The Precision Layer for
High-Dimensional AI.
Standard AI forces complex data into flat spaces, causing signal crowding and black-box errors. Powered by G-GAM geometric causal intelligence (Granular - Geometric Associative Memory), Ipvive preserves natural geometry across your pipelines, edge networks, and partner environments—dropping model noise from $\sim 18\%$ to under $2\%$ without an infrastructure overhaul.
import torch
from ipvive import HyperbolicSubstrate
# Drop-in non-Euclidean substrate beneath existing PyTorch / Scanpy latent embeddings
model = MyBiologicalTransformerOrAutoencoder()
substrate = HyperbolicSubstrate(curvature=-1.0, memory_engine="G-GAM")
# Transform flat embeddings to conformal hyperbolic space ($\Delta_{\text{CD}} \lt 1.8\%$)
latent_space = substrate.transform(model.embeddings)Poincaré $\mathbb{H}^3$ Manifold & Causal Trajectories
Physical & biological signals transformed to semantic primitives.
Multi-agent falsification loop running sub-10ms hypothesis tests.
$H^2/H^3$ conformal mapping & causal long-term associative memory.
$5,000 / Month Starter Program
Validate non-Euclidean latent precision on public datasets in 24 hours.
24-Hour Empirical Audit
Run on public reference datasets (CELLxGENE, TCGA, UniProt, PDB) with zero internal IP exposure or NDAs required.
Conformal Metric Delta Report
Direct empirical comparison showing exact $\Delta_{\text{CD}}$ metric noise drop from $\sim 18\%$ down to $\lt 2\%$.
Drop-In PyTorch & Scanpy Layer
Native python integration layer backed by dedicated Ipvive research engineers.