Data Dilemma to Discovery: How Ideas2IT Elevated Information Retrieval for an Engineering Giant

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Case Study

Data Dilemma to Discovery: How Ideas2IT Elevated Information Retrieval for an Engineering Giant

Solutions Deployed:

Semantic Product Information Retrieval System
Intelligent Chatbot Interface
Graph Database Integration

Vertical:

Manufacturing

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To address the challenge of navigating a catalog with over 2000 categories and 120,000 products, our client, a leading engineering giant, sought a solution for seamless access to critical information. Ideas2IT built a semantic product information retrieval system using Dolly 12B, Langchain, and FAISS. This solution boosted search precision by 74% and increased sales conversions by 23%, significantly improving efficiency and decision-making. The system offered real-time access to resources, enhancing workflow and collaboration.

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Managing Vast Product Data Across Multiple Stakeholders

Our client faced a significant challenge: managing an expansive catalog of over 2000 categories and 120,000+ products, with prices ranging from as low as $1 to as high as $1 million. Retrieving accurate and timely information from this immense database had become an almost insurmountable task.

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Case Study

Data Dilemma to Discovery: How Ideas2IT Elevated Information Retrieval for an Engineering Giant

Solutions Deployed:

Semantic Product Information Retrieval System
Intelligent Chatbot Interface
Graph Database Integration

Vertical:

Manufacturing

‍

Managing Vast Product Data Across Multiple Stakeholders

Enter your email to
access the full case study

Connect with Us

We'd love to brainstorm your priority tech initiatives and contribute to the best outcomes.

From Data Dilemma to AI-Driven Discovery: A Case Study in Semantic Information Retrieval

One-liner summary:

Ideas2IT engineered a secure, on-premise semantic search platform with a custom chatbot and graph-based knowledge base. The result: a 74% improvement in search precision and a 23% increase in sales conversions for a global manufacturing enterprise.

The Problem with the Status Quo

A global engineering leader with over 120,000 SKUs and 2,000 product categories was struggling to surface relevant product information for sales teams, channel partners, and pricing analysts. Legacy tools slowed down quoting and hurt deal velocity. Ideas2IT built a fully on-prem AI solution using Dolly 12B, LangChain, FAISS, and a custom product knowledge graph. The platform delivered real-time, role-aware search powered by Retrieval-Augmented Generation, resulting in a 74% boost in search accuracy and a 23% lift in conversions.

Where the Gaps Were

High Complexity: 120,000 SKUs across 2,000+ categories
Data Silos: Specs, pricing, and configurations scattered across ERP, PDFs, and legacy portals
Manual Intermediation: Sales and pricing teams relied on experts to interpret data
Lost Revenue: Slow responses delayed quotes and eroded deal momentum
‍Strict Security: Cloud-based AI was not an option. The solution had to run fully on-premise

What We Delivered

Ideas2IT designed and delivered a hybrid AI system that combined vector similarity, product knowledge graphs, and Retrieval-Augmented Generation to drive intelligent search and decision support. Key components included:

Metadata & Taxonomy Design: Mapped product hierarchies, attribute tags, and retrieval logic
LLM Infrastructure: Deployed Dolly 12B locally to power enterprise-grade reasoning
Vector Search Stack: Embedded product documents using BERT Sentence Transformer and indexed with FAISS
Graph-Based Modeling: Structured 120,000+ products into a searchable, traversable graph database
LangChain RAG Framework: Enabled chat-based querying with dynamic product lookups
Custom Chat Interface: Built internal chatbot UI with feedback loops and auto-logging
Real-Time Integrations: Connected pricing APIs, stock lookups, and related-product navigation
Role-Specific Prompting: Tuned behavior for different personas (sales, pricing, product teams)
‍On-Premise Compliance: Entire stack ran in an air-gapped environment for IP control and security

Outcomes We Achieved

Metric / Outcome	Value Delivered
Search Accuracy	+74% precision improvement
Sales Conversions	+23% uplift in closed deals
Time to Answer	Reduced from hours to seconds
Data Security	Fully on-premise, zero leakage risk
Stakeholder Coverage	Sales, pricing, product, partners
Products Indexed	120,000+ with deep category mapping
Compliance	Full GDPR + HIPAA with audit logs

Key Takeaways

Traditional search fails at scale, semantic context and structured relationships are both essential
RAG works best when paired with explicit business logic and decision paths
Adoption rises when users save time, reduce handoffs, and trust AI-generated answers
On-prem AI stacks unlock enterprise adoption where security is non-negotiable