One platform. Your infrastructure. Open-source models that continuously learn your business.
4MINDS is an enterprise AI platform built on open-source LLMs (Nemotron 3, Qwen, and OSS 120B), deployed on Kubernetes. On your premises, in your cloud account, or air-gapped with zero external connectivity. The model runs where your data lives.
The hardest part of enterprise AI isn't deploying a model. It's keeping it current. Your business changes faster than any retraining project timeline. Ghost weights solves this without the sprint cycle: a shadow copy of your model trains continuously on new enterprise data, covering documents, conversations, and corrections. When the updated model passes an eval gate you configure, it atomically swaps into production. Zero downtime. Every version is retained. Every update has a timestamped eval result before it touches production.
Knowledge retrieval is only as useful as its ability to connect information across documents, relationships, and context. Graph RAG preserves entity relationships in a knowledge graph and traverses those edges at query time. It handles queries flat vector RAG can't: regulatory cross-references, organizational relationships, contract obligations, multi-document reasoning. It's built into the same platform as ghost weights, shares the same context, and improves through the same fine-tuning loop. The platform also includes native multi-channel agent orchestration across 20+ channels, native agentic software engineering, LLM-native time series forecasting and anomaly detection, and a built-in eval layer that gates every model update. One platform, one contract.
Built for enterprises where the answer to cloud AI is no, and the answer to no AI isn't an option.
4MINDS is not a general-purpose AI API. It's built for organizations where data sovereignty, compliance, and model accuracy are requirements, not preferences.
Trading strategy, counterparty data, and proprietary models aren't leaving your network. Regulatory data residency requirements put the question clearly: where does data go during training? With 4MINDS, it doesn't go anywhere.
Patient data stays on your network. An air-gapped deployment means the model trains on your clinical documentation, diagnostic patterns, and care protocols, inside your perimeter, with zero external API calls.
Classified and sensitive workloads require physical or logical isolation. 4MINDS runs fully air-gapped, no external connectivity required, everything inside the perimeter.
Client data and case strategy confidentiality require that data never leaves the firm's environment. 4MINDS fine-tunes on your matter archives and precedent libraries inside your infrastructure.
Operational processes, equipment specifications, and supplier relationships are what differentiate your operation. That knowledge shouldn't train a model shared with competitors.
The CTO handed a compliance veto on their current AI vendor. The CISO asked to approve a data processing agreement they can't. The Head of AI tired of retraining projects that are already stale when they ship.
Five convictions behind every architecture decision we've made.
You should own your AI.
The enterprise's proprietary knowledge, the processes, terminology, and business logic built over years, is a compound advantage. A model trained on that knowledge and running inside your infrastructure is a durable asset. A model running on a third-party API is a subscription that renews at someone else's price, on someone else's terms. We build for the former.
Open source is the right foundation.
Proprietary model weights are a lock-in vector. Open-source models are portable, inspectable, and benchmarked by the broader community. We build on Nemotron 3, Qwen, and OSS 120B because the most capable enterprise models are increasingly open, and because our customers shouldn't be hostage to our roadmap.
A model that doesn't learn is already wrong.
Enterprise knowledge changes. Products change, regulations change, org charts change. A static model reflects the world as of its training cutoff. Ghost weights closes the gap continuously, automatically, and without downtime.
Retrieval quality is knowledge quality.
Flat vector similarity works for simple lookups. Regulatory cross-references, organizational relationships, and contract structures need a knowledge graph. When retrieval misses a connection, the answer is wrong. In regulated industries, wrong answers aren't just unhelpful; they're a liability.
The compliance team is right.
When a CISO says no to a cloud AI vendor, better assurances from the vendor aren't the answer. A different architecture is. The compliance team and the AI team should be working from the same deployment model. 4MINDS is built so they can be.
If the data sovereignty question hasn't been answered in your AI evaluation, that's the right place to start.
We'll walk through the deployment architecture, the ghost weights loop, and the eval gate. You'll leave with a clear answer to the compliance question.