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GenAI Chatbot Development for OSRAM with RAG

Services Covered:

Chatbot Development • AI and Machine Learning • Cloud Consulting and Services • Big Data Analytics and Business Intelligence
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Industry
Industry

Engineering & Manufacturing

Duration
Duration

8 months

Budget
Budget

Confidential

Start Date
Start Date

1 June 2024

Team Size
Team Size

1-5

Engagement Model
Engagement Model

Nearshore

Project Capability Score (PCS)

gauge meter
78/100
Good

Project Capability Score (PCS) estimates how capable Adastra is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

OSRAM GmbH

mapMunich, Germany

Project Summary

Adastra built a GenAI-powered chatbot for a global lighting manufacturer to reduce the time employees spent searching for procedural and machine-related documents. The system ingests and processes documents, applies vectorization and RAG, and lets users ask natural-language queries, returning answers within seconds. Deployed on AWS with an included CI/CD pipeline and feedback loop, the solution delivered immediate productivity gains, faster decision-making, strong adoption and high employee satisfaction.

Key Challenges

  • Reduce the time employees spend searching for procedural and machine-related documents
  • Improve productivity in production plants by providing faster access to information
  • Enable better and quicker decision-making in production environments
  • Ensure scalability and continuous improvement of the solution

Project Deliverables

  • GenAI-powered chatbot
  • Document ingestion and processing pipeline
  • Vectorization and Retrieval-Augmented Generation (RAG) knowledge retrieval
  • AWS-based scalable deployment with a CI/CD pipeline
  • Feedback and analytics loop to measure adoption and satisfaction

Project Solution

Adastra designed and implemented a GenAI-powered chatbot tailored to the client's production environment. The vendor ingested and processed all relevant documentation, applied vectorization and Retrieval-Augmented Generation (RAG) techniques, and enabled natural-language queries that return direct answers and point to source documents. The system was deployed on AWS (using Amazon Bedrock) with a CI/CD pipeline to ensure scalability and easy updates, and a feedback loop was built to measure adoption, satisfaction, and to support continuous improvement.

Project Outcome

  • The chatbot significantly reduced the time required to find critical procedural or machine-related documents
  • Employees can locate the right information in seconds, delivering immediate productivity gains and cost savings
  • Faster access to knowledge improved decision-making and reduced potential downtime in production
  • Strong adoption and high employee satisfaction were reported, with a feedback loop enabling continuous enhancement

Platforms

  • CloudCloud

Tech Stack

  • Amazon Web Services (AWS)Amazon Web Services (AWS)

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“The chatbot significantly reduced the time required to find critical procedural or machine-related documents, delivering immediate productivity gains. Adastra managed the project with precision and clear communication. Their team was responsive to the client's needs.”

Bernd Eberhard

Director R&D

Note: This endorsement is based on publicly available client feedback from external review sources.

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