Seven production pipelines deployed n8n + Python + AI agents + browser automation — handling everything from e-commerce product factories to B2B lead gen. Every system below has already processed thousands of real operations.
For e-commerce & digital-product sellers who can't keep up with listing volume
The problem it kills: manual listing takes 20–40 min per product. This engine takes a folder of products and turns them into complete, SEO-optimised, image-and-video-complete marketplace drafts — unattended, overnight, at 5 QPS-safe pacing.
The engineering moat: marketplace APIs punish naive automators — every write throttles after ~4 calls and new listings lock for minutes. The pipeline absorbs all of it: per-image retry loops, timing waits between writes, fingerprint dedupe, and a self-recovery script that automatically heals failed batches.
Client result: "drop products in a folder → wake up to live-ready listings."
For agencies & B2B sellers tired of buying stale, unqualified lead lists
The problem it kills: purchased prospect lists are cold, deduped-once, and nobody qualifies them. This engine builds lists from scratch — generates its own search query matrix, extracts and dedupes companies, reads each company's site like a marketer would, finds the actual decision-maker, and verifies their LinkedIn presence before a single message is sent.
The engineering moat: full relational schema (Pydantic models + SQLAlchemy), every company scored on marketing maturity and growth signals, then tiered — so outreach teams only touch whales first.
Client result: a qualified, tiered, deduped pipeline instead of a CSV of strangers.
For teams where 30% of "sent" emails are actually dead addresses
The problem it kills: cold campaigns bounce, land in spam, and campaigns hammer the same lead twice. Every address passes a multi-provider verification waterfall first; every send is deduped against a touched-leads ledger; AI drafts get humanisation passes so they don't read like a robot wrote them.
The engineering moat: SMTP failover across multiple mail providers, scheduling logic with warmup protection, and a live HTML dashboard showing campaign status in real time.
Client result: campaigns that protect the domain reputation and only talk to verified humans.
For founder-led B2B: connections in, qualified conversations out
The problem it kills: LinkedIn outreach is done manually, inconsistently, and with zero memory of who was contacted. This OS imports your entire network, scores it against the ICP, and drafts platform-appropriate sequences (LinkedIn, Instagram, cold email variants) through an AI "messaging council" — with every touch logged.
The engineering moat: a real CRM underneath — 5 SQLite tables, qualification logic, outreach logging with CSV audit trails — not a spreadsheet and vibes.
Client result: nobody gets double-contacted, every message fits the segment, follow-ups never slip.
One content library → every platform, on schedule, every day
The problem it kills: sellers cross-post manually — or not at all. This machine maintains per-account posting profiles, generates platform-native SEO captions, schedules and posts across Pinterest (multi-account), TikTok and Facebook, and keeps a per-account "already posted" ledger so the same asset never double-fires.
The engineering moat: three dedicated browser profiles + n8n daily cron workflows means posting runs even when nobody's at the machine — plus a batch orchestrator mode for backfilling entire product queues.
Client result: daily presence on 3 platforms with zero hands on keyboard.
From vague brief to commercially-differentiated image prompts, at 1,000-per-batch scale
The problem it kills: AI-generated art all looks the same, and generating it manually doesn't scale. This is a creative intelligence layer: six specialised agents (Research → Reference Curator → Art Director → Prompt Architect → Critic) turn fuzzy briefs into researched, differentiated prompts — plus a full prompt knowledge base (palettes, lighting, art movements, model behaviour) that compounds over time.
The engineering moat: headless browser engines drive ChatGPT and Gemini Studio through real profiles — uploading 100-prompt batches, verifying each download, and running 4K finishing passes automatically.Client result: a visual product factory, not a lottery ticket.
Know exactly where rivals are strong — before you launch
The problem it kills: "competitor research" is a manual spreadsheet job done twice a year. This engine continuously scrapes competitor listings via a read-only API, audits your own inventory against theirs for gaps and overlap, and synthesises ranked, actionable reports — so positioning decisions come from live data, not gut feel.
Same architecture, any domain: e-commerce, lead gen, social, content, auditing. That's why these re-deploy for a new client in days, not months.
📩 Want one of these running in your business? → Let's talk