AeroCopilot
An AI-powered aviation knowledge assistant designed to help aviation professionals find, understand and retrieve information from complex technical documents faster.
Aviation knowledge is valuable. Finding it can be slow.
Aviation organisations work with large collections of technical manuals, maintenance procedures, operational policies, safety documents, engineering references, training materials and regulatory information.
When information is buried across hundreds or thousands of pages, finding the right answer can require significant manual searching. Traditional keyword search can also fail when the user's wording differs from the wording used in the source document.
Product question: How might we help aviation professionals retrieve relevant technical information faster without forcing them to manually search through large document collections?
Designed around people who work with aviation information.
Maintenance & Engineering
Primary users who frequently need to locate procedures, inspection information and technical guidance.
Operations & Safety
Teams that need to quickly retrieve operational policies, safety procedures and relevant documentation.
Analysts & Trainers
Professionals and learners who need faster access to structured knowledge across aviation documents.
Ask a question. Retrieve the evidence. Generate a grounded answer.
Product hypothesis
If aviation documents can be converted into searchable semantic representations, professionals may be able to retrieve relevant information faster than relying only on traditional keyword search.
Core user story
As an aviation professional, I want to ask a natural language question and receive a concise answer supported by the relevant source document.
Five capabilities define the first version.
01
Document Ingestion
Load aviation PDF documents and extract their text and page-level metadata.
02
Semantic Retrieval
Convert document content into embeddings and retrieve semantically relevant information.
03
RAG Generation
Use retrieved evidence as context for grounded generative AI responses.
04
Citations
Return source documents and page references alongside generated answers.
05
Abstention
Refuse to provide an unsupported answer when the available documents do not contain sufficient information.
Retrieval happens before generation.
AeroCopilot uses Retrieval-Augmented Generation rather than asking the language model to answer from its general knowledge alone.
A controlled synthetic aviation document collection.
Because aviation technical documentation can contain proprietary, confidential or safety-sensitive information, AeroCopilot uses synthetic documents created specifically for this prototype.
Document areas
- Maintenance procedures
- Aircraft operations
- Safety procedures
- Inspection procedures
- Hydraulic system inspection
- Hydraulic leakage
- Abnormal aircraft conditions
- Personal protective equipment
The LLM is grounded by retrieved evidence.
Retrieval layer
The retriever searches the vector database for document chunks that are semantically relevant to the user's question.
Generation layer
The Groq-powered language model receives the retrieved evidence and generates a concise response grounded in those documents.
AeroCopilot is intentionally instructed not to invent information. When the available documents do not provide enough evidence, the system abstains instead of presenting an unsupported answer as fact.
I tested retrieval and answer behaviour separately.
The evaluation dataset contains eight questions covering both answerable and intentionally unanswerable scenarios.
| Evaluation | Result | Meaning |
|---|---|---|
| Test cases | 8 / 8 | All evaluation cases passed. |
| Retrieval accuracy | 100% | Expected document sources were successfully retrieved in the test set. |
| Behavior accuracy | 100% | Answerable questions were answered while unsupported questions triggered abstention. |
These results are development-stage evaluation results on a small synthetic dataset. They are not evidence of production reliability or aviation safety performance.
The intelligence layer is exposed through FastAPI.
AeroCopilot includes a REST API that separates the retrieval and question-answering capabilities from the presentation layer. This creates a foundation for future applications and integrations.
/ask
Submit a natural language question and receive a grounded answer with citations.
/search
Search the aviation document collection directly without invoking the language model.
/documents
Return the documents currently available to the AeroCopilot knowledge base.
A conversational interface for aviation knowledge.
The Streamlit application provides a simple interface where users can ask aviation-related questions and receive grounded responses supported by retrieved document evidence.
Natural Language
Users ask questions using everyday language instead of manually constructing search queries.
Grounded Answers
Responses are generated using relevant retrieved document content.
Evidence
Retrieved sources and page information are surfaced so users can understand where the answer came from.
This wasn't just a chatbot.
AeroCopilot demonstrates an end-to-end AI product development workflow: identifying a problem, defining users, designing the MVP, creating controlled data, building the retrieval pipeline, integrating generative AI, exposing the system through an API, creating a usable interface and evaluating the resulting system.
Product
Problem framing, user definition, product hypothesis, MVP scope and responsible-use boundaries.
AI Engineering
Document ingestion, chunking, embeddings, vector search, RAG, LLM integration and citation handling.
Delivery
Streamlit prototype, FastAPI backend, automated tests, evaluation framework, GitHub repository and deployment.
Designed with clear boundaries.
What AeroCopilot does
It demonstrates how retrieval-augmented generation can help users navigate a controlled collection of aviation documents and surface relevant information.
What AeroCopilot does not do
It does not replace aviation professionals, make autonomous maintenance decisions, certify aircraft, approve procedures or provide safety-critical operational authorization.
Any future production implementation would require representative governed data, domain-expert validation, cybersecurity controls, model monitoring, auditability, appropriate human oversight and applicable regulatory review.
Built across the AI product stack.
Programming
Python · Pandas · NumPy
RAG
LangChain · Retrieval-Augmented Generation
Embeddings
Hugging Face · all-MiniLM-L6-v2
Vector Database
ChromaDB · Semantic Search
Generative AI
Groq API · LLM-powered responses
Application
Streamlit · FastAPI · Pytest
From prototype to validated aviation knowledge system.
The next stage would focus less on simply adding features and more on validating whether AeroCopilot genuinely improves the speed, accuracy and confidence with which aviation professionals retrieve information.
Better Evaluation
Expand the evaluation dataset and measure retrieval precision, groundedness, citation accuracy and answer quality.
Domain Validation
Conduct structured testing with aviation professionals using appropriately governed representative documents.
Enterprise Integration
Explore authentication, document permissions, audit logging and integration with enterprise knowledge systems.