Build AI that thinks
like a dentist
AI that understands dental context
Production-ready AI application builder with RAG. Process dental textbooks, DICOM images, and integrate external APIs with Vertex AI and custom models.
from neurax import NeuraxEngine, ModelConfig
# Initialize Neurax with dental knowledge base
engine = NeuraxEngine(
model="gemini-pro-vision",
embeddings="colpali",
vector_store="chromadb"
)
# Load dental textbooks and DICOM files
engine.ingest_documents("./dental_textbooks/")
engine.process_images("./xray_images/", annotations=True)
# Deploy your AI application
response = engine.query("Explain implant placement technique")
print(response.answer) # AI-powered, context-aware responseWhat is Neurax?
Neurax is a complete AI application builder that lets you create production-ready RAG systems with your own data, custom models, and external integrations.
Document Processing
Ingest and process PDFs, images, and DICOM files. Extract text, tables, and visual information with multi-modal AI models.
- PDF text extraction
- Vision processing
- DICOM annotation
- Table recognition
Vector Database
Store embeddings in ChromaDB, Pinecone, or Weaviate. Semantic search with ColPali, text embeddings, or custom models.
- Multiple vector stores
- Semantic search
- Hybrid retrieval
- Custom embeddings
Model Deployment
Deploy on Vertex AI, use third-party APIs, or run custom models. Switch between providers with a single configuration change.
- Vertex AI integration
- OpenAI compatible
- Custom models
- Model versioning
Built for scale and flexibility
Modular architecture that adapts to your needs
Data Layer
Ingest and process multi-modal data
Embedding Layer
Convert data to vector embeddings
Storage Layer
Vector database and retrieval
Model Layer
AI model inference and generation
API Layer
External integrations and tools
Deploy anywhere: Cloud, on-premise, or hybrid infrastructure
Real applications. Real impact.
See how organizations are using Neurax to build production AI systems
Odento Dental RAG
Dentists need instant access to accurate clinical information from textbooks and research papers.
Feed all dental textbooks with vision processing for images and diagrams. Deploy a RAG system that understands dental context and provides cited answers.
Yoga Knowledge Base
Yoga instructors need a searchable database of ancient scriptures and modern techniques.
Process Sanskrit texts, pose images, and video demonstrations. Build a multilingual RAG system for yoga education.
Legal Case Assistant
Lawyers spend hours researching case law and legal precedents.
Import country-specific law books and case databases. Deploy AI that understands legal context and retrieves relevant cases.
Choose your model. Switch anytime.
Neurax supports multiple AI providers. Change models with a single line of code.
Google Vertex AI
OpenAI
Anthropic
Bring your own models
Deploy custom fine-tuned models or use open-source alternatives. Neurax supports any model that follows the OpenAI API format.
Enterprise-grade infrastructure
Production-ready features for teams and organizations
Self-Hosted Deployment
Deploy Neurax on your own infrastructure for complete data control and compliance.
SSO & Role-Based Access
Enterprise SSO, granular permissions, and audit logs for compliance requirements.
Advanced Monitoring
Real-time performance metrics, usage analytics, and cost tracking dashboards.
API Rate Limiting
Protect your deployments with configurable rate limits and quota management.
Version Control
Track model versions, rollback deployments, and A/B test different configurations.
Priority Support
Dedicated support team, SLA guarantees, and direct access to engineering.
Built for developers.
Simple APIs, comprehensive documentation, and powerful SDKs
Get started in 5 minutes
from neurax import NeuraxEngine
# Initialize engine
engine = NeuraxEngine(
model="gemini-pro-vision",
embeddings="colpali",
vector_store="chromadb"
)
# Ingest documents
engine.ingest("./textbooks/",
process_images=True)
# Query with context
response = engine.query(
"What is the success rate of "
"dental implants?",
return_sources=True
)
print(response.answer)
print(response.sources)
# Deploy API
engine.deploy(port=8000)Ready to build your AI application?
Start with our free tier. Scale as you grow. Enterprise support available.
No credit card required • Free tier includes 1000 queries/month