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Boston Aerospace AI
Predict with precision. Explain with clarity.
OpenAI-powered predictive maintenance for aircraft engines: 6-model ML ensemble predicts Remaining Useful Life, SHAP explains why, and an AI orchestrator with live Web Search investigates further — turning raw predictions into source-attributed engineering answers, built entirely solo.
Boston Aerospace AI v1.0.0 – Official Launch I'm a self-taught 19-year-old developer from Chimoio, Mozambique, and I built Boston Aerospace AI entirely on my own. This is its first stable release: an OpenAI-powered intelligence layer for aerospace predictive maintenance.
The Origin Story I didn't come into this from inside the aerospace industry — I came into it from the opposite direction. My first "lab" was a dumping site behind repair workshops in Chimoio. My first sensor was salvaged from a broken DVD drive. I taught myself machine learning through PDFs downloaded whenever I had internet access, coding in Termux between power outages. The idea behind Boston Aerospace AI came from a simple observation: predictive models can tell you a component's Remaining Useful Life with real accuracy, but they can't tell you why it's degrading or what to investigate next. SHAP helps — but raw feature-importance numbers still aren't an answer an engineer can act on. So I set out to build a system that doesn't replace predictive models, but wraps them in something that can actually explain and investigate.
The Problem We're Solving Black-Box Predictions — ML models output RUL estimates and anomaly scores, but not the "why." Information Silos — sensor data, technical manuals, and external research live in disconnected places. Time Pressure — in aerospace, every hour spent manually investigating an alert is an hour not spent preventing failure.
How Boston Aerospace AI Addresses This Foundation: Trusted ML Models The core is a 6-model ML ensemble (XGBoost, LightGBM, CatBoost, Random Forest, GBR, Extra Trees) trained on NASA's CMAPSS turbofan dataset to estimate Remaining Useful Life. Transparency: SHAP Explainability Every prediction comes with SHAP analysis showing exactly which sensor signals drove it. Intelligence: OpenAI Orchestrator This is the new piece in v1.0.0. The orchestrator takes the ML prediction and SHAP explanation and enriches them with: Internal technical documentation (manuals, service bulletins) — when available. External web research — when the question needs it (the model decides per-query, not on every message). It synthesizes all of this into a structured answer to three questions: What is happening? Why is it happening? What should I investigate?
Source-Aware Analysis Every part of the answer is tagged by origin — 🟢 ML Prediction, 🟡 SHAP Explanation, 🔵 Internal Document, 🔴 External Research — computed by the system itself, not self-reported by the model, so the attribution is reliable.
From Prototype to Platform Phase 1 — Local prototype. I originally built the assistant on Ollama, running fully offline/locally — important given inconsistent connectivity where I live. Phase 2 — OpenAI integration (this release). I rebuilt the orchestration layer on OpenAI's Responses API with native web search and structured output, which meaningfully expanded reasoning depth and made source-aware synthesis possible. The Ollama/local path stays in the codebase as a future option for operators who need fully offline, air-gapped deployments.
Important Disclaimer Boston Aerospace AI is a research and decision-support prototype. It is not certified for autonomous maintenance decisions and must never replace qualified aerospace engineers, approved maintenance procedures, or regulatory requirements (FAA, EASA, etc.). It's a collaborative assistant that helps engineers investigate faster — the final decision always rests with a human engineer.
Technical Architecture Código 📊 What's New in v1.0.0 ✅ OpenAI-powered AI Orchestrator with native Web Search ✅ Predictive Remaining Useful Life (RUL) estimation ✅ SHAP-based explainability ✅ Optional internal technical-document analysis (RAG) ✅ Structured, source-attributed responses ✅ Computer vision modules (crack detection, thermal analysis, vibration anomaly) ✅ Automated PDF maintenance reports
We Need Your Feedback This is a solo, self-funded project, still early. If you try it, break it, or have ideas for what would make it genuinely useful to real maintenance teams, I want to hear it — that feedback is how this gets better.
What's Next Validation with real (non-synthetic) operational data Integration with additional data sources (fleet history, weather, supply chain) Custom fine-tuning for specific engine types Working toward compliance/certification readiness Built alone, from Mozambique, with more curiosity than resources. Thanks for checking it out. 🛩️
About Boston Aerospace AI on Product Hunt
“Predict with precision. Explain with clarity.”
Boston Aerospace AI was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #150 on the daily leaderboard. OpenAI-powered predictive maintenance for aircraft engines: 6-model ML ensemble predicts Remaining Useful Life, SHAP explains why, and an AI orchestrator with live Web Search investigates further — turning raw predictions into source-attributed engineering answers, built entirely solo.
On the analytics side, Boston Aerospace AI competes within SaaS, Developer Tools, Artificial Intelligence, GitHub and OpenAI Day — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Boston Aerospace AI performed against the three products that launched closest to it on the same day.
Who hunted Boston Aerospace AI?
Boston Aerospace AI was hunted by Fernando Artur. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Boston Aerospace AI including community comment highlights and product details, visit the product overview.
Fernando Artur
Boston Aerospace AI v1.0.0 – Official Launch
I'm a self-taught 19-year-old developer from Chimoio, Mozambique, and I built Boston Aerospace AI entirely on my own. This is its first stable release: an OpenAI-powered intelligence layer for aerospace predictive maintenance.
The Origin Story
I didn't come into this from inside the aerospace industry — I came into it from the opposite direction. My first "lab" was a dumping site behind repair workshops in Chimoio. My first sensor was salvaged from a broken DVD drive. I taught myself machine learning through PDFs downloaded whenever I had internet access, coding in Termux between power outages.
The idea behind Boston Aerospace AI came from a simple observation: predictive models can tell you a component's Remaining Useful Life with real accuracy, but they can't tell you why it's degrading or what to investigate next. SHAP helps — but raw feature-importance numbers still aren't an answer an engineer can act on.
So I set out to build a system that doesn't replace predictive models, but wraps them in something that can actually explain and investigate.
The Problem We're Solving
Black-Box Predictions — ML models output RUL estimates and anomaly scores, but not the "why."
Information Silos — sensor data, technical manuals, and external research live in disconnected places.
Time Pressure — in aerospace, every hour spent manually investigating an alert is an hour not spent preventing failure.
How Boston Aerospace AI Addresses This
Foundation: Trusted ML Models
The core is a 6-model ML ensemble (XGBoost, LightGBM, CatBoost, Random Forest, GBR, Extra Trees) trained on NASA's CMAPSS turbofan dataset to estimate Remaining Useful Life.
Transparency: SHAP Explainability
Every prediction comes with SHAP analysis showing exactly which sensor signals drove it.
Intelligence: OpenAI Orchestrator
This is the new piece in v1.0.0. The orchestrator takes the ML prediction and SHAP explanation and enriches them with:
Internal technical documentation (manuals, service bulletins) — when available.
External web research — when the question needs it (the model decides per-query, not on every message).
It synthesizes all of this into a structured answer to three questions: What is happening? Why is it happening? What should I investigate?
Source-Aware Analysis
Every part of the answer is tagged by origin — 🟢 ML Prediction, 🟡 SHAP Explanation, 🔵 Internal Document, 🔴 External Research — computed by the system itself, not self-reported by the model, so the attribution is reliable.
From Prototype to Platform
Phase 1 — Local prototype. I originally built the assistant on Ollama, running fully offline/locally — important given inconsistent connectivity where I live.
Phase 2 — OpenAI integration (this release). I rebuilt the orchestration layer on OpenAI's Responses API with native web search and structured output, which meaningfully expanded reasoning depth and made source-aware synthesis possible. The Ollama/local path stays in the codebase as a future option for operators who need fully offline, air-gapped deployments.
Important Disclaimer
Boston Aerospace AI is a research and decision-support prototype. It is not certified for autonomous maintenance decisions and must never replace qualified aerospace engineers, approved maintenance procedures, or regulatory requirements (FAA, EASA, etc.). It's a collaborative assistant that helps engineers investigate faster — the final decision always rests with a human engineer.
Technical Architecture
Código
📊 What's New in v1.0.0
✅ OpenAI-powered AI Orchestrator with native Web Search
✅ Predictive Remaining Useful Life (RUL) estimation
✅ SHAP-based explainability
✅ Optional internal technical-document analysis (RAG)
✅ Structured, source-attributed responses
✅ Computer vision modules (crack detection, thermal analysis, vibration anomaly)
✅ Automated PDF maintenance reports
We Need Your Feedback
This is a solo, self-funded project, still early. If you try it, break it, or have ideas for what would make it genuinely useful to real maintenance teams, I want to hear it — that feedback is how this gets better.
What's Next
Validation with real (non-synthetic) operational data
Integration with additional data sources (fleet history, weather, supply chain)
Custom fine-tuning for specific engine types
Working toward compliance/certification readiness
Built alone, from Mozambique, with more curiosity than resources. Thanks for checking it out. 🛩️