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AI Customer Support Assistant — an AI-powered support system that uses a knowledge base to answer customer questions, handle requests across multiple channels, store structured data, and escalate cases to a human when needed.
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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Just finished building a Salon Booking Management System!
The goal was simple: make appointment management easier for salon owners and reduce the manual work involved in handling bookings and reminders.
The system connects the complete workflow in one place:
1 Customer makes a booking
2 Booking gets confirmed
3 Owner sees it on the dashboard
4 Upcoming appointments are organized clearly
5 Reminders help the owner stay on top of appointments
One of the key things I focused on was making the experience simple, clean, and easy to understand, while keeping the workflow practical for a real business.
This project helped me think beyond just building screens — and focus more on the actual business problem, user flow, and overall product experience.
Excited to keep improving it and turn it into an even more polished product.
#BuildInPublic #SaaS #WebApp #BookingSystem #SalonManagement #ProductDesign #MVP #NoCode #Entrepreneurship
Good work
Marlowe Mobile Detailing
Solo-owner mobile car detailer, Austin, TX (serving Round Rock, Cedar Park, and Lakeway)
Marlowe Mobile Detailing was losing about 1 in 5 bookings to no-shows and running behind on nearly every job. This build replaces a vague "can you detail my car?" text with a fully scoped, weather-aware booking flow that sets an accurate time and price upfront, captures access instructions before he arrives, and automates rain-day rescheduling so he never has to chase a customer again.
He works alone and travels to the customer. Rain cancels outdoor jobs. Customers under-describe how dirty their cars are, so jobs run long. About 20% of bookings were no-shows because people forgot he was coming to them.
This project focused on building and testing a practical AI security assessment lab for evaluating LLM defenses against prompt injection and jailbreak attacks.
I integrated Spikee by Reversec with a locally hosted cybersecurity model running through LM Studio, then added NVIDIA NeMo Guardrails to compare model behavior under three conditions: no guardrails, input filtering, and combined input/output protection.
The work included configuring the local model environment, building a custom FastAPI gateway, integrating NeMo Guardrails, troubleshooting model latency and timeout issues, creating a reusable Spikee target, and analyzing attack results using Spikee’s built-in reporting tools.
The project also explored different adversarial testing approaches, including prompt injection datasets, obfuscation, encoded attacks, Best-of-N testing, synthetic canary leakage tests, and structured benchmark comparisons.
The objective was to measure how much the guardrails reduced successful attacks while keeping the model, dataset, and testing conditions consistent.
Tools used: Spikee, NVIDIA NeMo Guardrails, LM Studio, Python, FastAPI, PowerShell, Parrot OS, local LLMs, JSONL datasets, and custom security testing scripts.
This project demonstrates a hands-on approach to LLM red teaming, AI safety testing, prompt-injection assessment, and guardrail validation for organizations deploying generative AI systems.
pretty cool tbh. testing the same attacks with different guardrail setups makes the results way more interesting.
