How a Mid-Size Retailer Rebuilt Its AI Tech Stack with the Right Agency Partner

A client story shared with C2CReview

Table of Contents

1. The Starting Point
2. What Wasn't Working
3. Finding the Right Partner
4. What Changed
5. Takeaways for Other Teams

The Starting Point

The operations lead at a mid-size retail business had already run two AI pilots before reaching out through C2CReview — a product recommendation experiment and an internal support chatbot. Both had technically "worked" in testing. Neither had made it into daily use.

What Wasn't Working

The pattern will sound familiar to anyone who's been through this: promising demo, quiet stall. In their words, the recommendation engine "looked great on curated test data and fell apart the moment real customer data hit it." The support chatbot had no clear owner once the initial project team moved on to other work, so nobody maintained it.

"We didn't have an AI problem. We had a data problem wearing an AI costume." — Operations Lead, mid-size retail client

Finding the Right Partner

Rather than searching blind, the team used C2CReview to compare agencies listed under software development, filtering specifically for teams with real AI implementation case studies rather than general software portfolios. They shortlisted three agencies and asked each for a data audit before any model discussion — a filter that immediately narrowed the field.

What Changed

The agency they selected spent the first two weeks purely on data infrastructure: consolidating three disconnected customer databases, cleaning historical purchase records, and setting up a real-time feed from the retailer's e-commerce platform. Only after that was the recommendation model rebuilt and reconnected.

Results are still being tracked over a full quarterly cycle, and the retailer has asked that specific performance figures stay under wraps until that data is finalized — but the qualitative shift has been immediate: the recommendation feature is now live, monitored weekly by an assigned internal owner, and the support chatbot has a documented escalation path instead of being left to run unattended.

[Placeholder: quarterly performance data to be added once the client confirms figures for public sharing.]

Takeaways for Other Teams

• A data audit before model selection isn't optional — it's the step that decides whether the rest of the project works.
• "It worked in the demo" is not the same as "it's ready for production."
• Ownership matters as much as the technology. An AI feature without an assigned internal owner degrades quietly and nobody notices until it's broken.

Businesses evaluating similar projects can compare vetted software and AI implementation partners directly on C2CReview.