Aerobot: AI For Aeronautical Training

Aerobot is a startup specializing in AI solutions for both private and professional aeronautical training. Traditionally, course materials and teaching methods in this field have remained largely unchanged, leaving students and instructors to grapple with outdated processes. Determined to bring modern solutions to an industry in need of innovation, Aerobot first attempted to build OpenAI custom GPT-based assistants and implement ElasticSearch for Retrieval-Augmented Generation (RAG). However, these early efforts revealed critical challenges around ensuring pedagogical compliance, minimizing “hallucinations,” and avoiding code-heavy workflows.

“We had serious doubts that our project could succeed—until Aimwork made it happen.”

1. Overcoming RAG & OpenAI Custom GPT Limitations

Challenge:

Before partnering with Aimwork, Aerobot faced significant hurdles in its quest to incorporate AI into aeronautical training:

Compliance & Accuracy
Using custom GPT assistants introduced the risk of generating inaccurate or off-curriculum responses—potentially compromising the safety-critical nature of aeronautical instruction.

Restricted RAG Capacity
The existing setup offered limited space for Retrieval-Augmented Generation, making it difficult to anchor all AI outputs in verified training materials and further increasing the likelihood of incorrect information.

Code-Intensive ElasticSearch
While ElasticSearch provided a potential RAG approach, it required extensive coding and specialized know-how, slowing development and making continuous iteration cumbersome.

Uncertain Viability
These technical roadblocks cast doubt on whether a truly next-generation training platform could be achieved with the existing mix of custom GPT and ElasticSearch solutions.

Solution:

By moving to Aimwork AI Management Workspace, Aerobot found a streamlined path forward:

Hosted Custom Agents
All personalized AI agents now run on Aimwork infrastructure, reducing the need for internal server overhead and complex deployments.

Chunk-Based Prompting for RAG
Instead of wrestling with ElasticSearch, Aerobot can easily organize training documents into “chunks”, manage RAG prompting and settings without coding, ensuring every AI response is grounded in approved, up-to-date materials.

Rapid Iteration
Aimwork’s intuitive interface mimics OpenAI’s API, enabling Aerobot to run multiple prompt tests, fine-tune their RAG pipeline, and quickly validate new approaches—without heavy coding.

Multimodal Capability
The platform supports both text and image inputs, empowering Aerobot to enrich aeronautical lessons with diagrams, cockpit illustrations, and more.

Outcome:

With Aimwork’s simpler yet robust architecture, Aerobot can confidently deliver AI-enhanced lessons that strictly adhere to established training content. The team no longer worries about OpenAI GPT “hallucinations” or time-consuming ElasticSearch configurations. As a result:

Reduced Development Overhead
Less custom code means more time to focus on refining lessons.

Improved Compliance
AI responses are consistently accurate and aligned with Aerobot’s pedagogical standards.

Greater Flexibility & Confidence:
Quick test cycles confirm new features, ensuring a dynamic learning environment that keeps pace with evolving industry needs.

Conclusion

By replacing OpenAI custom GPT setups and complex ElasticSearch integrations with Aimwork AI Management Workspace, Aerobot has resolved the technical bottlenecks that once threatened its vision. Freed from coding constraints and supported by a powerful chunk-based RAG approach, Aerobot now offers a modern, reliable, and fully compliant AI-driven training experience—setting a new standard in aeronautical education.