Case 04 · network · 2024
Exhibit Social
A B2B influencer-marketing platform built from zero: 10,000+ influencers indexed and 30+ brand campaigns run on it.
The brief behind Exhibit Social
I led the team that built a B2B influencer-marketing platform end to end: influencer discovery, automated data collection, analytics from the Meta and YouTube APIs, campaign management and a collaboration workspace for brands and creators.
Brands wanted a reliable, data-driven way to find and vet influencers for campaigns, instead of spreadsheets and guesswork. It was a B2B SaaS built from zero: scheduled data pipelines, Meta and YouTube API clients that respect rate limits, and a Laravel and Vue.js platform for brands and creators.
What constrained the work
Discovery, real-time analytics, automated data collection and a collaboration workflow, all built from nothing, on top of third-party APIs with strict rate limits.
How Exhibit Social is built
- Automated collection pipelines in Python gather and store influencer metrics on a schedule.
- Rate-limit-aware API clients for Meta and YouTube, with request queues and exponential backoff.
- A Laravel and Vue.js platform for discovery, campaigns, analytics dashboards and brand–creator collaboration.
- MySQL and Redis on Google Cloud for storage and caching.
Trade-offs and why
Scheduled collection over on-demand lookups: data is minutes old instead of live, and the platform never trips API limits.
One team owning every layer, from data pipelines to UI, meant less coordination and faster shipping.
What changed, measured
- influencers indexed
- 10,000+
- large brand campaigns
- 30+
10,000+ influencers indexed and kept up to date automatically.
30+ large brand campaigns run on the platform.
Influencer data is collected automatically instead of by hand.
Lessons from Exhibit Social
Automated data collection is the backbone of a B2B data product. Manual entry doesn't scale.
Design for third-party rate limits from the first day. Queues and backoff prevented account suspensions.
End-to-end ownership shortens the path from idea to production.
Technologies used
- Laravel
- Vue.js
- Python
- MySQL
- Redis
- Meta Graph API
- YouTube Data API
- Google Cloud
Services used
- Service: SaaS development and web platforms · Product engineering
Custom web applications, APIs and integrations, built to be inherited.
- Service: MVP Sprint · Product engineering
A production-grade first version of your product, at a fixed price and on dates fixed after the scoping week.
Where this work happened
- Working with teams in India · Mumbai · Bengaluru
A senior software engineer in Mumbai for Indian startups and product teams, on the same clock as you and close enough to meet in person when a session is worth it. It is for founders with a consumer launch to scale, a media or publishing platform to keep fast, or a PHP system to modernise.
Related writing
- Article: How long a SaaS MVP takes: the method, not a number · 10 October 2026
Why any single number for an MVP is a guess, what actually decides the time, the published time-boxes product teams use, and how to fix dates you can keep.