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Most businesses treat AI like a toy instead of an operational department.
Until your AI workflows are directly tied to cutting operational overhead or lifting conversion rates,
you're just playing with expensive software. Building robust automation pipelines requires deep
attention to detail and continuous iteration.
What is your biggest bottleneck when deploying automated AI workflows for clients ?
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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GoHighLevel Lead Follow-Up Workflow for an Oregon Construction Company
I built this GoHighLevel Connect Call workflow to help an Oregon construction company manage new inquiries from Networx, Google Ads, and other lead sources.
The workflow maps the different paths a lead can take, including successful contact, missed calls, continued follow-up, and human handoff. It also keeps the relevant tasks, contact updates, and next steps organized inside the CRM.
The goal was to create a consistent lead-management process without relying on scattered notes or manual reminders.
Good automation does not replace the sales conversation. It makes sure the right conversation happens at the right time.
I help service businesses with GoHighLevel workflow design, CRM organization, lead routing, follow-up automation, and system audits.
For a construction company specifically, that missed call path is probably doing more revenue recovery than any other branch. guys on a job site are not picking up mid pour so leads default to voicemail & just go cold without something automatically pulling them back in
Many businesses want to build AI agents and automated workflows, but they face challenges with unreliable outputs, complex AI logic, poor data validation, and systems that are difficult to maintain in production. Without structured workflows and strong type safety, AI applications can become unpredictable, expensive, and difficult to scale.
I will build type-safe, production-ready AI workflows using Pydantic AI, AutoGen, and modern agent development frameworks. I create reliable AI systems with structured outputs, intelligent agent collaboration, tool integrations, validation layers, and scalable automation pipelines.
I use powerful AI development tools including Pydantic AI, AutoGen, Agno, CrewAI, LangChain, LlamaIndex, Semantic Kernel, Haystack, OpenAI API, and Anthropic Claude API to design and deploy advanced AI workflows.
My process includes AI architecture design, agent workflow development, structured data validation, API integrations, tool calling, memory management, testing, debugging, and deployment. Whether you need AI agents, multi-agent systems, RAG pipelines, enterprise automation, or custom LLM workflows, I will help you build reliable AI solutions that are secure, maintainable, and ready for real-world production use.
Marketing teams don't need more AI tools, they need one that remembers their strategy. So I built them a teammate instead.
Designed and shipped Ella AI (https://www.atomicelevator.com/) end-to-end: an AI-powered marketing assistant that helps agencies and teams plan, execute, and refine strategy through intelligent automation — not just another content generator that forgets everything between sessions.
Stack: Next.js, Nest.js, PostgreSQL, Supabase, OpenAI LLM, OpenRouter, TanStack Query, Stripe, API Deck, WorkOS FGA, HubSpot, Sentry
Built the whole product from the ground up — architecture, backend, integrations, billing, the works.
Happy to talk through how the strategy-memory layer or the integration stack came together if you're curious always down to nerd out on agent architecture.
