Available for new projects

I build AI agents that do real work inside your product.

Not a chat widget. Voice and natural-language actions that create, update, and query real application state - with confirmations, guardrails, and the production infrastructure to run them safely.

Lenka Kadlec · Applied AI Product Engineer · Ex-Microsoft

Most teams are stuck at “we added a chat widget.” I move them to “users can actually do things through AI.”

The easy 20% - model calls, streaming, a chat box - is solved. The hard part is everything around it: getting an agent to trigger real state changes reliably, handling the failure modes, confirming before it acts, and keeping cost-per-action economically viable under real input.

That's the part I've actually shipped - and the full-stack depth to back it: auth, multi-tenancy, billing, and the infrastructure that keeps AI features from silently breaking in production.

Process

How I work with clients

Scoped, paid, and reassessed in stages. No open-ended retainers, no runaway scope.

01Paid engagement

Idea & AI Vetting

Before any code, I assess whether your AI use case is viable - where an agent genuinely adds value versus where it's theatre - what the MVP needs, and what it'll cost per action. You'll know if it's worth building.

02Paid engagement

Technical Roadmap

Architecture, stack, agent design, timeline, cost estimate, and risk areas - delivered as a written document you can take anywhere. Most clients stay.

03

Implementation

4-week packages with clear deliverables and checkpoints. Both sides reassess and recommit at the end of each package.

Services

What I build

AI that takes action is where I add the most value - backed by the full-stack engineering to ship it for real.

Agentic AI & In-App Actions

LLM tool calling and structured outputs that trigger real actions in your product - create, update, query, navigate - through natural language or voice. Human-in-the-loop confirmations, error handling, and guardrails so the agent acts safely, not unpredictably. This is the core of what I do.

RAG & Retrieval Systems

Retrieval-augmented generation grounded in your data - with attention to chunking, source hierarchy, and keeping answers honest instead of hallucinated.

AI Workflow Automation

Agentic pipelines that replace manual steps: document processing, intelligent routing, multi-step automations with cost modelling built in.

Full-Stack SaaS Development

End-to-end products: auth, multi-tenancy, billing, RBAC, real-time features - the production layer your AI features need underneath them.

Next.js / React Applications

Fast, accessible, production-ready web apps with App Router, RSC, and Tailwind. My primary stack, delivered to a high bar.

APIs, Infra & Hardening

REST/GraphQL APIs on Node.js, Prisma, PostgreSQL. Redis caching, rate limiting, audit logging, OWASP practices, and performance tuning baked in.

Selected work

What I've shipped

Personal products and professional highlights.

SoundAgent
AI agent · voice-driven SaaS

A production-grade multi-tenant project-management platform with an AI assistant that triggers real in-app actions by voice - create projects, assign tasks, and query status through natural language, with confirmations and real state mutations rather than a chat wrapper. Real-time collaboration, role-based access, and subscription billing built in. Currently in beta, being hardened for release.

Anthropic ClaudeVoice actionsNext.js 16TypeScriptPrismaSupabaseLemon SqueezyRBAC
Landing page ↗
Microsoft Teams
Messaging · millions of users

Shipped production features for Microsoft Teams messaging, used by millions worldwide. Led cross-team frontend delivery as primary engineer - coordinating rollout across multiple partner teams and working directly with PMs and principal engineers on scope, sequencing, and risk.

ReactTypeScriptCross-team leadershipLarge-scale frontend
Microsoft · 2024–2025
Video Verifier
AI fact-checking · cost-modelled pipeline

A full-stack app that turns any YouTube video into per-minute summaries, key points, and a “chat with this video” experience. Pulls the transcript, summarizes with Claude, extracts claims, and verifies each against web sources. Built end to end: durable background workflows, a multi-runtime architecture, streaming chat with prompt caching, and per-job cost tracking that surfaces real unit economics before you scale.

Anthropic ClaudeNext.js 16TypeScriptInngestDrizzleNeon PostgresGroq WhisperCloudflare R2

More work

RecipePeers
AI meal planning · PWA

A community recipe platform with AI-generated meal-plan population - users describe their preferences and an AI assistant fills their weekly plan. Built with Redis caching, Cloudflare edge delivery, S3 image storage, and a PWA shell for mobile. Full auth, rate limiting, and E2E test coverage.

Anthropic ClaudeNext.jsTypeScriptRedisCloudflarePostgreSQLAWS S3PWA
View project ↗
Contact

Let's build something that does real work

Open to new projects, collaborations, and conversations. If you've got an AI feature stuck at the chat-widget stage - or a product to build from scratch - reach out.