Insights & Trends

Aviation maintenance records piling up? Managing them manually is a compliance risk—and a waste of time. Learn how to digitize, organize, and retrieve records efficiently while meeting FAA & NARA mandates. Read the guide now!

Safety in aviation depends on more than just protocols—it’s about accurate, verified documents. Explore how document verification supports FAA compliance and the future of aviation safety.

AI is transforming inventory management in aviation. Discover the must-have features every supply chain manager needs to optimize operations and cut costs.

AI is transforming MRO inventory management. Learn how ePlaneAI’s innovative solutions can reduce downtime, optimize costs, and keep operations running smoothly.

Introduction: Aviation’s Document Deluge and the Accuracy Imperative

The aviation industry is inundated with critical documents – Airworthiness Certificates, Illustrated Parts Catalogs (IPCs), maintenance manuals, FAA Service Bulletins/Airworthiness Directives, logbooks, and more. These unstructured, high-volume documents are the lifeblood of aviation operations and compliance. For example, a single U.S. commercial aircraft can produce up to 7,500 pages of new documents per year to meet DOT and FAA requirements. Ensuring that AI systems can reliably interpret and utilize this mountain of data is non-negotiable. In building aviation-grade AI, one principle stands out: the quality of AI outputs is only as good as the accuracy of the underlying data extraction. In other words, if your document data extraction is flawed, even the most advanced AI model will propagate those errors – a classic “garbage in, garbage out” scenario. AI leads and technical teams must therefore prioritize high-precision document data extraction as the foundation of any aviation AI pipeline.

Maintenance, Repair and Overhaul (MRO) scheduling in aviation and manufacturing involves allocating skilled technicians, tools, parts, and hangar space to maintenance tasks under tight time constraints. Traditional methods (manual or legacy ERP planning) struggle with unpredictable breakdowns and diverse task requirements . In today’s “smart era,” AI-driven scheduling systems consider a wide range of variables – technician skills, certifications, location, parts availability, etc. – to create optimal work plans . For example, modern AI schedulers “consider countless variables — skills, certifications, location, parts availability — to create the most efficient plan,” learning from past jobs to optimize future schedules . Schedule AI applies this concept to MRO by continuously analyzing live data and using machine learning to predict, allocate, and optimize maintenance tasks in real time .

Data Engineering and Preparation for Inventory AI

Effective Inventory AI starts with a robust data pipeline. All relevant data from enterprise systems and external sources must be aggregated, cleaned, and transformed for AI consumption. This includes inventory data (historical sales, current stock levels, part attributes) and demand drivers (market trends, maintenance schedules, promotions, etc.) . By integrating internal ERP records with external factors (e.g. industry trends or seasonal patterns), the model gains a comprehensive view of demand influencers . Key steps in the data pipeline typically include:

Continuous Data Updates: Inventory AI is not a one-off analysis – it continuously learns. Data pipelines are scheduled to update frequently (e.g. daily or hourly), feeding new transactions (sales, shipments, RFQs, etc.) into the model. This ensures the AI always bases decisions on the latest state of the inventory and demand. Automated data quality checks and monitoring are in place to catch anomalies in the input data, so that garbage data doesn’t lead to bad predictions. In summary, a solid foundation of integrated, clean data in the cloud enables the AI models to perform optimally and adapt to changes over time.

Introduction

Accessing complex aviation data should be as simple as asking a question. AeroGenie is an advanced aviation-domain natural language-to-SQL system that allows analysts and executives to query vast aviation databases by simply phrasing questions in everyday language. Built by a ePlane AI team of MIT-level engineers with assistant product experience, AeroGenie bridges the gap between human language and aviation data, translating ambiguous questions into precise SQL queries on the fly. The result is an experience akin to conversing with a smart colleague or voice assistant, except this “colleague” instantly writes perfect SQL and retrieves the answer — even from thousands of aviation records — in under a second.

AOG delays cost the industry billions of dollars every year. AI-powered solutions predict failures before they happen, source parts instantly, and drastically cut maintenance downtime.

Not all new aircraft parts are created equal. Condition codes help determine traceability, usability, safety, compliance, and more. Here's what you need to know.

At MRO Americas 2025, ePlaneAI announced its transformation from a digital marketplace to a full-suite provider of AI-powered aviation solutions. The company introduced its new AI-driven platform, featuring tools like AeroGenie, Email AI, Inventory AI, and Document AI, designed to optimize procurement, inventory management, and maintenance workflows in real-time. These innovations aim to enhance efficiency, accuracy, and regulatory compliance for airlines, MROs, and suppliers. ePlaneAI also released a whitepaper on optimizing aerospace data with AI, emphasizing how automation can accelerate operations and improve supply chain resilience.

Flight delays cost billions. ePlaneAI integrates AI with ERP systems to prevent downtime, optimize inventory, and boost efficiency in aviation like never before.

Parts Analyzer is a cutting-edge global data aggregation service tailored to the aviation industry. It continuously collects and analyzes aviation parts information from across the internet, providing a real-time view of global supply and demand trends for top parts by condition and location. This platform essentially creates a “global puzzle”of the parts marketplace by assembling countless data points into one comprehensive picture. Key aspects of its data collection include:

By piecing together all these bits of information, Parts Analyzer offers an unparalleled perspective on the aviation parts landscape. This comprehensive view is crucial for informed decision-making, helping users spot opportunities and risks that would be invisible without a global data lens. Like other leading AI-driven platforms, we process an enormous breadth of data – but with a specialized aviation focus – to extract truly actionable intelligence from the noise.

Introduction: Aviation businesses deal with a high volume of emails every day – from parts RFQs (Requests for Quote) and inventory updates to support inquiries and warranty claims. Managing these manually is slow, labor-intensive, and prone to error. ePlaneAI’s Email AI solution addresses this by acting as an AI-powered agent behind your Outlook/Office 365 mailbox, automating email workflows with remarkable speed and accuracy. By leveraging Microsoft Graph API integration, custom aviation-trained language models, and a powerful orchestration engine, Email AI can parse incoming messages (and attachments), extract key data, check it against inventory or other systems, and instantly craft precise replies – all while ensuring enterprise-grade privacy and compliance. This not only accelerates response times dramatically (up to 90% faster, boosting conversion rates by ~25%), but also relieves your team from repetitive tasks so they can focus on high-value work.

Vector databases index high-dimensional embedding vectors to enable semantic search over unstructured data, unlike traditional relational or document stores which use exact matches on keywords. Instead of tables or documents, vector stores manage dense numeric vectors (often 768–3072 dimensions) representing text or image semantics. At query time, the database finds nearest neighbors to a query vector using approximate nearest neighbor (ANN) search algorithms. For example, a graph-based index like Hierarchical Navigable Small Worlds (HNSW) constructs layered proximity graphs: a small top layer for coarse search and larger lower layers for refinement (see figure below). The search “hops” down these layers—quickly localizing to a cluster before exhaustively searching local neighbors. This trades off recall (finding the true nearest neighbors) against latency: raising the HNSW search parameter (efSearch) increases recall at the cost of higher query time .

The Aviation Supply Chain Portal is essentially a private e‑commerce platform tailor-made for aviation suppliers and their customers . Designed exclusively for airlines, MROs, and parts distributors, it centralizes inventory, procurement, and supplier collaboration into one secure system . In practice, an OEM or parts distributor “white‑labels” this portal and invites its approved buyers (airlines, MROs, etc.) to log in. These buyers see a full catalog of parts (synced in real time from the seller’s ERP) and can search, filter, and compare items just as they would on a large online marketplace . Unlike open public exchanges, however, this portal is private – only the one supplier (with many buyers) is on the platform, giving the company complete control over pricing, stock, and user access .

Cut delays and boost efficiency in aircraft parts procurement. Explore the top 5 trends for 2025 to reduce lead times for aircraft parts procurement.

Aircraft maintenance software + AI = unstoppable efficiency. Slash cuts, reduce downtown, optimize MRO practices, and improve predictive maintenance and forecasting capabilities. Find your perfect software + AI combo.