Case study

How Shoelace scaled high-touch service delivery without scaling manual account-manager work

Built the operating layer behind onboarding, ad setup, campaign launch, retries, and account-manager workflows, turning repeated manual steps into reliable service throughput.

First engineering hire / operations systems

Executive summary

Shoelace originally launched as a SaaS product in the performance marketing space and evolved into a tech-enabled performance marketing service. As the model became more service-heavy, the bottleneck shifted from product features to operational throughput.


Vishaal joined as the first employee and first engineering hire, building the internal systems that helped service delivery scale without matching headcount growth.

Where work was breaking

At the time:

  • • client onboarding required too much setup
  • • account managers had to coordinate campaign launches manually
  • • Shopify, Facebook, reviews, email lists, and product data had to stay synced
  • • Facebook API instability created failure points
  • • every new ad format or client requirement added complexity

The operating problem was clear: if onboarding and campaign work stayed mostly manual, service delivery would be hard to run consistently and hard to scale.

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 systems and automation, even when the underlying data and APIs were messy.

  • • automatic Facebook Pixel installation on Shopify stores
  • • continuous Shopify catalog sync
  • • one-click ad launching for account managers
  • • integrations with supporting systems (reviews, email lists, etc.)
  • • retry logic and alerting to handle Facebook API failures
  • • manual fallbacks when third-party systems failed

These systems absorbed a large share of the repeated coordination work that account managers would otherwise have handled manually.

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 the workflow stayed reliable even when external systems changed or failed.

Operational outcome

Before

  • • 10-15 accounts per account manager
  • • About 15 minutes to launch an ad
  • • Heavy manual setup and coordination

After

  • • 100+ accounts per account manager
  • • About 1 minute to launch an ad
  • • Repeatable campaign workflows with fewer manual handoffs
  • • customer success and account teams experienced the largest productivity gains
  • • service delivery became much more repeatable without matching headcount growth

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

What this proves

A field-service company has a similar pattern: repeated jobs, recurring customers, messy handoffs, external systems, and too much work depending on people remembering each step. The lesson from Shoelace is that the highest-leverage work is often not replacing the whole business system. It is building the operating layer that makes repeated service delivery reliable.


Why it matters for recurring compliance: when field or admin teams repeat the same handoff hundreds of times, the leverage is in the operating layer behind the work.