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
Back to feedPost
I built an AI recruiter that actually does the interview.
Most AI recruiting demos stop at “the AI can talk.”
I wanted to build something more useful.
The system can:
→ Call a candidate
→ Conduct a structured voice interview
→ Ask follow-up questions
→ Record the conversation
→ Generate a transcript
→ Summarize the candidate's answers
→ Save everything back to the CRM
So instead of a recruiter spending hours doing the same initial screening calls, the AI handles the first stage automatically.
The interesting part isn't the AI voice.
It's connecting the entire process:
Candidate → AI Interview → Recording → Transcript → AI Summary → CRM → Recruiter
That's where AI automation starts becoming an actual business system instead of just another chatbot.
Built with GoHighLevel + AI voice + automation + API integrations.
More builds like this coming.
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
Trending
retrofuturism
Share your favorite designs from the past exploring the distant future. Or your own take on the aesthetic 🚀
halftone
Dots. Texture. Grain. Let's see your designs using the halftone technique.
Midjourney
Creatives are showing off what they're making with Midjourney.
colorgrade
Video editors sound off: How do you enhance the mood of your video in color grading?
Related posts
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.
Designed a modern AI Voice SaaS Dashboard.
Clean and intelligent UI with real-time voice analytics, call insights, conversation tracking, AI-powered features, and performance metrics for smarter customer communication.
Amazing work
WIP! Designing a hero section for an AI automation brand.
Nothing final yet — just testing background visuals.
Which one do you like more — A or B?
9 voted
60%
6 voted
40%
15 votes
Closed
