Google Ads MCP: An AI Agent That Can't Burn Your Budget
Google's official Google Ads MCP server is read-only. To let an AI agent change bids safely, it needs your own backend with business rules. Real account: CPC 0.82 to 0.47 EUR.
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GDPR-Compliant AI Agents: How to Protect Customer Data & IBAN with DLP Proxies
Automating customer operations with AI without violating European data protection laws. How Cloud DLP proxies de-identify PII and IBAN in real time before prompts hit LLMs, backed by Zero-Trust VPC perimeters.
Google ADK Agent Skills: How to Build Production-Grade Skills for Multi-Agent Systems
Stop writing 5,000-line monolithic system prompts. Architectural guide to 3-tier Agent Skills, Progressive Disclosure, Two-Layer Shields, in-loop self-correction, and Google Cloud Skill Registry.
Agentic AI & WebMCP: Why Autonomous AI Agents Are Replacing Classical SEO
How the new Google Cloud WebMCP standard and Model Context Protocol turn websites into self-describing, tool-executable APIs for autonomous AI buyer agents.
Stop Guessing Where Your Ad Budget Goes: One Control Panel for Every Channel
Where advertising budget actually leaks, five questions that test your own account in a minute, and what changes when every channel runs from one place with the checks built into the tooling.
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.
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.
Beyond Web Scraping: Automated B2B Lead Generation via Google Maps MCP
Why I ditched external scrapers (Apify) for direct Google Maps MCP — delivering direct company website URLs, 3-minute lead enrichment, Impressum email extraction, and zero scraper subscription fees.
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.
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.
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.
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.
AI Music SaaS: Personalized Songs in Polish — 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.
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 — production-grade, not a demo.
How to Build a Hybrid RAG Sales Agent — 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.
Invoice Automation Pipeline: IMAP → PDF → Gemini → 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 — automatically, end-to-end.
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.
B2B Lead Pipeline: 107 German Companies, $4.80 Cost, 22 Min
Built an automated B2B lead generation pipeline for the German market: Google Maps → Impressum scraping → Gemini enrichment → Supabase → CSV. 107 companies, 54 emails, §7 UWG compliant — all in 22 minutes for under $5.
Wallpaper Visualization Automation: AI Replaces Photoshop
Automated the manual Photoshop workflow for a wallpaper retailer: geometric transformation vs. AI generation — the architecture decision that matters. What works for Tapetenhändler, Küchenstudios, Möbelhäuser, and any German retail shop with room visualization needs.
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 — Parent Document Retrieval, LCEL chains, and two-layer storage — that actually eliminated hallucinations.
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.
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 — sub-second responses, near-zero API cost.
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.