Case Studies & Insights

Technical breakdowns of real projects β€” architecture decisions, engineering trade-offs, and business impact. Published on LinkedIn.

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Agent SEO Β· A2A Protocol Β· Agent Passport 5 min read

Agent SEO & A2A Protocol: Why Your Business Needs an Agent Passport

Autonomous AI buyer agents now search, evaluate, and qualify service providers directly. Learn how the Linux Foundation A2A standard and agent-card.json make your business discoverable.

Agent SEO A2A Protocol agent-card.json Linux Foundation GCP Cloud Run
AI Chatbot Β· Customer Support Automation Β· Google ADK 6 min read

Business Process Automation with RAG AI Agents: The Future of B2B Lead Generation

Turn company websites and PDF catalogs into instant vector search engines and automate customer inquiries 24/7 on Google Cloud Run.

Business Process Automation Google ADK Cloud Run RAG Agent
B2B Automation Β· Google Maps MCP Β· Lead Enrichment 5 min read

Beyond Web Scraping: Automated B2B Lead Generation via Google Maps MCP

Why I ditched external scrapers (Apify) for direct Google Maps MCP β€” delivering direct company website URLs, 3-minute lead enrichment, Impressum email extraction, and zero scraper subscription fees.

Google Maps MCP B2B Lead Gen Model Context Protocol Impressum Extraction Supabase
Cloud Automation Β· DevOps via AI Β· Infrastructure 5 min read

Cloud Infrastructure on Autopilot: Automating Google Cloud via AI Chat

Cut DevOps costs by 4x. Manage Google Cloud Console, inspect server logs, and deploy microservices in natural language using Model Context Protocol (MCP) without a full-time cloud engineer.

Google Cloud Model Context Protocol Cloud Run DevOps via AI GCP MCP
AI Engineering Β· Reliable Workflows Β· Operations 6 min read

Building Reliable AI Employees: How to Guarantee AI Agents Follow Company Workflows

Prevent AI hallucinations and missed instructions. How to use the trigger table pattern to ensure AI agents execute company standard operating procedures (SOPs) with 100% reliability.

Claude Code Cursor Prompt Engineering AI Agents Open Source
Market Research Β· Python Automation Β· Lead Gen 5 min read

Automated Demand Research: Analyzing Market Keyword Volume Without Marketing Agencies

Stop paying $150+/mo for SEO tool subscriptions. Query Google's official keyword database directly via python API to uncover profitable customer search terms in Germany instantly.

Google Ads API Python Keyword Research SEO Automation Open Source
GEO Β· AI Search Optimization Β· B2B Visibility 8 min read

Why Your Website is Invisible to ChatGPT & Perplexity: Generative Engine Optimization (GEO)

You rank on Google but AI search engines skip your business. How to make ChatGPT, Claude, and Perplexity recommend your products and services using SSR, llms.txt, and AI readiness audits.

GEO AI SEO llms.txt SSR Website Audit
Full-Stack Β· AI Music Β· SaaS 5 min read

AI Music SaaS: Personalized Songs in Polish β€” hitdlaciebie.pl

Built a full-stack AI music SaaS for a client in two weeks with zero prior experience in AI music generation. Two-model lyrics pipeline (Gemini 2.5 Flash + Claude Sonnet 4.6), four music APIs tested, Suno V5.5 shipped. Real problems: SSRF, duplicate webhooks, Shopify test mode.

Next.js 14 TypeScript Suno V5.5 Shopify PostgreSQL
RAG Β· AI Agent Β· E-commerce 7 min read

Web Sales Assistant for E-commerce: da-vinchi.pl

A full-stack AI Sales Assistant for a Polish wallpaper store. LangGraph ReAct agent with 5 tools, Hybrid RAG (SQL + pgvector), ETL pipelines, buyer-scenario tests, and a web chat widget β€” production-grade, not a demo.

Python LangGraph FastAPI Supabase pgvector LangSmith
RAG Β· Architecture Β· Production Case Study 6 min read

How to Build a Hybrid RAG Sales Agent β€” Zero Price Hallucinations

A technical deep-dive into why pure vector search fails for e-commerce, the SQL + pgvector split that fixes it, and the LangGraph ReAct pattern that cut API costs by ~90% while eliminating hallucinated prices.

Python LangGraph FastAPI Supabase pgvector Gemini
Automation Β· LLM Pipeline 6 min read

Invoice Automation Pipeline: IMAP β†’ PDF β†’ Gemini β†’ Drive + Supabase

Invoices from multiple suppliers arrive as PDFs by email in different formats. Manual routine: find, classify, extract, file, enter into accounting. Now it runs on a schedule β€” automatically, end-to-end.

TypeScript Gemini 2.5 Flash GitHub Actions Supabase Google Drive
Full-Stack Β· Multimodal AI 4 min read

AI Image Composer: Identity Replacement in 30 Seconds

A Polish creative studio was burning hours on trial-and-error prompting for personalized AI paintings. I built a production web app in one day using React 19 and Gemini that turns it into a 30-second workflow.

React 19 TypeScript Gemini Canvas API Railway
Automation Β· B2B Β· Lead Gen 6 min read

B2B Lead Pipeline: 107 German Companies, $4.80 Cost, 22 Min

Built an automated B2B lead generation pipeline for the German market: Google Maps β†’ Impressum scraping β†’ Gemini enrichment β†’ Supabase β†’ CSV. 107 companies, 54 emails, Β§7 UWG compliant β€” all in 22 minutes for under $5.

Python Apify Tavily Gemini Supabase
Automation Β· AI Image Β· Retail 5 min read

Wallpaper Visualization Automation: AI Replaces Photoshop

Automated the manual Photoshop workflow for a wallpaper retailer: geometric transformation vs. AI generation β€” the architecture decision that matters. What works for TapetenhΓ€ndler, KΓΌchenstudios, MΓΆbelhΓ€user, and any German retail shop with room visualization needs.

Python FastAPI Gemini React Supabase
RAG Β· Architecture 4 min read

From Data Mirroring to Knowledge Synthesis: RAG Architecture Evolution

My first mistake was vectorizing raw PDFs. The result was a "calculator without a brain." Here is the architectural shift β€” Parent Document Retrieval, LCEL chains, and two-layer storage β€” that actually eliminated hallucinations.

LangChain LCEL pgvector Supabase RAG patterns
Automation Β· QA 5 min read

QA Automation for RAG Systems: Testing AI Pipelines

Building a RAG pipeline is one thing. Knowing it works reliably under edge cases is another. I built a testing framework with buyer-scenario integration tests, 98% data optimisation, and GitHub Actions automation.

Python LangGraph pytest GitHub Actions Supabase
RAG Β· AI Agents 5 min read

Vibe Coding a Hybrid RAG Agent: Supabase + Gemini File Search

A 57 MB PDF catalog, 55K+ tokens, ~$45/mo in hot-cache costs. I engineered a Hybrid RAG Telegram Sales Agent combining Supabase pgvector for hard data with Gemini File Search for semantic context β€” sub-second responses, near-zero API cost.

Hybrid RAG Supabase pgvector Telegram Gemini
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Common thread across all these projects

The real engineering is not in the LLM call. It is in what surrounds it: controlled ingestion, deterministic fallbacks, idempotent pipelines, and structured storage. That combination is what separates production automation from demos.

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