Case Study 04
How a mid-size e-commerce firm boosted sales operations with an on-prem LLM RAG application.
A mid-size e-commerce firm needed a faster way for sales operators to find internal knowledge, summarize documents, and respond to customer questions without exposing sensitive commercial data to cloud AI services.
Who this engagement was for.
The client was a US-based e-commerce firm with around 120 employees, focused on online retail platforms for consumer goods. Their sales team handled customer inquiries, upselling, and deal progression, but lacked efficient tools for working across internal documents and knowledge sources.
As product complexity increased, the team needed a better way to retrieve answers, summarize materials, and support customer-facing conversations without slowing down sales operations.
Why research-heavy sales workflows were becoming a drag on growth.
In early 2026, the firm's sales operators were spending too much time searching scattered documents, internal emails, pricing guides, product specs, and customer histories just to answer routine questions. There was no centralized intelligent interface for querying that knowledge, which slowed responses and created missed sales opportunities.
The gaps were especially visible around summarization and retrieval. Operators needed faster ways to review documents, pull the right product details, and get answers without manually combing through multiple systems. Integration gaps with Slack and Google Drive also meant knowledge stayed siloed across tools instead of becoming part of a unified workflow.
Cloud-based LLMs were not acceptable because the company wanted to keep sensitive sales data and internal documents fully under its own control. The result was a sales team spending up to 40 percent of its time on research instead of customer engagement, which hurt conversion efficiency and increased frustration internally.
Developing an on-prem LLM RAG application with a dedicated UI.
- Built an on-prem Retrieval-Augmented Generation pipeline that combined vector-based retrieval with local LLM generation for context-aware answers.
- Delivered a web UI so sales operators could ask natural-language questions, upload documents, and receive responses with supporting citations.
- Added document summarization workflows for PDFs, reports, emails, and product materials so operators could extract the important details in seconds.
- Integrated the application with Slack for bot-assisted queries and Google Drive for ingestion and syncing of shared documents.
- Implemented role-based access, encryption, audit logging, and on-prem processing so sensitive sales information stayed fully under company control.
What the implementation looked like.
We collaborated with the firm to build a custom on-prem RAG application using local models such as Llama or Mistral, with a retrieval layer backed by a local vector store and orchestration patterns similar to LangChain. The interface was designed to be simple enough for day-to-day sales use while still supporting more advanced tasks like multi-document summarization and knowledge-grounded question answering.
The application was hosted entirely on the company's private infrastructure, which preserved data sovereignty and kept internal commercial knowledge out of third-party AI systems. The UI became the main access point for sales operators, while Slack and Google Drive integrations extended the same knowledge workflows into the tools the team already used.
The system was also built to be extensible, with observability dashboards and usage analytics so the team could understand adoption, performance, and where additional workflows might create more leverage.
What changed after the application went live.
Sales operators gained a single interface for querying internal knowledge, summarizing documents, and getting grounded answers quickly. Research-heavy tasks that previously consumed large parts of the day were compressed into short query flows, which gave the team more time for customer engagement and deal progression.
The new RAG system improved response speed, reduced workflow fragmentation, and made Slack and Google Drive part of the same knowledge loop instead of separate silos. Internal teams were able to support sales activity more effectively without exposing sensitive information to external LLM vendors.
Most importantly, the company established a secure on-prem AI foundation for future sales and support workflows. The platform was not just a point solution for retrieval; it became a flexible internal capability for document summarization, knowledge access, and additional AI-assisted use cases.