Multi-LLM AI Platform

Intelligence that adapts to every task

Experience the power of multiple AI models working together. Our platform intelligently routes tasks to the optimal LLM, transforming your data into actionable insights across sales, training, and operations.

AI-First Architecture

Built for organizations ready to harness the full potential of artificial intelligence and machine learning

Multi-LLM Orchestration

Intelligent task routing across Gemini, GPT-4, Claude, Llama, and specialized models. Each query gets the optimal AI for maximum accuracy and efficiency.

LLMs Gen AI Task Routing

RAG + Knowledge Graphs

Advanced retrieval systems that combine semantic search with knowledge graphs to surface contextual, real-time data when your teams need it most.

RAG Knowledge Graphs Semantic Search

Agentic Workflows

Deploy AI agents that plan, execute, and optimize complex workflows. From lead routing to customer scoring, automation that actually thinks.

AI Agents Automation Workflows

Data Classification

Automatically tag and classify organizational data with ML models. Transform unstructured information into structured intelligence.

MLOps Classification Data Processing

Azure AI Foundry and Google AI Studio Integration

We leverage Azure AI Foundry and Google AI Studio to orchestrate AI workflows and integrate ML models directly into your processes and workflows providing context and reasoning.

Python Kubernetes MLOps

Enterprise Scale

Built for reliability and performance. Secure, scalable systems that grow with your organization's AI ambitions.

Enterprise Security Scalability

See the Platform in Action

Experience our operational DevOps demo: Watch how our AI platform uses Neo4j knowledge graphs and RAG to aggregate real-time data from AWS, Google Cloud, Azure, on-premises systems, GitHub, SharePoint, and Azure DevOps to provide comprehensive project status insights.

Schedule a Demo

AI Processing Lifecycle

User Query
RAG Retrieval
Neo4j Tagging
AI Analysis
Intelligent Answer

Data Sources

AWS Google Cloud Azure On-premises GitHub SharePoint Azure DevOps
GPT-4 Claude Llama
GPT-4 Claude Llama
How far along is the project FTG?
Analyzing project FTG status across all connected systems:


🔍 Azure DevOps: Sprint 7/12 complete (58%), 23 tasks remaining, CI/CD pipeline passing

📊 AWS: Infrastructure deployed, 94% uptime, Lambda functions processing 1.2M requests/day

☁️ Google Cloud: BigQuery datasets updated, ML models trained on latest data, API endpoints active

🏢 On-premises: Database sync completed, legacy systems integrated via API gateway

📝 SharePoint: Documentation 87% complete, 12 pending reviews, risk register updated

💻 GitHub: 156 commits this week, 8 open PRs, code coverage at 92%, security scans passing

Overall Status: Project FTG is 72% complete with go-live target in 6 weeks. Key risks: dependency on vendor API, mitigated by redundant connections.

Ready to Transform Your Organization?

Join forward-thinking companies using AI to accelerate sales, streamline operations, and unlock insights from their data. Start your AI transformation today.