A Custom SKU Matching ML Model Saves 14+ Hours Daily

A custom Machine Learning Model and LLM architecture eliminated 14 hours of daily busywork, for K&L Wines. The system also delivered 99% accuracy, and paid for itself within weeks.

CONCEPT

The Six-Figure Problem Hiding in Plain Sight

K&L Wines, a family-owned retailer since 1976, was spending thousands annually on a problem they thought was unfixable. Their team spent hours each day hunting through over 500,000 wine records to find the right SKU.

This is nothing new to the wine industry. Wine names are chaos. "Château Margaux 2015" could appear as "Chateau Margaux '15," "2015 Ch. Margaux," or dozens of variations. Standard database search proved unsuccessful, and building in-house wasn’t feasible without Machine Learning engineers.

The Compound Productivity Tax

7 employees × 2 hours daily × $75/hour = $1,050/day

The cost of 2 full-time employees

Data Chaos At Scale

500,000+ records with countless naming variations

Standard matching fails at this complexity

The Hidden Scaling Blocker

More inventory = More variations = More manual work

Rising costs limiting their scalability

CHALLENGE

Why Off-the-Shelf AI Failed

Initially K&L Wines tried out-of-the-box AI solutions, but the results were unreliable.

Exceeds ChatGPT's Capacity

LLMs can't handle 500K-record context windows. They hallucinate, cost too much at scale, and fail in production.

Context limitations

Unsolvable Edge Cases

Traditional engineering scripts that didn’t work at scale with data in production that was imperfect.

Can't achieve precision

Unnecessary Technical Debt

Building a machine learning solution beyond the necessary scope. K&L needed a tool that worked, not another tool to manage.

Complexity without value

SOLUTION

Hybrid Intelligence Architecture

We combined traditional ML and modern LLMs in ways neither could work alone

ML Does The Heavy Lifting

Custom machine learning models process all 500,000 records using vector embeddings, matching algorithms (Levenshtein, Jaro-Winkler), and pattern recognition.

LLM Adds Precision

With only 50 records (within context limits), an LLM applies human-like reasoning. Understands "Ch." = "Château," "89" = 1989, common misspellings.

Why This Hybrid Approach Works

User Experience

Simple for Users, Powerful Under the Hood

We didn't just hand K&L Wines a repository. We built a simple interface where they type a wine name and get results—that's it. No training required. No ML expertise needed. Their team started using it day one.

Enter wine name
Type any variation of the wine name

Get 2-5 accurate matches
Results in 1 second, not 1 hour

Select correct SKU
Human verification, ML-powered options

RESULTS

Real Business Impact

Week 1 ROI. Production-validated results. No organizational change required

$273K+

Annual Savings
14 hrs × $75 × 260 days

99%

Accuracy Rate
Validated in production

60x

Speed Increase
1 hour → 1 second (10 wines)

2 FTE

Equivalent Capacity
No hiring required

Beyond The Numbers

Week 1 ROI

Solution paid for itself in first month. Everything after is pure profit.

Zero Retraining

Simple interface meant immediate adoption. No change management needed.

Scales Automatically

Handles inventory growth without adding headcount. Built for the future.

Is Your Team Drowning In Manual Data Work?

If you're losing hours daily to tasks that "nobody has time to fix", let's talk. We specialize in finding hidden operational bottlenecks and building custom ML solutions that deliver measurable ROI in weeks, not quarters.

Schedule discovery call