AI — woven into every layer of the modeling stack.
AI-assisted model construction, graph digitization, PDF-to-model conversion, and custom LLM integrations — built to accelerate scientific workflows without compromising on rigor or auditability.
PDF → model
Extract model equations, compartmental structures, and parameter tables directly from journal articles into runnable SAAM II model files. What used to take days of manual transcription now takes minutes.
Graph digitization
Recover numerical data from published figures — concentration-time curves, dose-response plots, Kaplan-Meier survival curves — with calibrated axis detection and sub-pixel point extraction.
Equation formatting
Convert LaTeX, MathML, or hand-written equations into the symbolic form your modeling tool expects — across SAAM II, MATLAB, Python, and C++ targets.
Custom LLM integrations
For pharma and biotech teams with internal modeling pipelines, Nanomath builds bespoke AI integrations that connect large language models to your existing scientific software — securely, auditably, and on your infrastructure.
- On-premise or private cloud deployment
- Domain-tuned for pharmacometric vocabulary
- Auditable provenance — every AI-generated artifact traceable to source
- SAAM II API bindings for programmatic model generation
Where AI doesn't go
Nanomath uses AI as an accelerator, not an oracle. We don't ship AI-generated model parameters into clinical decisions without human validation. We don't replace mechanistic reasoning with statistical correlations. And we don't pretend the LLM is the scientist.
Every AI-assisted workflow we build is structured so a human pharmacometrician owns the final answer — and so the AI's contribution is fully traceable in the audit log.
Build something with us
Have a workflow that's bottlenecked on manual model construction, data extraction, or literature review? We'd like to hear about it.