Case Studies

Here are few case studies that demonstrate our applications of machine learning (ML) and generative AI (Gen AI) in different industries and use cases:

1. Demand Forecasting for Automobiles

Client-Specific Requirement: Forecasts had to be segmented by region and vehicle type for supply chain optimization across multiple factories.
Constraint: Models had to run on-premise due to data confidentiality regulations.

Problem Statement:
The client required accurate forecasts of next year’s vehicle demand based on the past five years of data to prevent overproduction and reduce stockouts.

Methodology:

• Used ARIMA and LSTM time series models

• Incorporated seasonal sales trends, regional economic indicators, and historical demand

• Created custom forecasting pipelines for each vehicle category and region
  
  

Solution:
A hybrid forecasting system was deployed, capable of regional and model-level forecasts, integrated with the client’s ERP to auto-adjust procurement and production plans.

Benefits:

• Time: Reduced forecasting cycle from 3 weeks to 4 days

• Cost: ~30% reduction in overproduction-related warehousing costs

• Effort: Eliminated 70% of manual forecasting workload for demand planners
  
  

2. Generative AI with RAG for Shop Floor Robot Maintenance

Client-Specific Constraint: Manuals included scanned PDFs in multiple languages (English, German, Japanese), requiring OCR and multilingual support.
Requirement: Must run offline due to limited factory internet connectivity.

Problem Statement:
Factory operators struggled to find relevant robot maintenance info quickly from extensive multilingual manuals and datasheets, causing delays.

Methodology:

• Used Llama SLM with RAG pipeline

• Applied OCR preprocessing and multilingual text extraction

• Indexed documents locally using FAISS

• Built a natural language interface for maintenance teams
  
  

Solution:
A local AI assistant allowed technicians to ask maintenance questions in natural language and get instant, context-aware answers from indexed manuals.

Benefits:

• Time: Reduced time to access repair instructions by 75%

• Cost: ~20% cut in downtime costs, saving ~$250K/year

• Effort: Maintenance effort reduced by 50%, especially for less experienced technicians
  
  

3. Machine Vision for Quality Inspection of Connecting Rods

Client-Specific Requirement: The solution needed to be integrated with existing PLC systems and had to operate in a low-light environment.
Constraint: False negatives (letting a defective part pass) had to be <0.5%.

Problem Statement:
Manual visual inspection led to high human error and inconsistent quality in connecting rod production.

Methodology:

• Used CNN-based image recognition

• Trained model on annotated images of defects (scratches, dents, bolt issues)

• Integrated with industrial camera systems and edge computing units for real-time processing

• Adjusted preprocessing pipeline for low-light image enhancement
  
  

Solution:
A robust AI-driven inspection system was deployed, integrated with factory PLCs and edge devices to flag defects in real time and trigger rejection mechanisms.

Benefits:

• Time: Inspection speed improved by 4x

• Cost: Reduced defect-related returns and rework by 35%

• Effort: Manual quality control workload dropped by 60%; ensured <0.3% false negatives
  
  

4. Generative AI for Summarization of Legal Documents

Problem Statement:
Lawyers were spending too much time reading through long NDAs and contracts to extract key clauses, slowing down legal reviews.

Methodology:

• Used transformer models (Llama SLM) for summarization

• Fine-tuned models on legal documents for clause recognition

• Built clause-level highlights and tagging system
  
  

Solution:
Developed a legal document AI summarizer that generated structured outputs with key terms, obligations, risks, and summaries for fast review.

Benefits:

• Time: Reduced review time per document by 60%

• Cost: Enabled firm to increase client volume by 20% with the same team

• Effort: Lawyers spent less time on repetitive reading and more on strategic work
  
  

These case studies highlight our diverse applications of machine learning and generative AI across industries, demonstrating our ability to solve complex problems, improve operational efficiency, and provide valuable insights.