A leading aromatherapy educator approached Kirschbaum with a challenge: students in their certification programs needed a faster, clearer way to research essential oils, safety guidance, and recipes using their extensive catalog of in-depth articles, chemical component reports, and third-party materials.

The client envisioned a conversational reference tool where students could ask questions in plain language – for instance, “Which oils contain linalool?”, “Can I diffuse this around pets?”, or “What’s a good blend for sleep?” – and receive accurate, cited answers grounded in their existing educational content.

Kirschbaum partnered with their team to bring that vision to life.

The primary challenge was to design and implement an AI-powered reference assistant that met strict accuracy, traceability, and safety requirements across a complex content ecosystem:

• Content existed in multiple publishing systems with no unified search layer. 

• Every answer had to cite and link back to original sources to ensure trustworthiness and academic rigor. 

• Students asked questions spanning chemical components, safety warnings, and practical usage scenarios, requiring the system to interpret nuanced domain concepts reliably. 

• The assistant needed to function as an embedded feature within a paid educational platform without disrupting existing workflows. 

• Due to the health-related nature of aromatherapy, the tool had to include contextual guardrails (e.g., use around children or pets) so that answers remained safe and  compliant with best-practice guidance. 

Meeting all of these constraints while maintaining usability, speed, and reliability demanded a carefully engineered AI and data infrastructure.

We partnered with the client in a phased, engineering-driven engagement that delivered value incrementally while mitigating risk. Our process involved:

1. Beginning with a tightly scoped proof of concept.

We began by ingesting and indexing the client’s core article library into a vector database using OpenAI models. Content was split into granular chunks to give the AI precise access to individual sections rather than broad summaries, improving relevance and accuracy. 

2. Expanding knowledge sources dynamically.

Alongside internal content, we integrated external partner blog systems and enabled ongoing ingestion of new educational materials. This ensured the assistant’s knowledge base stayed current and comprehensive. 

3. Building a robust backend for retrieval and response shaping.

Using a Laravel-based backend, we implemented:

• Vector search and document retrieval.

• AI querying and response construction.

• Automated article synchronization.

• Embeddable delivery within the client’s existing platform. 

4. Enforcing rigorous citation handling.

To ensure every answer could be traced back to original sources, responses weren’t streamed as raw AI output. Instead, the system assembled and enriched each answer with complete citations before delivering a polished, unified message. 

5. Delivering within the student ecosystem.

By embedding the AI assistant directly into the client’s platform interface, we made it seamlessly accessible to learners without introducing new tools or workflows.

The solution delivered a clean, conversational AI assistant that transformed how students engage with educational content. Key features included:

• Natural-language questioning: Students can now ask questions in plain English about essential oils, chemistry, safety, and practical usage. 

• Fast, reliable answers: Rather than manually searching hundreds of articles, users receive concise answers with verifiable citations attached. 

• Practical guidance: The assistant supports “recipe”-style queries, helping students explore specific use case combinations with contextually appropriate safety guidance. 

• Embedded experience: Delivered as a native feature of the existing educational platform, the tool integrates smoothly into the student journey without requiring additional logins or separate apps. 

Educational engagement improved as learners could research faster, trust the information they received, and draw connections across sources more efficiently.

This project highlights several core insights into applying AI responsibly in domain-specific educational contexts:

• Accuracy must be engineered, not hoped for. Citation-driven responses were critical for user trust in a field where misinformation can have real-world implications. 

• Data integration underlies AI value. A powerful frontend experience depends on a well-structured, comprehensive content ingestion and retrieval system. 

• Seamless integration maximizes adoption. Embedding the tool within the existing student platform ensured usage didn’t require learners to adopt new workflows. 

• Iterative delivery de-risks complexity. Starting with a proof of concept and expanding incrementally allowed us to validate assumptions and refine the model before broader deployment. 

The result is a student-focused product that bridges rich educational content with intuitive AI assistance, enhancing learning without compromising reliability or safety.