Can you deploy across multiple cloud providers?

Yes. Multi-cloud AI architectures across AWS, Google Cloud, and Azure. Serverless endpoints, edge computing, and auto-scaling infrastructure.

$ build --from-scratch

AI-Native Systems. Built From Scratch.

Q: Why build a custom CRM instead of using Salesforce?

A ground-up AI-native CRM has intelligence in every layer — real-time ML lead scoring, automated multi-channel follow-ups via AI agents, and genuinely predictive pipeline analytics. Designed around your business, not a generic platform.

Custom AI-powered websites, intelligent CRM platforms, on-premise LLM deployments, and cloud AI infrastructure — engineered ground-up for your business. Not plugins. Not templates. Real AI systems.

// WHAT WE BUILD

Three Pillars of Custom AI

AI Websites

Websites that think, convert, and learn. AI chatbots with RAG, smart search, personalised content per visitor, and conversion optimisation baked in.

Next.jsReactRAGServerless

AI-Native CRM

Not Salesforce with a plugin. Real-time ML lead scoring, AI agents handling follow-ups across email, WhatsApp, and voice. Pipeline that predicts.

ML ScoringWhatsAppVoice AIAnalytics

Local LLM

Your own AI model on your own hardware. Complete data sovereignty — nothing leaves your network. Fine-tuned on your proprietary data. NVIDIA DGX powered.

NVIDIA DGXNeMovLLMOn-Premise

// CRM DEEP DIVE

Intelligence at Every Layer

Off-the-shelf CRMs force you into their workflow. We build around yours — your sales process, your approval chains, your terminology, your edge cases.

AI lead scoring — real-time ML, not static rules
Automated follow-ups via AI agents (email, WhatsApp, voice)
Pipeline intelligence — AI predicts deal outcomes and timing
Customer health scoring — churn risk before it happens
Natural language queries: "Show me stale leads in KL"
Integration with ERP, email, WhatsApp, phone systems

// LOCAL LLM DEPLOYMENT

Your Data. Your Model. Your Building.

Deploy large language models inside your building on your own hardware. Complete data sovereignty — nothing leaves your network. Cheaper long-term than cloud APIs at scale.

Air-gapped deployment available — zero internet required
Fine-tuned on your proprietary documents and data
NVIDIA DGX Spark — 1 PFLOP FP4, 128GB unified memory
Use cases: knowledge base, document search, code assistant
Internal customer service AI — fully private
200B+ parameter models running entirely on-premise

// OUR STACK

The Technology Behind Every Build

# NovaGenAI Technology Stack

frontend:
  framework: Next.js 15 + React 19
  styling: Tailwind CSS + Custom Design System
  hosting: Vercel / Cloudflare Pages

ai_layer:
  models: Claude Opus · GPT-5 · Gemini Pro · Llama 3
  rag: LangChain + Pinecone / Weaviate
  voice: ElevenLabs + Whisper
  agents: Multi-Agent Orchestration (40+ specialists)

infrastructure:
  on_premise: NVIDIA DGX Spark · Grace Blackwell GB10
  inference: vLLM · TensorRT · NIM
  cloud: AWS · Google Cloud · Azure
  containers: Kubernetes (GKE / EKS)

data:
  databases: PostgreSQL · Redis · MongoDB
  vectors: Pinecone · Weaviate · pgvector
  analytics: BigQuery · Metabase

security:
  compliance: PDPA · SOC 2 · ISO 27001
  encryption: AES-256 at rest · TLS 1.3 in transit
  auth: OAuth 2.0 · RBAC · MFA

Frequently Asked Questions

What makes an AI-powered website different from a regular website?

An AI-powered website has intelligence built into its core — AI chatbots that understand context and qualify leads, smart search that learns from user behaviour, personalised content per visitor, and AI analytics that optimise conversion rates in real-time.

Why build a custom CRM instead of using Salesforce or HubSpot?

A ground-up AI-native CRM has intelligence embedded in every layer — real-time ML lead scoring, automated multi-channel follow-ups via AI agents, and genuinely predictive pipeline analytics. Designed around how your business actually works.

What is a local LLM deployment and why would my company need one?

A local LLM runs on your own infrastructure — nothing leaves your network. Critical for data sovereignty and becomes cheaper long-term than paying per-API-call to cloud providers, especially at scale.

What NVIDIA hardware do you use for on-premise AI?

We deploy across the full NVIDIA ecosystem — DGX Spark, DGX systems, and custom GPU infrastructure. We use NeMo for fine-tuning, NIM for optimised inference, CUDA for acceleration, and TensorRT for production deployment.

Can you deploy AI workloads across multiple cloud providers?

Yes. Multi-cloud AI architectures across AWS, Google Cloud, and Azure. Serverless AI endpoints, edge computing for low-latency inference, and auto-scaling infrastructure.