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.
Industry
No items found.
Location
USA
Tech Stacks
Challenge

A fragmented product catalog with 120,000+ SKUs across 2,000+ categories made search painful, and sales conversion worse. They needed smarter, faster product discovery at scale.

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
Industry
No items found.
Location
USA
Tech Stacks
Challenge

A fragmented product catalog with 120,000+ SKUs across 2,000+ categories made search painful, and sales conversion worse. They needed smarter, faster product discovery at scale.

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

Co-create with Ideas2IT

We show up early, listen hard, and figure out how to move the needle. If that’s the kind of partner you’re looking for, we should talk.
We’ll align on what you're solving for - AI, software, cloud, or legacy systems
You'll get perspective from someone who’s shipped it before
If there’s a fit, we move fast — workshop, pilot, or a real build plan
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Manufacturing