Shoelace

Operational context

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.

Where work was breaking

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.

System built

Role: first engineering hire / operations systems.

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.

• • 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.

Constraints and edge cases

The strategy was to automate the highest-leverage bottlenecks first, even though the environment stayed messy:

• • Facebook introduced new ad formats constantly
• • core APIs were unreliable and failure-prone
• • client requirements varied widely

The systems had to be robust, retry-safe, observable, and overrideable when needed. Automation was paired with manual fallbacks so account managers kept flexibility instead of becoming blocked by the tooling.

Operational 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

We can turn high-touch, failure-prone service delivery into a repeatable operating model. This is the most direct bridge to field-service positioning: when repetitive coordination is what limits scale, the highest-leverage move is to build the operating system behind the work.

Result: The business could scale service delivery profitably because the operating system carried more of the work.