Platform

Multi-Cloud AI. Single Platform.

Deploy across AWS, Azure, GCP, or on-premise. Connect to 500+ models and your entire enterprise stack. 

Deployment Models

Choose the deployment model that fits your security, compliance, and performance requirements. 

SaaS

Production-ready in hours with automatic updates and elastic scaling

On-Premise

Complete control within your data center infrastructure 

Hybrid

Develop in cloud, deploy on-premise, or mix based on data sensitivity

Air-Gapped

Fully isolated deployments for classified or highly regulated environments

Enterprise Grade Infrastructure

Built for mission-critical workloads with automatic scaling, end-to-end encryption, and enterprise-grade availability. 

Kubernetes-Native

Container orchestration with automatic scaling and self-healing

Enterprise-grade availability

Enterprise-grade availability with built-in redundancy 

End-to-End Encryption

Data protection in transit and at rest 

Comprehensive Auditing

Full audit trails with role-based access control

Multi-Model Intelligence

Connect to 500+ models with complete vendor independence. 

Cloud Providers

OpenAI GPT, Anthropic Claude, Google Gemini Pro, Ultra, Flash, Amazon Bedrock (Claude, Llama, Titan, and more)

Local & Open-Source Models

Deploy Llama, Mistral, Falcon, and other open models on your own infrastructure for maximum control and cost optimization.

Intelligent Model Routing

Automatically route requests to the optimal model based on task complexity, cost targets, data sensitivity, compliance requirements and real-time availability.

Hybrid Model Strategy

Combine cloud models for general tasks with local models for sensitive data - all within a single workflow.

Enterprise Integrations

CRM platforms (Salesforce, HubSpot, Dynamics, and more)
ERP systems (SAP, Oracle, NetSuite, and others)
Document repositories (SharePoint, Google Drive, Box) 
Databases (PostgreSQL, MySQL, MongoDB, Oracle)
Communication tools (Slack, Teams, Email)

API-First Architecture

RESTful APIs for bidirectional data flow
Standards-based connectors for enterprise systems
Flexible integration options for custom requirements

Model Context Protocol (MCP)

Custom data sources
Proprietary business logic
Third-party service integrations
Specialized processing tools 

Enterprise Machine Learning Integration

Connect proprietary ML models to AI agents
Build and deploy custom models with AutoML
End-to-end ML Ops for training and deployment
Combine traditional ML with generative AI for optimal results

Low-Code Agent Builder

Drag-and-drop workflow designer 
Pre-built agent templates by industry and use case
Test and debug in sandbox environments
Version control and rollback capabilities 

Developer Tools

Comprehensive APIs for custom development
Documentation and integration guides
Infrastructure-as-code support

Automated Deployment

CI/CD pipelines for reliable, repeatable deployments
Testing and validation workflows
Streamlined path from development to production
Typical Implementation Timeline:
4-8 weeks from kickoff to production
Dedicated Account Management

Single point of contact for your success

24/7 Technical Support

Enterprise SLA with priority escalation

Professional Services

Implementation, training, and optimization expertise 

Regular Business Reviews

Quarterly strategy sessions and roadmap planning

Industry Templates

Pre-configured agents and workflows for common use cases

Migration Support

Guided transition from legacy systems

Training Programs

Onboarding for technical and business users

Best Practice Guidance

Proven patterns and implementation support

Measurable Business Impact

of routine tasks automated
data-driven decision-making
accuracy in automated processes
productivity gains in knowledge work

Ready to Build Intelligence Into Your Enterprise?

Deploy AI agents that integrate with your systems, respect your data sovereignty, and scale with your business.