Our client is an ERP software provider serving mid-sized to large enterprises across the manufacturing, retail, logistics, and several other industries. Their platform manages critical business operations, including finance, procurement, inventory, human resources, customer management, and compliance.
As the customer base grew, so did the volume and complexity of data stored within each ERP instance. Clients were requesting more intuitive ways to explore and utilize their data beyond static dashboards and structured reports.
We developed a modular, tenant-aware AI assistant powered by LLM combined with RAG, embedded directly into the ERP platform. The assistant was designed to work independently for each ERP client, leveraging their unique data to generate accurate, real-time answers.
Software Deployment
Understanding Data Landscape
We conducted workshops with the ERP provider and key client stakeholders to identify the most valuable use cases, understand the data models and structure across ERP modules, define user roles, access levels, and integration points.
Preparing Knowledge for Retrieval
In this phase, we built connectors to ingest and preprocess data, including structured tables from SQL-based modules and documents and attachments from file storage. All content was normalized, chunked, and embedded into a vector database.
Building the RAG + LLM Pipeline
We deployed a Retrieval-Augmented Generation pipeline tailored to semantic search using embeddings and a vector DB that connects the retriever to a domain-aligned LLM, designed with prompt templates and guardrails for ERP-specific interactions.
Client-Specific Rollout and Validation
Once built, we launched the assistant in a controlled environment for each client, deployed securely with data isolation per tenant and integrated authentication with role-based visibility. We ran hands-on testing and gathered structured feedback from users.
Monitoring and Continuous Improvement
After the go-live, we focused on refinement and long-term support, including tracking of top queries, feedback, and drop-off points; fine-tuning prompts and retrieval logic based on real usage. We also added support for multilingual capabilities and expanded document formats.
Users no longer need to know database structures or navigate rigid filters, but simply type questions they have.
The assistant retrieves and combines insights from multiple sources, such as procurement data, vendor feedback, and delivery logs.
Every answer is generated based on the user’s access rights, role, and data visibility rules, ensuring compliance with enterprise security.
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