BullMentor

BullMentor is a marketplace that connects retail traders with verified trading mentors. It was one of the last projects I worked on at Salt Technology, and one where I carried the same principal-developer responsibilities I'd built up on Nexia Wellness into a more complex, multi-sided marketplace product.

BullMentor lets traders discover, filter and evaluate trading mentors by market, style, experience and pricing, view detailed mentor profiles, and book 1:1 mentorship sessions. Mentors are verified — the platform uses broker-statement verification as part of its trust model — and the product has grown from a directory into a marketplace with mentorship and community/subscription elements. Behind the public surfaces sit the workflows a two-sided marketplace runs on: onboarding for both kinds of account, mentor–trader messaging, and the administration and dispute-resolution tools used to keep the two sides working.

Professional Experience · Salt Technology · 2024 – 2025

• Built the onboarding for both sides of the marketplace — mentors and traders — including the dynamic forms and validation behind each path
• Built the mentor discovery, filtering and profile surfaces, integrating the REST APIs behind mentor data, pricing and booking
• Integrated calendar-based session booking, from a mentor publishing availability to a trader taking a slot
• Led development of the admin panel — user management, session tracking, and the dispute-resolution workflow administrators use to settle issues between the two sides
• Collaborated on the real-time mentor–trader chat, built on a WebSocket architecture
• Implemented mobile-first layouts across the product, and kept discovery pages fast with lazy loading, image compression and Next.js SSR/SSG

A marketplace like this lives or dies on how quickly a visitor can trust a mentor and move from browsing to booking. That means the discovery, filtering and profile experience has to feel fast and clear even as the amount of mentor data, pricing structures and verification signals grows.

• Modelled the mentor profile as one component set with variable states rather than a template per kind of mentor. Pricing, verification and availability all differ, and branching by type would have multiplied the surface every new tier had to touch.
• Server-rendered the public discovery and profile pages, where being indexable mattered as much as being fast, and chose the rendering mode per page rather than applying one across the product.
• Designed each API-backed view around its loading and error states from the outset — mentor data, pricing and availability all arrive asynchronously, and a marketplace that looks broken while it waits loses the visitor before it finishes loading.

• Built responsive, reusable components for mentor discovery, filtering and profile surfaces in React, Next.js and Tailwind CSS
• Integrated REST APIs powering mentor data, pricing and booking flows, with attention to error handling and loading states
• Applied lazy loading, image optimization and render-blocking-script reduction across the marketplace's discovery and profile surfaces
• Implemented technical SEO fundamentals — meta tags, structured data and sitemaps — for public marketplace and profile pages

BullMentor shipped as a production marketplace product with a discovery-to-booking journey built to scale as mentor and trader volume grows.