independent deploy, data contracts, and UI ownership.
• Codify standards early: design tokens, component libraries, API schemas, logging, and SLOs.
• Adopt AI where it compounds: scaffolding, tests, refactors, analytics queries. Keep critical paths human-led.
• Instrument everything. Track cycle time, release frequency, defect rates, and user-perceived performance.
• Invest in mentoring. It's the fastest route to sustainable velocity and fewer single-points-of-failure.
• Use hackathons for discovery. Prototype fast, validate faster, kill weak bets early.

Final note

Kateryna sums it up well: "I'm driven by building systems that change how teams work and how people live." If your goal is the same, aim for decisions that free teams to ship faster, improve user outcomes, and stand up to scale.