How Garmentory Cut Product Moderation Time from 5 Days to 48 Seconds

Garmentory is a marketplace for independently owned clothing and home goods stores. They connect over 1,000 boutiques to shoppers looking for unique items. And until recently, they had a bottleneck that was capping how fast they could grow.

Every product that came into their system had to be moderated by a human before it could go live. Titles needed standardizing. Categories needed mapping. Sizes and colors needed normalizing. Descriptions needed cleaning. Six people worked on this full-time, and keeping up during busy seasons was a constant challenge.

The backlog would stretch to weeks during peak periods. Vendors fretted about when their products would go live. Some items went stale before they ever hit the site. The team knew they had great inventory waiting in the queue that they couldn't get to fast enough.

Then they tried something different. They started using Logic to automate the moderation process. Within a week, they went live. Almost instantly, their moderation time dropped from 4-5 days to 48 seconds.

This is the story of how they did it.

Watch the full conversation: This post is based on a live coffee chat with Garmentory CEO Sunil Gowda and Logic CEO Steve Krenzel. They go deeper on the tactical details of automation, AI adoption for small teams, and what it actually takes to scale operations without scaling headcount. Watch the recording here.

The Problem: Manual Moderation Was Limiting Growth

Sunil, Garmentory's founder, has been building the company for ten years. He came from engineering roles at Expedia, Microsoft, and Zillow. He thought he knew how to build marketplaces.

But Garmentory had its own challenges. The company imports products from over 1,000 different sellers, and each one formats their data differently. One boutique might list a shirt as "Women's Casual Top - Blue - Size M." Another might call the same type of item "Ladies Blouse (Medium, Navy)." A third might include their shipping policy in the description field.

Someone had to look at each product and clean it up. That meant reviewing 10 to 20 pieces of data per item: title, category, size, color, description, whether it's final sale, whether it's made to order, and more.

"A person could probably moderate about 200 products a day," Sunil said. "And that too, the quality would drop the more products you moderate. It's a very mentally taxing process."

The team had built up a 10-page document of rules. Veteran employees who'd been doing this for 5+ years could maintain quality. New hires took months to get up to speed. And even the veterans made more mistakes as the day wore on.

The math was tough. With 5,000 to 10,000 products coming in each week during busy seasons, and a realistic cap of maybe 1,500 products moderated per day at acceptable quality, backlogs were hard to avoid.

Why Traditional Approaches Weren't Working

Garmentory had already tried heuristic-based automation: if the description contains certain keywords, automatically mark it as final sale. If the price is below a threshold, flag it for review. Basic pattern matching.

It helped at the margins. But the long tail of variations made simple rules insufficient. Each boutique had its own quirks. The heuristics handled maybe 20% of the cases. The other 80% still needed human eyes.

They also couldn't justify moderating lower-priced items. If a product sold for $15, the cost of human moderation ate into any potential margin. So they set minimum price thresholds, which meant leaving some opportunities on the table.

"We had about 100,000 to 150,000 products active on the site at any given time," Sunil said. "That was basically where we were constantly hitting the limit with human moderation."

Meanwhile, the AI hype cycle was in full swing. Sunil watched from the sidelines, frustrated.

"We were trying things here and there. Image normalization, advertising calculations. But it felt like we weren't really adopting AI as quickly as possible. Mostly because we didn't have the in-house knowledge. If you follow the news, there's so much jargon being thrown around. RAG, this, that. It was overwhelming."

The Solution: Turning SOPs into APIs

The breakthrough came when Sunil started talking to Steve Krenzel, Logic's founder. Steve had spent years at companies like Brex and Convoy watching operations teams struggle with the same problem from a different angle.

At those companies, ops teams worked from prescriptive standard operating procedures. Hundreds of people needed to standardize how they thought about processes. And any time they wanted to change something, they had to wait for engineering to update the tools.

Logic took a different approach. Instead of requiring teams to build complex AI pipelines, you get a production-ready API endpoint you can start hitting immediately. No infrastructure to set up. No prompt engineering rabbit holes. You define what you want, Logic gives you a typed, versioned API that behaves reliably at scale.

"With Logic, we put the document in and we get this very familiar API interface," Sunil said. "These are the inputs I need to provide, and these are the reliable outputs I can expect. That unlocks AI usage in every single part of our business because this is how we already build everything."

The implementation took about a week. No changes to Garmentory's existing stack. They'd been consuming APIs from 20+ services already. This was just another one.

The key insight was breaking the problem down. Instead of trying to automate the entire 10-page SOP at once, Sunil split it into 3-4 discrete components. Each one could be tested independently against historical human-moderated products.

"Every single time, very quickly, it exceeded the quality that our human moderators were achieving," he said.

{{ LOGIC_WORKFLOW: moderate-product-listing-for-policy-compliance | Workflow as API: Moderate product listings for policy compliance }}

Results: From Bottleneck to Growth Engine

The numbers tell the story. Moderation time dropped from 4-5 days average to 48 seconds. Every product that gets imported now goes live in under a minute.

Weekly product imports tripled, from 5,000 to 15,000-20,000. The constraint wasn't capacity anymore.

Active inventory value grew from $150 million to $250-300 million. And Garmentory has signed agreements to bring in up to a billion dollars worth of additional inventory as they expand into new markets.

Cost per product moderated fell from about 25 cents to a few cents.

The team is still 9-10 people. No hiring spree required.

But the numbers don't capture the second-order effects. With moderation costs near zero, Garmentory dropped their minimum price threshold to $15. Now they can list items that weren't economical before. That gives new customers a lower-risk entry point to try the service.

On the other end, they started expanding into luxury products. Previously, the moderation bottleneck meant they had to prioritize. Now they can pursue both ends of the market simultaneously.

"Our vendors are happy," Sunil said. "They used to ask when their products would go live. Some of them actually purchase inventory specifically for Garmentory because our customers are different from their walk-in customers. For those scenarios, faster turnaround really matters."

The Framework: How to Identify AI Automation Opportunities

Steve has talked to hundreds of companies about AI adoption. The pattern he sees in successful implementations is consistent: start small, break problems down, validate before scaling.

"The areas where people get stuck in the muck is when they bite off really, really large tasks. They try to do these complex multi-step agentic workflows. But by breaking the task down into relatively well-defined, isolated decisions, you get a level of reliability that you just won't get from super complex agentic workflows."

His rule of thumb has evolved over the past year. "A year ago it was: if you have a human looking at a piece of data and making a decision about it in 2 seconds or less, you should absolutely be having AI do that. Fast forward a year, it's 30 seconds. If you're looking at a piece of data, doing anything with it in less than 30 seconds on a regular basis, there's no need for you to be doing that."

The 30-second window is a gimme. You'll be able to automate that, no problem. More complex tasks are possible too, but that threshold is the easy win.

The validation step matters. If you can't sit in front of ChatGPT and get it to do the task with you guiding it, it's probably not going to work hands-off at scale. You should have a rough idea whether automation will work within half a day at most.

"We knew AI could probably help us with product moderation," Sunil said. "But we also knew it could be a weeks-long or months-long process before we could evaluate it. We didn't want to invest that time into something that might not work. With Logic, within half a day we knew this could work."

Beyond Moderation: Expanding AI Use Cases

Once one process worked, Garmentory started seeing opportunities everywhere.

Size charts were a constant friction point. Customers shopping for unfamiliar brands wanted to know if items would fit. Garmentory's designers had been manually creating brand-specific size charts: visit the brand's website, extract the data, plug it into a template, render it, upload it.

Over 2-3 years, they'd built charts for maybe 200 brands. It was slow, tedious work that nobody was excited about.

Now they use Logic for it. Give it a URL, it fetches the page, extracts the size chart data, formats it into an HTML table, renders an image. They've gone from 200 brand size charts to over 2,000, with a goal of 5,000 by year end.

"Add to cart rates are much higher when we provide a brand-specific size chart," Sunil said.

They're also exploring aggregating product reviews at the brand level. Individual products have thin review data because of the long-tail catalog. But rolled up to the brand, there might be enough signal to display something useful.

"Every single problem I have with the business, I now think: can I use Logic to solve this?"

Common Concerns Addressed

What happens when AI gets something wrong?

Logic generates test cases designed to probe edge cases and ambiguous scenarios. When tests surface an issue, you can update your process description to handle it, and the fix applies immediately. You can also add your own test cases showing expected behavior for specific situations. Logic uses semantically similar historical inputs as examples, so one fix often improves handling of related cases.

And if a version underperforms in production, one-click rollback gets you back to the previous state.

What scale do you need to benefit?

Less than you'd think. Steve mentioned a customer who transcribes purchase orders maybe a dozen times per month. Very low volume, but each time it saves hours of work. And the automation catches errors that humans were making.

Garmentory runs some automations millions of times monthly, others just hundreds. The key question isn't volume. It's whether the task is repeated and standardized.

How is this different from just calling OpenAI directly?

The HTTP call to a model is the easy part. Everything else is hard.

Logic sends context to the model that's about 8x larger than your original document. It generates schemas with thorough descriptions for every field. It retrieves semantically similar historical results to improve consistency. It handles the inherent non-determinism of language models and gives you a deterministic interface.

"The actual HTTP call to the model is trivial," Steve said. "The tricky thing is everything that goes around that."

Key Takeaways

If you're considering AI automation for your operations, here's what worked for Garmentory:

Start with a single, sharply-felt paper cut. Not a grand AI strategy. One specific process that's causing real pain.

Break that process into small, evaluable components. Don't try to automate a 10-page SOP in one shot. Split it into pieces you can validate independently.

Time-box your evaluation. If you can't tell within half a day whether the approach will work, reconsider.

Measure what matters. For Garmentory, it was moderation time, throughput, and ultimately inventory value. Know your metrics before you start.

Let success snowball. Once one automation works, you'll start seeing opportunities you didn't notice before.

The technology exists to automate most routine decisions. The question is whether you'll use it.

Frequently Asked Questions

What is Logic?

Logic lets you build AI-powered features and tools fast. You define what you want, and Logic gives you a production-ready API endpoint with typed inputs and outputs, versioning, testing, and automatic scaling. No infrastructure to manage, no prompt engineering required.

How long does it take to implement Logic?

Garmentory went live within one week. Most teams can validate whether a use case will work within half a day.

What scale do you need to benefit from Logic?

Any repeatable process benefits. Garmentory runs some automations millions of times monthly, others just dozens. The key is whether the task is repeated and standardized, not raw volume.

How does Logic handle errors?

Logic generates test cases that probe edge cases and ambiguous scenarios. When tests surface an issue, updating your process description is straightforward and the fix applies immediately. You can also add your own test cases. Logic retrieves similar historical inputs to improve consistency, so fixes tend to generalize. If a version underperforms, use one-click rollback.

How is Logic different from using ChatGPT or the OpenAI API directly?

The HTTP call to a model is easy. Logic handles everything else: structured schemas with detailed field descriptions, semantic retrieval of similar historical examples, versioning, testing, and reliability at scale. The context Logic sends to the model is typically 8x larger than what you provide.

Can Logic handle complex multi-step processes?

Yes, but the most reliable approach is breaking complex processes into discrete, well-defined decisions. Garmentory's product moderation involved 10-20 data points per item, but they split it into 3-4 separate components for testing and validation.

What if I don't have my process documented?

You can start from scratch. Describe what you're trying to build, and Logic will generate a starting point you can iterate on. Most teams go from idea to working API in under an hour.

How Garmentory Cut Product Moderation Time from 5 Days to 48 Seconds