How We Built an AI-Powered Homeopathy Platform with Next.js

When most people think of healthtech, they picture hospital systems and electronic health records. But there's a whole world of specialized medical disciplines that still rely on outdated tools. Homeopathy is one of them.

At Similia, we set out to build a modern software platform for homeopathic practitioners. Here's what we learned building it.

The Problem

Homeopathic practitioners work with a repertory — essentially a massive index that maps symptoms to remedies. Think of it as a database with tens of thousands of entries, cross-referenced in complex ways. Traditionally, this comes as a 1,500-page book.

They also need a materia medica — detailed descriptions of each remedy from multiple authors. And they need case management to track patients over time.

Most existing software in this space looks like it was built in the early 2000s (because it was). We wanted to bring it into 2026.

Our Tech Stack

We chose Next.js with the App Router as our foundation. Here's why:

• Server Components let us render heavy materia medica pages without sending massive JavaScript bundles to the client
• Route Groups helped us separate the public marketing site from the authenticated practitioner dashboard
• Server Actions simplified our form handling for case notes and patient records
• ISR (Incremental Static Regeneration) for our blog and educational content

For the database, we use PostgreSQL with Prisma ORM. The repertory data alone has hundreds of thousands of rows representing the relationships between symptoms, remedies, and their grades.

The AI Layer

The most exciting part of the project has been integrating AI-powered analysis. When a practitioner enters a set of symptoms, our system doesn't just do a simple lookup. It uses AI to:

1. Analyze symptom patterns across the full repertory
2. Suggest differential remedies the practitioner might not have considered
3. Cross-reference with materia medica to validate suggestions against the classical literature

We built this as a separate service that the main Next.js app calls via API routes. This keeps the AI processing isolated and scalable.

Lessons Learned

Domain expertise is everything. We spent months learning homeopathic methodology before writing a single line of code. Without understanding how a practitioner actually thinks through a case, we would have built the wrong product.

Performance matters for large datasets. When your repertory has 100,000+ entries and a practitioner needs instant search results, database indexing and query optimization become critical. We heavily optimized our PostgreSQL queries and added Redis caching for frequently accessed rubrics.

Internationalization from day one. Homeopathy is practiced globally. We built i18n into our architecture from the start using Next.js built-in internationalization support, and it saved us enormous refactoring later.

What's Next

We're continuing to improve the AI analysis capabilities and expanding our materia medica library. If you're interested in the intersection of AI and traditional medicine, or if you're a practitioner looking for modern tools, check out Similia.

We'd love to hear from other developers building in niche healthtech spaces. What challenges have you faced?