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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 Their HR and operations teams were buried in redundant questions, while employees fumbled through outdated folders for simple SOPs.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.
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 Employees often relied on outdated documents or conflicting information, creating compliance risks.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.
Policy documents were scattered, unstructured, and impossible to retrieve quickly.
Routine queries consumed most of their daily bandwidth, leaving little time for strategic work.
Different teams gave different answers, fueling confusion and compliance risks.
We built a custom RAG-based chatbot that transformed scattered files into a searchable, secure knowledge hub.
Ingested and chunked 500+ SOPs, policies, and manuals into a secure vector store (FAISS/OpenSearch).
Integrated with OpenAI/Amazon Bedrock for contextual, high-accuracy responses across HR, fleet, and operations.
Ensured sensitive data stayed restricted to authorized personnel.
No new apps to learn—employees accessed answers in the tools they already used.
Gave HR/IT teams analytics, usage insights, and a feedback loop for continuous improvement.
Let’s talk about building your own intelligent knowledge layer—with zero extra headcount.
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