Shoelace

Overview

Shoelace originally launched as a SaaS product in the performance marketing space. When Vishaal joined, the company had just raised a seed round and was doing four figures in revenue.
Vishaal was the first employee overall and the first engineering hire, operating as a generalist with a mandate to do whatever was necessary to make the business viable.
Over time, it became clear that the fastest path to revenue and survival was to evolve into a tech-enabled agency, using software to deliver services at scale.

Context

At the time:

• • the SaaS model was struggling to gain traction
• • revenue was low and margins were thin
• • client onboarding and account management were almost entirely manual
• • Facebook Ads was evolving rapidly, with frequent API changes and instability

Running the business manually at SaaS-level pricing would have resulted in negative margins and eventual failure. Scale through automation was the only viable path forward.

The problem

As Shoelace shifted toward a services-based model, core workflows were extremely time-intensive:

• • onboarding new clients
• • connecting Shopify stores and Facebook Ads accounts
• • pulling product catalogs for ad creatives
• • creating and launching campaigns manually in Facebook
• • reporting results back to clients

These workflows required constant human effort and coordination. Without automation, there was no path to sustainable scale.

Role

Role: first engineering hire / operations automation.

Partners: three founders. Scope: cross-functional work focused on identifying the biggest operational bottlenecks and removing them through automation—even when solutions had to accommodate messy data, unreliable APIs, and changing requirements.

Approach

The strategy was to automate the highest-leverage bottlenecks first, even if the systems weren’t perfect.

• • Facebook frequently introduced new ad formats
• • their APIs were unreliable and prone to failure
• • client requirements varied widely

Designed systems that were robust, retry-safe, observable, and overrideable when necessary. Automation was paired with manual fallbacks so account managers retained flexibility.

Systems built

• • automatic Facebook Pixel installation on Shopify stores
• • continuous syncing of Shopify product catalogs
• • one-click ad launching for account managers
• • integrations with supporting systems (reviews, email lists, etc.)
• • retry logic and alerting to handle Facebook API failures

These systems absorbed operational complexity and eliminated repetitive manual work.

Outcome

• • account managers scaled from 10–15 accounts to 100+ accounts each
• • launching an ad went from a 15-minute manual process to ~1 minute
• • customer success and account teams experienced the largest productivity gains
• • the agency model became economically viable at scale

As the business grew, these systems were extended to support additional ad formats, automated client reporting, and new workflow variations.

What this proves

This case study illustrates an approach to operational automation inside real, messy businesses:

• • identifying the highest-leverage operational bottlenecks, not just surface inefficiencies
• • building automation that survives unreliable APIs, changing requirements, and edge cases
• • increasing throughput per employee instead of adding headcount

In short: systems as a force multiplier for operations, not incremental process improvement.

Result: High-leverage automation enabled a SaaS-turned-agency to scale profitably, allowing each account manager to handle 100+ clients without increasing headcount.