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
Protect your business and customer data when using AI Voice Agent
A dental clinic needed a HIPAA-compliant AI voice agent to
automate calls and protect patient data. I built a local voice system with
on-premise STT, ensuring no audio left the clinic. Each booking synced to the
CRM and doctors’ Google Calendars via secure OAuth, with all data encrypted
(AES-256). I added anonymization, 30-day auto-deletion, and audit logs to meet HIPAA. The clinic reached 100% compliance, cut booking errors, reduced staff workload by 70%, and ensured full data privacy and security.
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
Related posts
Title: AI agents are everywhere in 2026. The ones that survive production do these 4 things.
Everyone's shipping AI agents right now. Very few ship ones that hold up in production.
After building agents with founders and product teams, these are the four shifts I'm seeing in the market this month:
Agents are leaving the chatbox.
Forecasts point to roughly 40% of enterprise apps embedding task-specific agents by the end of 2026, up from under 5% in 2025. The UX that wins shows its reasoning, asks for approval at key moments, and uses real interfaces instead of chat bubbles.
Memory is the new moat.
Stateless bots reset every session. Agents with a memory layer compound context: brand voice, past results, goals, constraints. It's the idea behind the strategy-memory layer I built into an AI marketing assistant.
Build vs. buy is flipping.
McKinsey found nearly a third of surveyed organizations passed on buying at least one software product because coding agents could build it in-house. Fast to prototype isn't the same as safe to run, and architecture decides which one you get.
Guardrails are now a feature.
Many enterprise agents ship without meaningful security controls. Least-privilege access, audit trails, approval gates, and cost limits are what turn a demo into something a company can trust.
I'm a full-stack AI engineer (React, Next.js, Node, Rails, PostgreSQL, OpenAI) who works directly with founders and technical teams from architecture to launch. If you're building an agent product and want a technical partner rather than a ticket-closer, I'd love to hear what you're working on.
Where is your agent getting stuck on the way to production: UX, memory, or trust?
#AIAgents #LLM #FullStack #NextJS #GenerativeAI #SaaS
I analyzed 3M+ job postings across 161 countries and 93K companies to identify hiring trends, in-demand roles, work-mode patterns, and salary insights.
I used Python for data cleaning and preprocessing, SQL for analysis, and Power BI to build an interactive dashboard.
Key areas analyzed:
• Global hiring demand
• Top job roles by demand
• Remote vs. onsite/hybrid work patterns
• Average salary trends
• Monthly hiring trends
This project helped me turn a large dataset into clear, actionable insights for understanding the global job market.
AI-powered voice receptionist platform for clinics that can handle patient calls, answer questions from a clinic knowledge base, qualify inquiries, capture leads, and manage appointment bookings. The platform includes custom AI agent configuration, real/demo calling, calendar workflows, call history, lead tracking, human handoffs, and call-quality analytics—all managed through a multi-workspace dashboard.
