We know what AI can do, what it cannot do, and how to deliver meaningful outcomes.
Our expertise spans predictive and generative AI across computer vision, reinforcement learning, and large language models.

1. Automated Service Bulletin Integration Using LLMs

Enterprise Software Market Cap: $275M LLM Parsing
Problem Statement

OEM service bulletins arrive as unstructured PDFs with critical directives. Analysts must manually interpret and transfer this into a database with thousands of tables—a slow, error-prone process.

Our Solution

We built an LLM-powered system that parses bulletins (text & tables) and maps them to the database schema. It extracts inspection steps and parts, ensuring accurate, automated updates.

Service Bulletin Pipeline Diagram

2. Knowledge-Graph Assisted RAG for Analytics

Enterprise Software Market Cap: $275M Knowledge Graph
Problem Statement

Retrieval relied on SQL-based RAG across 4,000+ tables, causing slow response times and inconsistent joins for complex analytical queries.

Our Solution

We introduced a Knowledge-Graph–assisted RAG pipeline. This parallel semantic layer improved retrieval precision, explainability, and query speed at enterprise scale.

Knowledge Graph Architecture

3. RAG-Powered Automation of SAP Documentation

ITSM Provider Market Cap: $550M Automated Docs
Problem Statement

Manually drafting extensive Process Design Documents (PDDs) for SAP implementation is slow and inconsistent, relying heavily on consultants' memory of best practices.

Our Solution

A RAG-based LLM system that generates complete PDDs by retrieving from SAP best-practice libraries. It automates drafting, ensuring consistency and faster delivery.

RAG Document Generation Flow

4. Clean Core Enablement for SAP S/4HANA

ITSM Provider Market Cap: $550M Legacy Modernization
Problem Statement

Legacy custom code embedded in the SAP core increases technical debt and upgrade risks, violating Clean Core principles during migration.

Our Solution

We used LLM-assisted analysis to identify redundant code and migrate necessary extensions to SAP BTP, creating a lean, upgrade-friendly core.

Clean Core Strategy Diagram

5. AI-Powered Product Recommendation Engine

Online Jewellery Retail Computer Vision E-Commerce
Problem Statement

Customers struggle to find jewelry that matches their style in a large catalog. Traditional rule-based recommendations fail to recognize aesthetic similarities.

Our Solution

We built a CV-driven engine that understands visual attributes (motifs, finishes). It suggests "look-alike" and "complete-the-set" items, boosting engagement and conversions.

Visual Recommendation Engine