Automated Answers, Zero Wait Time – Reducing HR Load with a RAG-Based Fleet Chatbot
Imagine replacing hours of repetitive HR and ops support with a single chatbot—one that knows every fleet policy, SOP, and HR guideline your team could ask for. Now imagine it works 24/7, never gets tired, and actually understands your employees’ questions.
Client
A USA-based tech-enabled delivery company
Industry
Logistics & Last-Mile Delivery
Timeline
4 months
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Quick Snapshot
A leading tech-enabled delivery company in the USA struggled to manage policy-related queries across a vast fleet operation. Their HR and operations teams were buried in redundant questions, while employees fumbled through outdated folders for simple SOPs.
We deployed a custom Retrieval-Augmented Generation (RAG) chatbot that instantly answered queries, reduced HR workload by 75%, and turned their unstructured chaos into searchable knowledge.
Brief
Managing a large delivery fleet across multiple states isn’t just about moving packages—it’s about people, policies, and keeping everyone aligned.
The client had invested heavily in digital tools, but their internal knowledge was scattered across emails, PDFs, intranet pages, and cloud folders.
Simple queries like “What’s the late delivery penalty?” or “Can I claim mileage for XYZ?” became multi-hour scavenger hunts. Employees often relied on outdated documents or conflicting information, creating compliance risks.
The bottleneck? HR and ops teams acting as human search engines—manually finding, forwarding, or copy-pasting answers dozens of times a day.
The client needed a smarter way to deliver consistent answers, without adding headcount.
Challenges
Information Access Bottlenecks
Policy documents were scattered, unstructured, and impossible to retrieve quickly.
Overworked HR & Ops Teams
Routine queries consumed most of their daily bandwidth, leaving little time for strategic work.
Inconsistent Knowledge Delivery
Different teams gave different answers, fueling confusion and compliance risks.
Solution
We built a custom RAG-based chatbot that transformed scattered files into a searchable, secure knowledge hub.
Smart Document Parsing & Vector Embedding
Ingested and chunked 500+ SOPs, policies, and manuals into a secure vector store (FAISS/OpenSearch).
LLM-Powered Semantic Search
Integrated with OpenAI/Amazon Bedrock for contextual, high-accuracy responses across HR, fleet, and operations.
Role-Based Access Control
Ensured sensitive data stayed restricted to authorized personnel.
Slack & Intranet Integration
No new apps to learn—employees accessed answers in the tools they already used.
Admin Dashboard
Gave HR/IT teams analytics, usage insights, and a feedback loop for continuous improvement.
Technology Used
- AI Frameworks & Logic Layer – LangChain/LangGraph with LLMs (Llama, Claude) and prompt engineering for precise responses.
- Search & Retrieval – AWS OpenSearch, embedding models, and Redis Cache for lightning-fast, accurate results.
- Cloud AI Infrastructure – AWS Bedrock powering Knowledgebase, RAG, and Agents, with AWS Evaluate Models for tuning.
- Security & Compliance – AWS Guardrails, AWS IAM, and public/private key encryption for data safety.
- Data & Storage – AWS RDS, AWS S3, and custom MongoDB schema for structured and unstructured data.
- Deployment & Scalability – Python (FastAPI) backend with AWS CodeDeploy for zero-downtime updates.
Impact
If your teams are spending hours answering the same questions, it’s time to automate the answer.
Let’s talk about building your own intelligent knowledge layer—with zero extra headcount.
Our Solutions in Action
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Lets Talk
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