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Project context

Logistics Knowledge Retrieval Layer

Knowledge layer for logistics teams that answers operational questions from internal documentation with source-backed responses, so staff stop losing time in manuals, folders, and colleague escalations.

Working pilotLogistics operation with complex internal procedures2025

Client identity kept confidential

Soft macro of layered paper — depth of accumulated knowledge
84%
Answer relevance
Measured
~150
Documents indexed
Observed
8
Pilot users
Observed

Evidence

Measured with eight pilot users across approximately 150 indexed documents. Broader production rollout was the proposed next phase.

The challenge

The solution

Technology stack

Qdrant + Azure OpenAILangGraph RAG Agent

The result

The pilot group rated 84% of answers as useful or correct, and users found answers in under 30 seconds for questions that previously took 15 to 20 minutes of searching or internal escalation.

For the operation, that means less dependency on the same experienced colleagues and less time disappearing into procedural lookup work. That is why the next step was obvious: expand coverage and roll it out more broadly.

Next step

Want to explore where this could land first in your operation?

Start with one concrete process. In the AI Opportunity Scan we assess where AI adds value and what the best first route looks like.

Logistics Knowledge Retrieval Layer Case Study | Laava