Turn general-purpose AI into airline-specific intelligence.
Airline AI adapts existing LLMs to the needs of airlines, connects them with verified airline knowledge and operational systems, and delivers accurate, context-aware responses and recommendations.
A complete AI Intelligence Layer for airlines.
Airline AI combines model adaptation, real-time airline knowledge and system integration to make existing AI truly understand the airline business.
Airline-Specific AI
Adapts existing LLMs using SFT, LoRA and QLoRA to understand airline processes, terminology and customer needs.
Learn more →Live Airline Knowledge
Uses RAG, verified content and operational data to provide up-to-date, context-aware information.
Learn more →System Integration
Connects with airline systems through APIs and MCP/tool calling when operational context is required.
Learn more →Trusted & Explainable
Validates responses, follows airline rules and supports transparent, controlled recommendations.
Learn more →One intelligence layer. Multiple airline workflows.
Designed to support both customer-facing experiences and internal operational teams without requiring airlines to replace their existing systems.
Customer Intelligence
Understand complex customer questions using journey context, policies and verified airline information.
Travel Guidance
Provide personalized guidance around connections, baggage, disruption scenarios and alternatives.
Operations Intelligence
Bring operational data, rules and AI reasoning together for decision support.
Disruption Analysis
Analyze operational scenarios and prepare relevant information for customer and operations teams.
A single passenger question can require many systems.
A passenger travelling from Istanbul to Bangkok through Doha asks a natural-language question. Airline AI turns it into the data and policy checks required for a grounded response.
Designed to work with existing airline technology.
Airline AI acts as an adapter and intelligence layer between existing AI interfaces and the airline's own data, rules and operational systems.
Enterprise AI engineering from model to production.
The independent R&D platform combines domain-specific LLM adaptation, retrieval, agentic workflows, evaluation and production-oriented LLMOps.
Domain-specific LLM adaptation
SFTPEFTLoRAQLoRATRLSynthetic DataRAG & knowledge engineering
EmbeddingsVector DBRetrievalRerankingMetadata FilteringContext EngineeringAgentic AI
LangGraphMCPTool CallingMulti-step WorkflowsEnterprise engineering
JavaSpring BootSpring CloudMicroservicesREST APIsPythonFastAPILocal inference
Qwen3-4B-Instruct-2507PyTorchHugging FaceNVIDIA CUDAvLLMEvaluation & LLMOps
LLM EvaluationMLflowDockerKubernetesCI/CDPrometheusGrafanaPrototype with synthetic data. Validate with real airline data.
The current R&D and prototyping phase uses synthetic data to test the architecture and AI capabilities without requiring access to real airline data. The next phase is designed to validate the platform with real airline data and verified enterprise knowledge sources.
Synthetic Data
Test architecture, retrieval pipelines, agentic workflows, tool calling, model adaptation and evaluation while keeping the prototype independent of customer data.
Real Airline Integration
Work with real operational data and verified corporate knowledge to improve model adaptation, validate real-world scenarios, integrate with existing systems and evaluate performance.
Built around validation, evaluation and controllability.
The R&D platform evaluates model and retrieval behavior rather than treating an LLM response as automatically correct.
Response Quality
Answer relevance, context relevance, faithfulness and retrieval quality.
Tool Accuracy
Evaluation of tool selection, calls, execution and operational data retrieval.
Model Performance
Latency, throughput, GPU memory consumption and resource utilization.
Grounded Responses
Context-aware responses designed to remain aligned with verified information.
From independent R&D to airline deployments.
The platform is being developed as an independent R&D foundation, with customer-specific integration and pilots as the next commercialization stage.
R&D Platform
Core AI, airline simulation, RAG, agents, MCP and evaluation infrastructure.
Airline Integration
Connect customer-specific data, knowledge bases, APIs and operational services.
Customer Validation
Run focused pilots for customer assistance and operational decision support.
Qatar → GCC → Global
Build a Qatar-based enterprise AI company and expand across airline markets.
See how Airline AI can work with your airline.
Explore how an AI Intelligence Layer can connect existing AI interfaces, knowledge sources and operational systems to deliver airline-specific intelligence.