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Earned Top Rated Plus in one of the most competitive times for freelancers.
One of the contracts behind this milestone has been an AI-powered marketing automation system for a restaurant business.
The system helps manage social engagement, run campaigns, track what is working across channels, and use that performance data to improve the next actions automatically.
The part I like most is that it is not built around constantly writing new prompts or calling myself a “Prompt Engineer.”
The AI agent is designed to work with real campaign data, learn from what performs better, and adjust workflows over time to improve engagement and conversions.
For me, Top Rated Plus is not just a badge. It reflects building systems that actually work in production and create measurable value.
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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Pipeline view of an agentic fundraising prototype for an AI client built in Framer. Rendered in Ultramock.
I had Claude build out ~800 rows (~50 columns) of dummy contact data in supabase with varying degrees of lead enrichment and match scoring to simulate a live DB connection.
Really enjoying using Claude x Framer. Its truly a superpower for building simulation engines like this.
800 rows of dummy data for a proto, madness
1 week ago I posted that I hit top 5% creatives. It seems like it snowballs.
Ask old clients for reviews, and study what makes professionals in your field worth more.
Good job
My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.