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About the project

The client needed a way to give consistent technical support to all teams. Earlier, technicians depended on manuals or senior experts. This caused delays and different ways of solving the same problem. There was no central system. As a result, errors were repeated, and troubleshooting was not consistent. To solve this, we built an AI-powered assistant based on the client’s internal knowledge base. The assistant provides step-by-step guidance and asks follow-up questions to refine answers.

It works across web and mobile, and supports text, voice, and image inputs. This makes it useful for both field technicians and office teams. The system only responds using approved documentation. Each answer includes references to ensure accuracy. A role-based setup was also created so that different users can access features based on their responsibilities. We also added a feedback system where users can rate responses. This helps improve the system over time.

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Technologies used

The solution was built using modern and reliable technologies. Python (FastAPI) was used to develop a fast and structured backend. LangChain supported the agent-based AI architecture, while OpenAI handled the language processing. Qdrant was used for vector-based knowledge retrieval, and PostgreSQL managed structured data efficiently.

Magento
Python (FastAPI)
PHP
LangChain
PHP
OpenAI
Qdrant Vector Database
Qdrant
PHP
PostgreSQL

Services we offered

We handled the full development and setup of the system, including:

  • AI assistant development

    Built a conversational assistant that delivers structured, step-by-step technical guidance and adapts responses based on user queries and real scenarios.

  • System architecture and access control

    Designed a secure and scalable backend with role-based access, ensuring technicians, managers, and admins interact with the system as per their roles.

  • Knowledge integration and multimodal support

    Integrated the assistant with internal documentation and enabled text, voice, and image inputs to support real-world usage across field and office environments.

  • Ongoing support and improvement

    Implemented feedback loops and monitoring to refine responses over time, helping improve accuracy and keep the system aligned with operational needs.

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Final results

The solution brought more structure to the client’s support process.

  • Teams can now access the same information in real-time

  • Reduced dependency on senior experts

  • Faster issue resolution in the field

  • More consistent troubleshooting across teams

  • Improved overall reliability of technical support

  • Better knowledge sharing across teams