Airline RAG Knowledge Assistant

RAG over aviation SOPs for precise operational knowledge retrieval.

OutcomeGrounded answers from aviation SOPs instead of manual PDF search

AIRAGPineconeOpenAIPythonDocument Intelligence

30 days

focused RAG build

30 days · 2-person team

Aircraft wing and aviation operations context — AI Systems · Aviation

The Challenge

A large airline had extensive SOP and operational documents for pilots and related teams. Finding precise answers meant manually searching long PDFs and structured documents.

What We Built

We partnered with Aviation enterprise · Anonymized to ship a production-ready AI platform for Aviation. We ingested PDF and SOP knowledge sources and created vectorized representations for retrieval. We implemented a RAG pipeline that retrieves specific source-backed knowledge before generating answers. We scoped the engagement as a focused partial project around the AI retrieval layer and web experience, not a full airline systems replacement.

How We Delivered

Ingested PDF and SOP knowledge sources and created vectorized representations for retrieval.

Implemented a RAG pipeline that retrieves specific source-backed knowledge before generating answers.

Built a Python web application with OpenAI models and Pinecone for streaming, domain-specific responses.

Scoped the engagement as a focused partial project around the AI retrieval layer and web experience, not a full airline systems replacement.

Scope of Work

SOP and PDF ingestion with vector indexing
Source-grounded RAG answer pipeline
Python web app with streaming LLM responses
Focused 30-day AI retrieval-layer delivery

Tech Stack

Tools we deployed in production, not just POC'd.

PythonPineconeOpenAI

The Results

RAG

Grounded answers from SOP documents

Streaming

Real-time LLM responses for operators

Partial

Focused 30-day RAG implementation

Grounded answers from aviation SOPs instead of manual PDF search
RAG, Grounded answers from SOP documents
Streaming, Real-time LLM responses for operators
Partial, Focused 30-day RAG implementation

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