Unified Intelligence Platform
A production-grade AI platform integrating NVIDIA NIM Healthcare APIs — AlphaFold2, MAISI, Evo 2, and Biomedical AI-Q — with enterprise data pipelines and human-in-the-loop validation. Built to serve 2M+ patient records at 99.9% uptime.
Apogeee integrates five NVIDIA NIM Healthcare APIs via serverless GPU inference. Each model is routed dynamically based on request type, confidence thresholds, and regional availability. Hover each card for regional API availability.
Advanced AI agents for biomedical research using retrieval-augmented generation. Synthesizes literature, clinical data, and genomic sequences for evidence-based insights.
Predicts 3D protein structures from amino acid sequences with atomic accuracy. Powers drug discovery pipelines and molecular interaction modeling at enterprise scale.
Generates high-fidelity synthetic medical images (CT, MRI) for data augmentation. Addresses privacy constraints and rare-condition dataset scarcity in clinical AI.
Integrates information over long genomic sequences while retaining sensitivity to single-nucleotide mutations. Enables genome-scale analysis and variant interpretation.
Generative AI pipeline for designing protein binders using BioNeMo NIM microservices. Accelerates lead identification in therapeutic development workflows.
import requests
NVIDIA_API_KEY = "nvapi-..." # Get free key from build.nvidia.com
NIM_BASE_URL = "https://integrate.api.nvidia.com/v1"
def query_biomedical_aiq(user_query: str) -> dict:
"""Route clinical queries to NVIDIA Biomedical AI-Q NIM (US/EU)."""
return requests.post(
f"{NIM_BASE_URL}/chat/completions",
headers={"Authorization": f"Bearer {NVIDIA_API_KEY}"},
json={"model": "nvidia/biomedical-aiq-research-agent",
"messages": [{"role": "user", "content": user_query}],
"temperature": 0.2, "max_tokens": 1024},
).json()
def predict_protein_structure(sequence: str) -> dict:
"""Predict 3D structure via AlphaFold2 NIM.
WARNING: Available in US only — add region check before calling."""
return requests.post(
f"{NIM_BASE_URL}/protein-structure/alphafold2/predict-structure-from-sequence",
headers={"Authorization": f"Bearer {NVIDIA_API_KEY}"},
json={"sequence": sequence, "databases": ["uniref90", "mgnify"]},
).json() # Returns PDB structure + per-residue confidence scoresFive-layer architecture from React presentation to NVIDIA NIM GPU inference, with full observability at every layer via OpenTelemetry and custom SLA dashboards.
Dynamic routing across 5 NVIDIA NIM Healthcare APIs based on request type, regional availability, and confidence thresholds. Automatic failover to secondary models when primary NIM is unavailable.
Clinical review workflows for AI outputs with confidence < 0.85. Reduced manual effort by 25% while maintaining clinical accuracy and HIPAA compliance across 2M+ patient records.
End-to-end tracing for every NIM API call with latency histograms, model drift detection, and SLA enforcement. P99 latency maintained below 800ms across all healthcare models.
MAISI-powered CT/MRI synthetic image generation for data augmentation. Solved dataset scarcity for rare conditions while maintaining HIPAA compliance and clinical realism.
EXPLORE_FURTHER
Apogeee is built on the same NVIDIA NIM Healthcare APIs available to you today. Start with free serverless APIs at build.nvidia.com — no GPU infrastructure required.