4MINDS is an enterprise AI platform that deploys open-source LLMs on your Kubernetes cluster — on-prem, air-gapped, or in your own cloud. It includes Ghost Weights continuous fine-tuning, Graph RAG knowledge retrieval, built-in evaluation gating, and native multi-channel agent orchestration.

Does 4MINDS require sending data to the cloud?

No. 4MINDS runs entirely inside your infrastructure. There are zero external API calls during inference. Your data never leaves your network. 4MINDS is air-gapped capable and supports classified enclave deployments.

What are Ghost Weights?

Ghost Weights is 4MINDS' continuous fine-tuning system. A shadow copy of the model trains on new data while the live model serves traffic. When the eval gate passes, the update swaps atomically — zero downtime, no retraining sprints.

What LLMs does 4MINDS support?

4MINDS runs on open-source LLMs including Nemotron, Qwen, and OSS 120B models. You are never locked to a proprietary model vendor. New open-source models can be added as they are released.

How does 4MINDS pricing work?

4MINDS uses annual per-deployment pricing. There are no per-seat fees, no per-token charges, and no data leaving your network. Tiers include Proof of Concept, Enterprise, and Government & Defense.

Enterprise AI Platform

EnterpriseAIthatrunsinyourinfrastructureandlearnsyourbusiness.Nottheirs.

Ghost weights trains a shadow copy, evaluates it, and swaps atomically on pass. Graph RAG traverses entity relationships for queries flat vector search gets wrong. Fully air-gapped — inference, fine-tuning, and agents all run on your infrastructure. No network egress. No cloud dependency.

See How It Works

47+enterprise deployments

120Bparams, open-source models

0msmodel swap downtime

100%of platform features fully air-gapped — inference, training, agents

GRAPH RAG · DEMO

sandbox · no data stored

Ask a question flat search can't answer.

4MINDS Graph RAG running against a demo knowledge base. It traverses entity relationships — not just text similarity. The queries below require multi-hop reasoning. Flat vector search gets them wrong.

Trusted by enterprise teams

Financial Services

Defense & Government

Healthcare

Legal

Manufacturing

47+Enterprise deployments

120BParameter models, open-source

0msDowntime on model swap

Enterprise AI Problem

Your most sensitive data can't leave the building

CISO

Your data leaves your building.

Every query you send to OpenAI or Anthropic goes to their infrastructure. Their data processing agreement governs what happens to it. Your compliance team read that agreement. That's why they said no.

Head of AI

The model goes stale on deployment day.

The model you shipped six months ago doesn't know about the product launch last month, the policy revision last quarter, or the terminology your teams actually use. That's what a static model does.

CTO

Per-token pricing scales with every workflow you add.

The more your teams use the AI, the higher the bill. Every new use case means re-evaluating the budget. That's the wrong direction for infrastructure you should own.

Ghost Weights

Core Technology

The model stays current. Zero downtime.

Most enterprises deploy a model and watch it fall behind. Ghost weights closes the loop permanently — continuous retraining, automatic eval gate, atomic production swap.

0minutes of downtime per model update

100%rollback available at any point

Knowledge Graph · 4 entity types · 7 relationships

Graph RAG

Knowledge Retrieval

The query your RAG pipeline can't answer.

"Which customers in financial services have open support tickets related to the compliance module that was updated last quarter?"

Flat RAG can't answer this query

Feature	Flat RAG	Graph RAG
Retrieval unit	Text chunk	Entity + relationship
Multi-hop queries	✗	✓
Entity relationships	Lost in chunking	Preserved in graph
Compliance cross-references	Partial	Full traversal
Organizational knowledge	Flat	Structured

See Graph RAG in Action →

Agent Platform

The raw LLM answers questions. Enterprise Harness makes it a production worker.

Most enterprise AI deployments fail not at the model but at everything around it: tool access controls, memory management, failure recovery, governance, and human approval workflows. Enterprise Harness is that infrastructure layer — built natively into 4MINDS alongside Ghost Weights and Graph RAG.

All five components run on the same platform as Ghost Weights and Graph RAG — sharing context, the same fine-tuning loop, and the same governance layer. Not a bolt-on. Not a third-party integration.

Explore the Agent Platform →

Tool Orchestration & Sandboxing

Define exactly which tools, APIs, and file systems each agent can access. Actions run in isolated containers. If an agent fails or behaves unexpectedly, the blast radius stays bounded to what you permitted.

Memory Management

The agent context window is actively managed across sessions. Past actions are summarized and archived. Relevant prior context is retrieved and injected when needed.

Execution & Feedback Loops

Agents observe the result of every action and self-correct without human intervention. A failed step triggers diagnosis and retry rather than a hard stop. Each step continues until resolution.

Guardrails & Governance

Fine-grained RBAC on every agent action. Rate limits, policy checks, and audit trail on every execution step. How your AI uses tools defines how it gets governed.

Human-in-the-Loop

Configure exactly when the agent stops and waits for approval. High-stakes actions require human sign-off. Routine operations run autonomously. The enterprise enforces it.

Multi-Cloud Ready

DEPLOYMENT OPTIONS

Three deployment models. One platform.

One platform. Three deployment modes. All with full Ghost Weights, Graph RAG, and eval capability.

SELF-MANAGED

On Your Infrastructure

Deploy on your Kubernetes cluster, fully air-gapped. Inference, fine-tuning, and agents all run on your infrastructure. Zero network egress. No cloud dependency at any layer.

Fully air-gappedKubernetes-nativeInference sovereignty

FASTEST TO PRODUCTION

Managed by 4MINDS

4MINDS runs and maintains the platform on AWS, Google Cloud, or Azure. Fastest path to production.

SLA-backed uptimeManaged upgradesAWS · GCP · Azure

BYOC

Your Cloud, Our Software

Deploy into your own AWS, Google Cloud, or Azure account. You own the infrastructure. 4MINDS manages the software.

Your VPC / VNetYou control billingSoftware-only license

Available on

All deployment modes include Ghost Weights continuous fine-tuning, Graph RAG, built-in eval, and the full agent platform.

How We Compare

Enterprise AI without the per-token bill.

Every cell below is an architectural difference, not a marketing claim.

3–5×TCO reduction vs. OpenAI API over 24 months

$0per-token cost on self-hosted compute

	Recommended4MINDS	OpenAI Frontier	Anthropic
Data sovereignty	Your infrastructure — no third-party jurisdiction	Cloud-hosted, CLOUD Act jurisdiction	Cloud-hosted, CLOUD Act jurisdiction
Pricing model	Open-source on your compute	Per-token API	Per-token API
Model freshness	Ghost weights: continuous	Static, knowledge cutoff	Static, knowledge cutoff
Retrieval	Graph RAG: multi-hop	Flat vector RAG	Flat vector RAG
Compliance audit trail	Built-in eval gate + log	Not included	Not included
Automated red-teaming	Built-in, on-prem, runs before every update	Not included	Not included
Agent platform	Native: shared fine-tuning + KG	Frontier agents, OpenAI only	Cloud-only, Claude only
Open-source models	Nemotron 3, Qwen, OSS 120B	No: proprietary GPT	No: proprietary Claude

See the full OpenAI comparison →See the full Azure comparison →See the full Anthropic comparison →See the full AWS Bedrock comparison →See the full Google Vertex comparison →See the full Claude Code comparison →

From the Blog

All articles →

Ghost Weights

Zero-Downtime Model Updates: How Atomic Weight Swap Works

6 min read→

Graph RAG

Graph RAG for Financial Services Compliance: Why Multi-Hop Queries Matter

8 min read→

Strategy

Cloud Datacenters Are Military Targets. What That Means for Your AI Infrastructure.

6 min read→

See 4MINDS in your environment.

30 minutes with a 4MINDS engineer. We'll walk through the deployment architecture against your use case. You'll see a live deployment, not slides.

See the Architecture

Fully air-gapped, your cloud, or ours. Inference, fine-tuning, and agents stay under your control — nothing leaves the perimeter.