Case Studies

Proven results in real environments

We measure success by the impact we create. Explore how we've helped organizations across industries transform their operations with AI solutions that deliver measurable outcomes.

Retail & E-commerce

Cutting Forecasting Errors by 40% for a National Retailer

A multi-channel retail platform with 200+ locations

The Challenge

The client struggled with inventory management across their retail network. Their legacy forecasting system produced significant errors, leading to stockouts of popular items and overstock of slow-moving products. This resulted in millions in lost sales and carrying costs annually.

Our Solution

We developed a custom regression model incorporating historical sales data, seasonal patterns, promotional calendars, local events, and weather data. The model was deployed as a cloud-based microservice, integrating directly with their existing inventory management system.

Technologies Used

PythonTensorFlowAWS SageMakerPostgreSQLREST API

Results & Impact

40%
Reduction in forecast error
$2.3M
Annual savings in inventory costs
15%
Increase in product availability
8 weeks
From concept to production
"Hardcode delivered a solution that immediately impacted our bottom line. Their team understood both the technical requirements and our business constraints."

VP of Operations

Logistics & Supply Chain

Automating Demand Prediction for a Logistics Leader

A regional logistics company serving Fortune 500 clients

The Challenge

Manual demand forecasting processes were causing inefficient resource allocation. The operations team spent 30+ hours weekly on spreadsheet-based predictions, with accuracy rates below 70%. This led to underutilized capacity and missed delivery windows.

Our Solution

We built a cloud-based ML workflow that ingests data from multiple sources—shipping records, client forecasts, market indicators—and produces daily demand predictions. The system includes automated retraining pipelines and real-time monitoring dashboards.

Technologies Used

Pythonscikit-learnApache AirflowGCPBigQuery

Results & Impact

85%
Prediction accuracy achieved
30+
Hours saved weekly
22%
Improvement in fleet utilization
12%
Reduction in overtime labor
"The automated system has transformed how we plan our operations. We're now proactive instead of reactive, and our clients have noticed the improvement."

Director of Logistics

Financial Services

Optimizing Customer Interactions with Fine-Tuned LLMs

A Series B fintech startup with 500K+ active users

The Challenge

The client's customer support was overwhelmed with repetitive queries about account management, transactions, and product features. Their generic chatbot solution had low resolution rates and frustrated users, leading to increased support tickets and churn risk.

Our Solution

We fine-tuned a large language model on the client's knowledge base, support transcripts, and product documentation. The resulting chatbot handles complex, multi-turn conversations with domain-specific understanding, escalating to human agents only when necessary.

Technologies Used

GPT-4 Fine-tuningLangChainVector DBPythonFastAPI

Results & Impact

78%
Automated resolution rate
60%
Reduction in response time
4.5/5
User satisfaction score
45%
Reduction in support costs
"Our users now get instant, accurate answers to their questions. The chatbot understands context in ways our previous solution never could."

Head of Customer Experience

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