SUCCESS CASE

AI Assistant
optimizes knowledge management and
projects savings
of 250k in the
energy sector

The implementation of a Generative AI assistant to centralize access to critical manuals and documents reduced equipment downtime and generated projected savings of 250k in 12 months.

GenAI
RAG
Prompt Engineering

Results

projected cost reduction in 12 months
0 k
gain in agility in reducing downtime
0 %

Instant access to technical content and internal processes

Multilingual support for queries in 3 languages.

Overview

With complex and decentralized operations in the energy sector, the client faced bottlenecks in accessing essential technical information. Documents and operational manuals scattered across departments slowed down support for field teams, increasing unplanned equipment downtime and generating avoidable operational costs.

The challenges

Fragmented access to information, internal documents, and technical manuals scattered across different areas and systems.
Prolonged equipment downtime, delays in locating critical data during maintenance and field operations.
Decentralized and multilingual operations, with the need to support teams with demands in Portuguese, English, and Spanish.
Data security and segregation, requiring strict permission controls to prevent the leakage of confidential information between areas.

Journey
and solutions

ília developed an intelligent search and query platform powered by Generative Artificial Intelligence. The architecture combined Grounding, hierarchical Chunking, and Re-Rank techniques integrated with a Private Knowledge Base (Vector DB), enabling the assistant to answer complex queries in natural language with high precision and security.

What did we deliver?

Virtual assistant with a customized UI and support for three languages (EN, PT, and ES).
Advanced RAG architecture with a vector database (Vector DB), Grounding, and Re-Rank.
Dynamic content update pipeline for manuals and technical standards.
Authentication and governance layer with secure data segregation by department.

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