VietPOS.AI deploys RAG knowledge assistant for a chain of 50 convenience stores

In early January 2026, VietPOS.AI completed the deployment of a RAG (Retrieval-Augmented Generation) system — an internal knowledge assistant — for a chain of 50 convenience stores in Southern Vietnam. This is one of the first enterprise AI projects in Vietnam to apply RAG to store-level operations support, not just customer chatbots.

The Problem: New employees don’t know 200+ policy pages

This convenience store chain has a high employee turnover rate — typical of the industry. Each new employee on shift needs to memorize over 200 pages of documents: return policies, incorrect invoice handling procedures, weekly rotating promotional product lists, shift opening/closing procedures, handling customer complaints… In practice, new employees have to call the shift supervisor or hotline whenever they encounter an unfamiliar situation, causing service disruptions and pressure on the central support team.

VietPOS.AI solved this by building a knowledge assistant that can query the entire chain’s internal documents in natural Vietnamese, pre-installed on the POS tablet right at the counter. Employees type or say “customer wants to exchange a shirt for a different size after 8 days, is that allowed?” — the system responds accurately within 3–5 seconds, citing the original policy section as evidence.

Two-Tier RAG Architecture

The system is designed with two tiers to balance speed and accuracy:

Tier 1 — Retrieval: A Vietnamese-optimized embedding model (fine-tuned on 50,000 sample questions from the Vietnamese retail industry) indexes all internal documents. When a question is received, the system retrieves 5–8 most relevant document segments.

Tier 2 — Generation: A language model generates natural language answers based on the retrieved document segments, with clear citations for each source segment. Answers are concise, match the chain’s internal tone, and avoid hallucination — a factor the retail chain absolutely cannot tolerate.

The entire system runs on edge servers located at the chain’s data center in HCM — NO public cloud usage. This was a mandatory requirement from the chain’s leadership to ensure internal policy data does not leak to foreign AI companies.

12-Week Deployment Process

The project went through four clear phases: (1) survey and clean internal documents — 4 weeks; (2) infrastructure setup + indexing — 3 weeks; (3) training and quality evaluation using the chain’s sample question set — 3 weeks; (4) wave-based deployment: 5 pilot stores → 20 stores → 50 stores — 2 weeks. Total 12 weeks from contract signing to official operation.

Phase (3) was the most time-consuming and critical for success: VietPOS.AI and the chain’s team jointly built a set of 500 sample questions covering real-world scenarios, checked answer accuracy, and fine-tuned prompts until reaching the acceptance threshold (90%+ of questions answered correctly with citations).

Results after two months of operation

According to feedback from the chain’s operations team after the first two months: the number of calls from stores to the support hotline decreased by approximately 60%; the time to handle “unfamiliar” situations dropped from 8–10 minutes (waiting for the hotline + explanation) to 1–2 minutes (searching via the assistant). New employees became more confident starting from their second shift, instead of needing two weeks of rigid training.

VietPOS.AI is currently in the negotiation phase for deployment with two other retail chains — with scales of 30 and 80 stores respectively. Interested businesses can register for a consultation at vietpos.ai.