Salespark — AI Suggestion API
OpenAI product copy for Shopify, at scale
In short: Salespark — AI Suggestion API (AI / E-Commerce) is a case study from Daniyal Alam’s portfolio: OpenAI product copy for Shopify, at scale. Core stack: Fastify, TypeScript, MongoDB.
A multi-tenant Shopify app backend that uses OpenAI to generate optimised product titles and descriptions, with credit-based billing, real-time webhooks and a tested, containerised stack.
- Industry
- AI / E-Commerce
- Scope
- Production SaaS & full-stack systems
- Core stack
- Fastify · TypeScript · MongoDB · OpenAI
The problem
Shopify merchants struggle to write SEO-optimised, conversion-focused product copy at scale.
The solution
A Fastify API that generates alternative titles and descriptions from product context, deducts credits per use, persists history in MongoDB and notifies via Pusher.
Technical challenges
The engineering problems this build had to get right.
- Per-user OpenAI cost control via credits
- Shopify webhook reliability & latency
- Concurrent requests across many stores
Architecture
Implementation — what shipped
Engineering notes
Lesson for similar AI builds: design for per-user OpenAI cost control via credits and shopify webhook reliability & latency up front — these are the parts that are expensive to retrofit once the feature list grows.
Technologies
- Fastify
- TypeScript
- MongoDB
- OpenAI
- Shopify API
- Pusher
- BullMQ
- Redis
- Jest
- Docker
Building something similar?
A 30-minute discovery call is enough to scope the approach and a realistic budget. Related at DanixSoft: AI & Machine Learning.