Case Study · SaaS & AI Matching

Sidekick CRM

Virtual assistant directory using vector database embeddings and in-app helper bots.

Sidekick CRM

Overview

About this project

SideKick CRM & Marketplace is a double-sided directory connecting businesses with virtual assistants (VAs), utilizing vector database embeddings to score matches and featuring an in-app helper bot.

Semantic matchmaking engine parsing qualitative

Semantic matchmaking engine parsing qualitative

Challenge & Solution

The Challenge

Matching businesses with the right virtual assistant based on skills, time zones, and working styles is highly inefficient when relying on manual filtering. Traditional keyword search misses qualitative strengths, resulting in poor hiring matches and high turnover.

The Solution

We engineered an automated matchmaking engine that generates high-dimensional vector embeddings of business requirements and assistant profiles using OpenAI embeddings. Matches are scored and ranked instantly in PostgreSQL. An in-app helper bot powered by Claude 4.5 assists businesses in refining their hiring criteria.

Interactive VA marketplace directory with

Interactive VA marketplace directory with

Core Features

Semantic matchmaking engine parsing qualitative

Semantic matchmaking engine parsing qualitative job requirements.

Interactive VA marketplace directory with

Interactive VA marketplace directory with real-time video portfolios.

Integrated hiring pipeline with scheduler

Integrated hiring pipeline with scheduler and Stripe contract payments.

In-app AI assistant guiding employers

In-app AI assistant guiding employers through onboarding and hiring.

Integrated hiring pipeline with scheduler

Integrated hiring pipeline with scheduler

AI & Architecture

AI Intelligence

Matchmaking engine powered by OpenAI embeddings, alongside an in-app conversational helper bot running on Claude 4.5 for recruitment support.

System Architecture

Next.js 14 client communicating with an Express backend, querying a vector-enabled Supabase database.

Database & Caching

Supabase PostgreSQL with pgvector for embedding searches, cached with Redis for high-speed catalog searches.

Messaging

Push notifications for match recommendations, Daily.co integration for in-app video interviews, and SendGrid email notifications.

In-app AI assistant guiding employers

In-app AI assistant guiding employers

Tech Stack

Frontend

Next.js 14Framer MotionTailwind CSS

Backend

Node.jsSupabaseOpenAI APIClaude 4.5Stripe APIDaily.co

Deployment

Frontend hosted on Vercel, backend servers deployed on Heroku with Supabase backend.

We hired three virtual assistants within a single afternoon. The semantic matching engine is incredibly accurate.
D

David Lin