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How to Use AeroGenie to Streamline Procurement Reporting in Aviation
August 27, 2025
What makes AI tools understand human language? It’s not magic—it’s NLP. Learn how NLP works, where it's going, and how it's changing the way we query and build reports with AI.
Procurement reporting in aviation has never been easy. Between disjointed ERP systems, cryptic data fields, and the sheer volume of supplier and part-level information, reporting teams spend more time wrangling data than analyzing it. But AI reporting tools like AeroGenie—a natural language-driven SQL assistant and intelligent report builder—is changing that.
In this guide, we’ll explore how AI reporting tools simplify procurement for aviation manufacturers, MRO shops, and procurement teams. You’ll learn how it removes friction from data querying, helps analysts and non-technical users access clean data, and cuts the time it takes to generate production-ready reports.
We’ll also break down how AeroGenie works under the hood, examining how it compares to legacy tools and other NL2SQL systems, as well as what it means for the future of AI in aviation intelligence.
The aviation procurement reporting problem
The aviation industry depends on precise, timely, and often highly customized procurement reports. Buyers need to track costs across global vendors. Engineers need part usage stats tied to aircraft models. Finance needs to reconcile procurement budgets with invoices. And executives need high-level summaries they can trust during volatile times.
Yet for all this complexity, the tools available often fall short. Most aviation companies rely on massive SQL (Structured Query Language) databases—often across multiple systems—that require deep technical knowledge to navigate. Creating a simple report might take days or weeks as analysts write, test, and revise SQL queries, only to have a stakeholder ask for a new data slice that breaks the whole thing.
It’s a system built for friction:
- SQL bottlenecks that only a few team members can resolve.
- Nonstandard vendor data formats that increase cleaning time.
- No simple way to automate custom metrics or visualize trends across procurement categories.
ePlaneAI’s AeroGenie eliminates these blockers by letting teams generate SQL queries in everyday language, validate ambiguity, and build polished reports—all in just a few clicks.
What is AeroGenie and how does it work?
AeroGenie is an AI-powered SQL assistant and intelligent report builder designed specifically for aviation teams. Instead of manually writing complex SQL queries, users type natural language questions like “Show me total parts ordered by Boeing in Q1” or “Break down procurement costs by supplier region.”
AeroGenie then translates these requests into accurate SQL, runs the query, and returns clean, formatted results—complete with graphs, charts, or exportable tables.
At the heart of AeroGenie is a natural language to SQL (NL2SQL) engine that combines language modeling, schema awareness, and iterative clarification prompts. This means it doesn’t just guess at what users want—it asks follow-up questions when necessary to eliminate ambiguity. For example, if a user says “Get me monthly engine spend,” but the schema has both engine_purchase_cost and engine_service_cost, AeroGenie will ask the user to clarify which one to use.
This context-first approach makes AeroGenie especially powerful in aviation, where databases often contain multiple similarly named fields and custom metrics vary by department.
Key features built for aviation procurement teams
AeroGenie isn’t just a chatbot glued to a SQL interface. It’s a full procurement intelligence layer that understands the needs of aerospace manufacturing, MROs, and logistics teams.
Here’s what sets it apart:
- Intelligent query clarification: AeroGenie uses AI-powered ambiguity checks to spot underspecified requests and prompt users for details before generating SQL.
- Schema-aware search: It “knows” your table names, column headers, and joins, so you don’t have to. It auto-maps fuzzy or abbreviated user input to exact schema terms.
- Custom metric modeling: Procurement teams often rely on internally defined KPIs (Key Performance Indicators), like cost-per-flight-hour or vendor delay penalty rate. AeroGenie can be trained to recognize and calculate these.
- No-code report builder: Once a query runs, AeroGenie generates visuals and tabular outputs you can export or embed—no BI (Business Intelligence) platform needed.
- Secure audit logs: Every query and edit is logged, so finance teams and auditors can verify exactly how each number was pulled.
Why NL2SQL is exploding in enterprise tools
Natural language to SQL (NL2SQL) is becoming common in enterprise data tool sets, as it democratizes access to complex data—without requiring every team member to be fluent in SQL.
In large aerospace organizations, procurement, or finance info often lives across sprawling relational databases. Historically, only SQL-savvy analysts could extract insights from these systems. NL2SQL removes this bottleneck by allowing anyone—from operations managers to supplier coordinators—to ask plain-language questions and get answers immediately.
The technology has gained steam thanks to two converging trends:
- The rise of generative AI and LLMs: Tools like Gemini, GPT, and Claude make it easier to parse natural language and understand user intent (Google Cloud).
- The cost and scale of modern data warehouses: With platforms like BigQuery, Snowflake, and Redshift, companies are ingesting more data than ever and need faster ways to query it at scale.
NL2SQL bridges the gap between technical power and non-technical users. AeroGenie takes this one step further by adding aviation-specific schema training and ambiguity resolution on top of standard LLM workflows.
The problem with off-the-shelf NL2SQL tools
While the promise of NL2SQL is huge, many tools fall flat when deployed in real-world production environments because generic solutions often lack the context and nuance needed for enterprise-grade querying.
Here are a few common failure points:
- Ambiguity in user input: Many NL2SQL engines struggle with underspecified questions like “Show me inventory status,” which could refer to any of a dozen fields across multiple tables. Without clarification, the tool guesses—and often guesses wrong.
- Schema mismatch: Off-the-shelf models are trained on open datasets or e-commerce schemas, not your proprietary aviation database. This leads to mismatched joins, missing filters, and inaccurate groupings.
- Lack of error feedback: When SQL generation fails, many tools just throw a syntax error or blank screen. They don’t ask follow-up questions or refine their logic based on user feedback.
AeroGenie addresses these gaps with built-in schema awareness, dynamic ambiguity checks, and an interactive prompt loop. Instead of one-and-done SQL generation, it’s a back-and-forth conversation with context at every step.
How AeroGenie enhances NL2SQL with schema awareness
One of the standout features of AeroGenie is its deep schema awareness. Unlike generic NL2SQL tools that attempt to map natural language to a vague, one-size-fits-all structure, AeroGenie is trained on your actual table definitions, business logic, and naming conventions.
Schema awareness means:
- AeroGenie understands your column names, table relationships, and business metrics.
- It knows that “on-time departures” likely refers to a specific column filtered by departure_status = 'On-Time'.
- It can disambiguate (resolve confusion) between similarly named fields across tables (e.g. flight_id in scheduled_flights vs completed_flights).
This level of understanding allows AeroGenie to produce SQL that’s not just syntactically correct but also semantically accurate so you get the right data the first time.
AeroGenie also uses vector search and metadata mapping to identify table aliases, join paths, and data lineage. In other words, it knows what the user probably meant, even if the input wasn’t perfect (Google Cloud).
Handling ambiguity: What makes AeroGenie different
Ambiguity is the silent killer of NL2SQL performance. If a user types “Show me Q2 performance,” how should the system know whether to show revenue, number of flights, fuel efficiency, or all of the above? If a generic tool isn’t guessing wrong, it will usually crash.
AeroGenie, however, treats ambiguity as a solvable problem.
Here’s how:
- Prompt engineering: AeroGenie uses few-shot examples to train the model to recognize vague input.
- Follow-up questioning: If a query lacks necessary detail, AeroGenie pauses and prompts the user for clarification. For instance, it might ask, “Do you want Q2 revenue, profit, or operating costs?”
- User feedback loop: The tool offers editable draft SQL and explanations so users can correct the assumptions without starting over.
- Embedded context: When generating SQL, AeroGenie loads in table metadata, business metric descriptions, and previously asked questions to improve intent matching.
By treating every interaction as part of a broader, ongoing dialogue, AeroGenie builds more accurate and more trusted reports over time.
Why business users love NL2SQL—and where it breaks
For business users like analysts, marketers, and operations managers, NL2SQL is a game-changer. Instead of waiting on data teams to write and revise queries, they can ask questions in plain English and get instant answers:
- “How many cargo delays did we have last week?”
- “What’s our average turnaround time for Route 3 in Q1?”
- “Which airport had the most missed connections in June?”
But it’s not a perfect solution. NLSQL has flaws and foibles when:
- The data contains inconsistent formats (“1k” vs. “1000” vs. “1,000.0”).
- Metrics are custom and complex (e.g., “weighted revenue per mile”).
- Users ask multi-step or underspecified questions.
- The NL2SQL tool doesn’t know your schema or joins.
Without domain-specific knowledge, most generic tools produce SQL that’s wrong, incomplete, or misaligned with the business question. AeroGenie solves this with schema training, feedback loops, and proactive ambiguity checks—bridging the gap between what users say or ask and what they mean.
Behind the scenes: How AeroGenie generates SQL
The process AeroGenie uses to generate SQL is deliberately multi-layered to reduce error and maximize trust.
Here’s how it works under the hood:
- Classifies the question: Is it a direct query, an ambiguous one, or a multi-step analysis? The routing agent decides.
- Loads context: It pulls schema metadata, business logic, recent queries, and vector-embedded examples into the prompt.
- Generates a draft SQL: Using Gemini and fine-tuned LLM (Large Language Model) logic, AeroGenie writes an initial query.
- Performs checks: It evaluates whether the SQL aligns with intent and syntax. If not, it enters a clarification loop.
- Prompts the user (if needed): The user might see, “Which metric would you like to use for 'performance'?”
- Executes and summarizes: Once the SQL is finalized, AeroGenie runs it and returns a plain-English explanation of the results.
This workflow mirrors Google's own approach to NL2SQL in BigQuery and Gemini, where feedback refinement, semantic search, and contribution analysis models work together to support even complex data questions (Google Cloud).
Tackling ambiguous, underspecified, and complex queries
NL2SQL works beautifully when questions are clear and data is simple. But this conflation is rate. Many analysts don’t know what they’re looking for when examining the data, and most business questions fall into three tricky categories:
- Ambiguous: “How many orders shipped last quarter?” – Which region? Domestic or international? All product categories?
- Underspecified: “Show me return rate by team.” – What team? Which time period? Which return type?
- Complex: “What caused our Q2 margins to drop in the Southeast?” – This requires multi-step reasoning, not a single SQL query.
These queries can’t be solved with brute force LLM power alone. AeroGenie handles them using a combination of:
- Routing agents to classify question types
- Vector search to retrieve similar resolved queries and schemas
- Clarification loops that ask follow-up questions before generating SQL
- Contribution analysis models to break down key drivers of change
The power of feedback loops and user training
One of the most overlooked features of NL2SQL systems is collaborative feedback. Getting SQL right, as with generative AI models, is iterative.
AeroGenie incorporates real-time feedback loops at several points:
- Before query generation: It may prompt, “Which date column should we use—shipment_date or order_date?”
- After draft SQL creation: It asks, “Does this match what you expected?”
- After results are returned: Users can rate the output or flag errors.
These signals train the model over time to align with your data, business logic, and company-specific vocabulary (e.g., “CPM” might mean something different in marketing vs. operations).
Even technical users benefit, as they can skip repetitive boilerplate queries and focus on refining logic, improving dashboards, or optimizing performance.
Why your data quality will make or break NL2SQL
No matter how advanced your natural language interface is, it’s only as smart as the data underneath. If your data is messy, inconsistent, or lacks context, NL2SQL will either fail outright or produce hallucinate answers that look right, but aren’t.
Common landmines:
- Inconsistent formatting: 'Male', 'male', and 'M' all mean the same thing, but the model won’t know that unless it’s trained or cleaned.
- Acronym soup: Every company has undocumented shorthand. NL2SQL can’t guess what “FRC margin” or “NRR” means without context or annotation.
- Poor table joins: If your schema isn’t designed for relational clarity, NL2SQL will generate inaccurate or broken queries.
- Custom business logic: Your company’s way of calculating CAC (Customer Acquisition Cost), churn, or sales velocity? That’s not in the model unless you build it in.
AeroGenie supports these gaps through data profiling, auto-suggested views, and schema-level metadata enrichment, but your internal data team will still need to invest in:
- Controlled vocabulary
- Consistent naming conventions
- Simplified, joinable views for each department’s use cases
Going beyond dashboards: Use cases that matter
NL2SQL unlocks a range of self-serve analytics use cases that traditional dashboards struggle with, especially non-technical teams.
Here are a few examples:
- Customer success: “Show me churn risk for customers with <3 logins in the last 30 days and an open support ticket.”
- Sales ops: “What’s the average deal size for Q3 in the mid-market segment, compared to Q2?”
- Marketing: “Which campaigns had the best ROI in terms of LTV:CAC ratio this year?”
And with features like contribution analysis, AeroGenie can go even deeper. For example, if revenue dropped, it can help answer why by analyzing regional performance, product categories, and customer cohorts without manual slicing and dicing (Google Cloud).
The Gemini + BigQuery advantage
While many NL2SQL tools promise convenience, few are built on infrastructure that can scale with enterprise complexity. That’s where Google’s Gemini models and BigQuery ecosystem shine.
Gemini Flash 1.5 acts as a routing agent, classifying questions by complexity and determining how to handle them, whether through simple SQL generation, contribution analysis, or ambiguity resolution (Google Cloud).
Meanwhile, BigQuery does the heavy lifting:
- Stores question-SQL training pairs with vector embeddings
- Uses native vector search to retrieve semantically similar questions
- Executes validated SQL at scale
- Powers contribution models that highlight what changed and why
This combination enables real-time querying, model refinement, and explainable insights—all without hand-coding.
It also future-proofs your analytics stack: As Gemini improves, your NL2SQL workflows get smarter without needing to retool your infrastructure.
Use cases: NL2SQL shines across all departments
NL2SQL solves real problems across all departments, not just BI. Here are a few powerful use cases:
- Marketing & sales ops: “What was our CAC by channel last month?”
- Finance: “Show me all expenses over $5K in Q2 across APAC.”
- Product teams: “How many users dropped off between steps 2 and 3 of onboarding?”
- Customer success: “Which accounts have open support tickets and haven’t logged in this month?”
Traditionally, answering these would require waiting on a data team, sifting through dashboards, or understanding schema relationships. NL2SQL changes that by acting as a translator for non-technical users.
AeroGenie’s contribution analysis further helps business teams by surfacing insights they didn’t even know to look for. For example, AeroGenie can pinpoint that most churn last quarter came from customers on a specific mobile OS version.
What’s next: Analytics for all, at scale
The goal is both faster queries and broader access.
The goal isn’t just faster queries—it’s wider access. With tools like AeroGenie, we’re inching toward a future where:
- Business teams don’t need to wait in a data request queue.
- Analysts focus on strategy, not syntax.
- Insights are surfaced before stakeholders even think to ask.
NL2SQLis changing the culture of data. Instead of data engineers and gatekeepers, anyone in the organization can understand business performance.
As more and more companies are integrating vector search, contribution analysis, and routing agents, NLP insights will eventually become widespread.
Ready to eliminate SQL bottlenecks and modernize your procurement reporting? Try AeroGenie and see how fast, accurate, and intuitive NL2SQL can be—customized for your aviation data and your team’s real-world workflows. Book a demo with ePlaneAI today →
Glossary of NLP and NL2SQL terms
Here’s a quick glossary to help decode the jargon, with a rating for how essential each is to understanding the tech:
Ambiguity check
Definition: A process where the system asks for clarification if a question could have multiple meanings.
Example: You ask, “Show me Airbus data.” The AI might reply, “Do you mean aircraft delivery numbers or maintenance reports?”
Why it matters: Keeps decisions based on clear intent, especially when terms have multiple meanings in aviation (e.g., “landing” vs. “landing rights”).
BigQuery
Definition: A fully managed, serverless data warehouse from Google Cloud that allows users to run fast, SQL-based queries on large datasets using built-in machine learning and analytics tools.
Example: AeroGenie uses BigQuery to store question-SQL training pairs, run vector searches, and execute final SQL queries at scale.
Why it matters: BigQuery provides the speed, scalability, and infrastructure for enterprise-grade NL2SQL tools, enabling real-time analytics, contribution modeling, and seamless LLM (Large Language Model) integration (Google Cloud).
Contribution analysis
Definition: A statistical method to identify which factors contributed most to a result or change in data.
Example: Noticing a spike in fuel costs, this tool could break it down: 40% jet fuel price hike, 30% flight delays, 20% route changes.
Why it matters: Pinpoints what’s driving cost or performance trends across the fleet.
Contribution model
Definition: A machine learning model (like those in BigQuery ML) used to analyze how variables affect business metrics.
Example: Helps determine if flight route changes or staffing shortages are more to blame for on-time performance drops.
Why it matters: Explains the “why” behind KPI changes.
Few-shot learning
Definition: Teaching an AI model to perform a task by showing it just a few examples.
Example: You type “Show engine hours for past 3 months,” and after correcting it twice, the AI learns what format you want.
Why it matters: Reduces training time and improves AI’s personalization for each company.
Join clause
Definition: A SQL operation that connects data from two or more tables based on shared information.
Example: Linking pilot schedules with aircraft maintenance records to see how downtime affects staffing.
Why it matters: Essential for cross-functional insights, like combining ops and HR data.
Large language model (LLM)
Definition: A type of AI trained on massive text data to understand and generate human language, like Gemini or GPT.
Example: It can turn “Which routes had the most cancellations last month?” into an actual database query.
Why it matters: LLMs are the brains behind Document AI’s ability to understand stakeholder questions.
Metadata
Definition: Data that describes other data, providing context and meaning to database fields, tables, and relationships. In aviation procurement reporting, this might include column descriptions, data types, or business rules tied to each field.
Example: AeroGenie uses metadata mapping to understand that departure_status = 'On-Time' is linked to on-time performance metrics.
Why it matters: Metadata helps NL2SQL tools interpret user intent correctly, resolve ambiguities, and generate accurate queries without requiring users to know every table or column name (Google Cloud).
Named entity recognition (NER)
Definition: A method where the AI identifies specific things like names of people, places, or companies.
Example: In a support log, it spots “Boeing 737” as a model, “Dallas” as a location, and “GE” as an engine vendor.
Why it matters: Powers smart filtering and tagging of long text documents.
Natural language processing (NLP)
Definition: The field of AI that helps computers understand human language (IBM | https://www.ibm.com/think/topics/natural-language-processing).
Example: Lets staff ask, “Show me all aircraft overdue for inspection,” instead of needing to write SQL.
Why it matters: Core to making complex data accessible to non-technical staff.
NL2SQL (natural language to SQL)
Definition: Technology that converts a plain-English question into a structured SQL database query.
Example: “Which routes lost the most revenue last quarter?” becomes a precise query in your operations database.
Why it matters: Enables natural conversation with enterprise data systems.
Prompt engineering
Definition: Crafting better questions or inputs to get accurate responses from an AI.
Example: Instead of “Revenue by route,” saying “Monthly revenue breakdown by flight route, Q2 2024.”
Why it matters: Improves AI output without needing to touch code.
Routing agent
Definition: A tool that decides how to handle your request—basic SQL lookup vs. advanced analysis.
Example: It might route “What’s our CO₂ footprint?” to an emissions model, not just a SQL database.
Why it matters: Optimizes which backend tools respond for best accuracy.
Schema
Definition: The blueprint of your database. It guides how tables exist, what’s in them, and how they connect.
Example: Like a blueprint showing where pilot names, aircraft IDs, and flight dates live across your systems.
Why it matters: AI needs the schema to understand how to pull the right data.
Semantic search
Definition: Searching based on meaning, not just keywords—often using AI embeddings.
Example: Typing “recent maintenance delays” surfaces records even if the exact phrase isn’t used.
Why it matters: Retrieves more relevant info, even with fuzzy input.
Syntactic rigidity
Definition: SQL has strict rules; a missing comma or typo can break the whole query.
Example: “SELECT * FROM Aircraft WHERE status = ‘delayed” will fail without the closing quote.
Why it matters: Highlights why NL2SQL is so useful—it removes that fragility.
Tokenization
Definition: Splitting text into parts (words, subwords, etc.) so AI can process it.
Example: “Runway closed” gets split into “runway” and “closed” so the AI can understand it.
Why it matters: Foundation of how LLMs read and analyze text.
Vector embedding
Definition: Turning text into math so AI can measure meaning and similarity (Google Cloud).
Example: “Fuel consumption” and “burn rate” mean similar things—embeddings help the AI know that.
Why it matters: Enables smart matching, ranking, and retrieval across documents.
Vector search
Definition: A method of retrieving information based on semantic meaning rather than exact keyword matches. It compares text using mathematical representations (vector embeddings) to find the closest match in meaning.
Example: A user types “engine purchase spend,” and the AI surfaces a field labeled “powerplant_acquisition_cost” because the concepts are similar—even if the words aren’t an exact match.
Why it matters: Vector search helps NL2SQL tools like AeroGenie understand what users mean, not just what they say, improving query accuracy even when phrasing is vague or inconsistent (Google Cloud).
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