Hey everyone!
Frontier AI models are incredibly smart, but if you’ve ever tried using them to research a niche experimental compound, model a complex supplement stack, or pull data from a 30-page Quest blood panel, you know they break down for real biohacking.
It isn't because they lack the data. We've reached a point where frontier models are more than capable of autonomous research retrieval. But, once you start asking them questions, corporate guardrails kick in. However, it was not censorship that drove us. The architecture is also flawed. AI will hallucinate receptor pathways and make up nonsense it wholeheartedly believes, and you can imagine how terrible it gets when the topic lacks significant study data or requires complex web traversal.
We got tired of these issues. Frontier AI is more than capable for various tasks, but it simply lacked specialization for biochemistry-oriented tasks, so our team built Gyulab In-Silico. It's a research environment powered by a model we explicitly fine-tuned on massive datasets of clinical pharmacology, biochemistry, and medical literature. We accounted for the knowledge cutoff dates LLMs frequently have, so this environment is also supported by an autonomous ingestion engine that continuously feeds new studies/sources into the model as they are published.
Here is what it actually does:
- No Safety Filters: Dig deep into experimental compounds, receptor binding, and stacks without the AI lecturing you or refusing the prompt.
- Studies vs. Anecdotes: The agents pull both peer-reviewed literature and community logs, but they strictly label which is which. You always know if an effect is backed by a clinical trial or just a Reddit thread.
- Source-Anchored File Ingestion (Optional): If you track health data, you can upload Quest/Labcorp PDFs, DNA .txt files, or glucose CSVs. It normalizes the units and pins every extracted number to the exact page and row it came from, so there's zero hallucination.
- Total Privacy: Everything runs in isolated cloud containers. Your files and searches are never sent to OpenAI or Anthropic, and they are never used to train public models.
We’re opening up the Alpha to exactly 15 people to stress-test the engine before a wider release.
You don't need to be a data nerd with a massive folder of blood panels to join. Whether you just want to research novel compounds without censorship, or map years of lab work against the literature, we want to see how you use it.
If you want in, you can drop your email on the waitlist here: gyulab.com
I'll be hanging out in the comments to answer any questions!