AI AGENTS THAT EXECUTE
WORKFLOWS END-TO-END
I build AI agent systems, RAG pipelines, marketing automation, and operational infrastructure. Autonomous systems that execute workflows without human intervention, running on real production infrastructure at real scale.
What Changes When AI Is Built Into Operations
Four transformations shipped to production and measured.
No AI vision. Manual ops across the board.
4-agent ecosystem handling research, outreach, content and intel.
No CAC tracking. Zero visibility into channel performance.
UTM + GA4 + Brevo attribution. Real CAC per channel, per campaign.
Manual content. Inconsistent output, slow cycles.
2,400+ entities scraped, 1,828 enriched, email generation automated.
Growth was 'try stuff and see what sticks'.
600% organic traffic, 8.1% CTR, 121% spot volume lift.
Source · Production engagements with KuCoin, EU fintech, Entangle, and stealth advisory clients
Production AI Infrastructure
AI Agent Systems
Autonomous agents for research, outreach, content, competitive intel. Multi-step workflows without prompting.
RAG Pipelines
Vector DBs, semantic search, source-cited answers grounded in your data. Every figure carries its source and a freshness date.
AI-Powered Growth
Programmatic SEO, content automation, lead enrichment, full attribution. Growth ops run by agents.
AI Ops Infrastructure
Multi-provider orchestration, cost gating, caching, monitoring, failover. The boring plumbing that keeps AI cheap.
Marketing Automation
Email sequences, lead scoring, CRM integration, campaign orchestration. AI writes, humans approve.
Competitive Intelligence
Automated scraping, entity enrichment, regulatory mapping — monitored continuously.
How AI Systems Work In Production
Four phases. Real infrastructure. Real numbers from the EU fintech pipeline currently running in production.
Data Collection
Scraping, APIs, entity discovery across multiple sources with rate limiting, proxies, and error handling built in from day one.
Processing & Enrichment
Entity resolution, contact discovery, dedup, validation. Multi-provider enrichment with fallback and a match-rate gate.
AI Generation
Multi-provider orchestration (Claude, Gemini, OpenAI) with cost gating, caching, and quality validation per request.
Deployment & Monitoring
Production on Hetzner VPS, Docker containers, automated BD sequences, uptime monitoring, alerting on drift and failure.
AI Systems At Scale
Three production engagements. Real numbers from shipped systems.
Growth Automation Pipeline
Scraped EU VASP/CASP registries across 27 member states, enriched 1,828 entities, built a BD pipeline: registry → Gemini email → Brevo sequences.
Cross-Chain Protocol Growth
600% organic traffic growth for a cross-chain protocol via AI-assisted content pipeline and SEO architecture. 8.1% CTR — double the industry average.
Layer-1 Blockchain Launch
Token launch GTM for a quantum-resistant L1. Republic fundraise strategy, investor narrative, raise closed in under a month.
Source · EU VASP/CASP registries (27 member states), Brevo, Republic raise data
What Running AI In Production Looks Like
Multi-provider orchestration with automatic fallback. No single vendor can break the pipeline.
API calls / month
Claude · Gemini · OpenAI
Cost per enriched contact
€200 spend for 1,828 contacts
Infrastructure uptime
Hetzner VPS · Docker · health checks
Cost reduction
vs manual enrichment (€3K → €200)
Source · Live engagement metrics, EU fintech & stealth advisory
Questions Worth Answering
04 questions
How are AI agents different from a ChatGPT subscription?
An agent is an autonomous system that executes multi-step workflows without prompting. I build agents that scrape regulatory databases, enrich contact records, generate personalised outreach, and send via Brevo — running on their own schedule, no human in the loop. ChatGPT is the underlying model. The agent is the system that wraps it in a job.
What's a RAG pipeline and why do I need one?
Retrieval-Augmented Generation searches your own documents first, then answers from what it found, with a citation on each claim. I build these on pgvector with source attribution, and every figure carries the class of source it came from and the date it was last checked. That last part is the difference between an answer you can act on and one you have to go verify yourself.
How much does AI infrastructure cost to build?
Builds run $15K–$50K depending on complexity. A basic RAG pipeline sits at the lower end. A multi-agent system with scraping, enrichment, generation, and deployment orchestration is at the higher end. Ongoing costs are API usage (Gemini, Claude, OpenAI) plus hosting. Every build I ship includes cost gating so you approve spend before it happens.
Can AI really replace growth marketers?
AI handles the repetitive layer: scraping leads, enriching contacts, drafting emails, A/B testing subject lines, reporting attribution. Marketers do strategy, positioning, creative direction, optimisation. On the EU fintech engagement, enriching 1,828 contacts manually would have taken 60 hours at €50/hr (€3K). Automated, it cost €200 in API calls — a 15× reduction at the same throughput.
Question not listed? [email protected]
Source · Real client questions, EU fintech & advisory engagements
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AI INFRASTRUCTURE
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