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SaaS Platform2024

Exhibit Social — B2B Influencer Marketing Platform

Team Lead — end-to-end B2B influencer marketing platform built from scratch

app.exhibit.social/
Exhibit Social — B2B Influencer Marketing Platform — live website screenshot
Team Lead
role
End-to-End
type
B2B SaaS
model
Client

Exhibit Magazine India Pvt. Ltd.

Overview

Architected and built Exhibit Social — a full-scale B2B influencer marketing platform — completely end-to-end while leading the development team at Exhibit Magazine India Pvt. Ltd. The platform enables brands to discover, vet, and collaborate with influencers through a data-rich, automated ecosystem. I was responsible for every layer of the product: from architecture and backend engineering to data pipelines and team leadership.

Technologies Used
LaravelVue.jsMySQLRedisPythonWeb ScrapingMeta APIYouTube APIGCP
The Challenge

Problem Statement

Brands lacked a reliable, data-driven way to identify and engage with the right influencers for B2B campaigns. The platform needed robust influencer discovery, real-time analytics, automated data collection via scraping, and a seamless collaboration workflow — all built from zero.

The Solution

How I Solved It

Led the end-to-end build of the platform including influencer discovery engine, data scraping pipelines to collect and store influencer metrics at scale, campaign management tools, analytics dashboards integrating Meta and YouTube APIs, and a collaboration interface for brands and influencers. Managed and mentored the engineering team throughout the entire product lifecycle.

Outcomes

Results Achieved

Full B2B influencer marketing platform built end-to-end
Automated scraping pipelines for influencer data collection & storage
Real-time analytics via Meta & YouTube API integrations
Led and managed the development team across the full build
Platform live and serving brand campaigns in production
Insights

Key Takeaways

1. Automated data collection pipelines are critical for B2B platforms — manual influencer data entry doesn't scale.
2. Meta and YouTube API rate limits require careful architecture — implementing request queuing with exponential backoff prevented account suspensions.
3. End-to-end ownership from architecture to deployment reduces coordination overhead and accelerates shipping.
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