Supporting Complex Engineering Support with LLM-Powered Search and Diagnosis

Quick Summary

Challenge

A global water treatment technology leader needed a way to unlock technical documentation — from manuals to product sheets — to assist field engineers and customer service teams in solving installation issues faster.

Solution

Tatras Data built a robust RAG-based LLM system that understands complex layouts, handles technical abbreviations, and adapts responses based on user role, region, and permissions.

Result

Faster diagnosis, reduced downtime, and multilingual support for service reps and account managers, powered by a secure, role-aware GenAI assistant.

Tech Stack

AI: OpenAI LLMs | ML: Custom abbreviation handling Layout-aware vision models | Data & Retrieval: Secure RAG Role- and region-sensitive indexing Validation logic | Dev: Serverless pipeline for document parsing and chunking API-based deployment | Viz: Internal dashboards for query quality Model output monitoring | Security: RBAC Multilingual support GDPR-aligned regional filters

The Challenge

A world-leading water treatment company faced a high-impact bottleneck:

Field teams and customer service agents struggled to quickly find answers buried in dense technical documentation. The materials included complex PDFs, tables, and product specs filled with abbreviations and layout variations.

When a client site experienced a technical issue, delays in retrieving accurate troubleshooting steps directly impacted downtime, SLAs, and customer satisfaction.

New recruits needed a faster learning curve. Experienced account managers needed instant, context-sensitive access, without having to sift through hundreds of pages.

A Day in the Life: Before Our Solution

An engineer on-site receives a customer alert:

"The conductivity sensor on unit X-930 isn’t responding as expected."

They consult the manual, but it’s a 200-page PDF with tabular data, nested references, and undocumented acronyms.

They email a senior team member, wait for a reply, and patch together information from old threads and product specs. Diagnosis can take hours. Some answers never arrive in time.

Pain Points:

Field agents spent critical time searching across outdated or siloed documents
Technical abbreviations made manual search difficult and error-prone
Response quality varied widely based on employee experience
Complex layouts (tables, diagrams) were hard to parse by existing systems
Lack of region-specific data filtering risked compliance and added confusion

Solution

1. Core Innovation

Tatras Data built a production-grade LLM assistant that understands technical complexity — and responds with speed, accuracy, and context.

Key components include:

Abbreviation Handling Engine
Recognizes and interprets domain-specific shorthand, even when used inconsistently across manuals.
Layout-Aware Vision Module Processes technical PDFs, extracting structured content like tables and embedded specs for use in context.
Advanced Chunking & Retrieval Optimization
Uses dynamic strategies to enhance precision and relevance in matching queries to document fragments.
Answer Validation Layer
Assesses the quality of RAG-generated responses. When confidence is low, it supplements answers using public model reasoning.
Role + Region-Aware Filtering
Ensures users only receive information they are permitted to access, based on function, geography, and compliance rules.

2. Key Features

Domain-Specific Query Engine: Handles engineering jargon, part codes, and abbreviations
Vision-Based PDF Parsing: Extracts and integrates tabular and layout-rich data
Secure Access Control: Filters content based on user permissions and region
Multilingual Support: Serves users across global operations
Response Quality Validation: Auto-detects weak answers and improves them in real time
Custom Serverless Pipeline: Continuously ingests and prepares documentation for AI-ready use

3. Workflow Integration

The assistant is integrated into the firm’s internal support and field service tools. Field technicians, customer service teams, and account managers can type questions in natural language, and instantly receive contextual, role-aware answers, complete with technical references.

Outcomes

✅ LLM deployed in production across support teams globally

⏱️ Significant reduction in diagnosis and resolution times

🔍 Accurate answers even for complex product sheets and specs

📚 Dynamic knowledge base refresh with secure filtering

🌐 Multilingual support improves onboarding and customer experience across regions