Case study
AI Search for a Semiconductor Maker on Multilingual Technical Content
How SquareShift built a secure Elasticsearch search foundation, then added AI summaries and a grounded, multilingual chat for technical content.
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Client
A semiconductor company modernizing its enterprise search experience.
Industry
Semiconductors
Services
Stack
Elasticsearch · Kibana · Elastic Agent Builder · Jina · Python · Amazon Bedrock
How SquareShift delivered it.
Project Context
The client wanted its users to find accurate product, technical and documentation content easily. SquareShift designed a search foundation on Elasticsearch and added AI summaries and a grounded chat experience.
Project Objectives
Build secure indexing for technical content.
Improve search accuracy and add source-backed AI summaries and multilingual chat.
Challenges
Content came in many formats, needing one search approach without losing document details.
Search had to balance exact part numbers with meaning, and keep restricted content secure.
Solution Delivery
SquareShift designed secure indexing for web pages, PDFs and other technical content, with metadata and document-level access controls.
Tuned search to combine exact part numbers with semantic understanding.
Built an Agent Builder chat with citations, follow-ups and multilingual answers.
Tested answers against the client's own test queries with automated checks.
Recommended speed and cost savings and handed over documentation.
Outcome
Search and AI chat run on one foundation. Three capabilities were delivered: data, AI search and AI chat. Delivery was on time, under a tight timeline.
Part numbers and meaning have to work in one search, and restricted content has to stay restricted.
Principal Consultant, SquareShift