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This section shows the AI response and notification layer of the workflow. Leads are prioritized, routed to Slack, converted into professional customer replies, drafted in Gmail, and logged automatically.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Built JobTracker AI — because a spreadsheet shouldn't be your job search strategy. 📋➡️🤖
Paste a JD, get an instant fit score against your resume, and see exactly where the gaps are before you hit apply. No more guessing why the callback never came.
Full pipeline tracking (Applied → Interview → Offer), automated follow-up reminders via n8n, and AI-powered parsing that actually reads the JD so you don't have to read it five times.
Built end-to-end — architecture, AI integration, automation, deployed and working. Not a prototype that dies after the demo.
Open to building this (or something like it) for you. DMs open.
Amazing work
FlowPilot is a fully interactive SaaS demo showcasing an AI-powered business operations agent and its end-to-end workflow from client request to paid project.
hi looks cool....btw did we receieve credits for the challenge?
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.
