4MINDS Enterprise AI Platform for Amazon EKS helps organizations deploy business-specific AI applications and agents inside their own AWS environment. 4MINDS connects enterprise data, workflows, policies, and operational context into a knowledge graph-powered intelligence layer, with continuous model optimization and fine-tuning to move AI use cases from proof-of-concept to production with security, governance, and control.
4MINDS helps organizations move beyond generic AI assistants and stalled proof-of-concepts by deploying production-ready AI systems grounded in real business context. This Amazon EKS deployment option is designed for customers that want to run 4MINDS in their own AWS environment with greater control over infrastructure, data, security, and governance.
The platform unifies structured and unstructured enterprise knowledge into a knowledge graph-powered intelligence layer, enabling AI applications and agents to reason across relationships, dependencies, workflows, policies, and changing business conditions instead of simply retrieving isolated information. 4MINDS combines enterprise reasoning, continuous model optimization, fine-tuning, knowledge-grounded validation, source traceability, model orchestration, and workflow automation to help teams build AI systems that adapt as the business changes.
Customers use 4MINDS to support enterprise search, knowledge management, revenue intelligence, customer operations, workflow automation, decision support, and other business-specific AI use cases on AWS. AWS provides the foundation. 4MINDS makes AI business-specific.
Highlights
Connect structured and unstructured enterprise knowledge into a knowledge graph-powered business context layer, with fine-tuning and model optimization that help AI adapt to your workflows, policies, and changing conditions.
Run 4MINDS as a private, customer-managed deployment on Amazon EKS for teams that need stronger control over data, infrastructure, security, and compliance.
Operationalize AI agents and applications with knowledge-grounded validation, source traceability, model orchestration, enterprise integrations, and workflow automation.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Designed for professional teams that require enhanced performance and flexibility, this plan includes three custom AI models, up to 10,000 LLM calls per month, 10 concurrent queries, 1,000 document uploads per month, 50 GB of secure storage, up to five user seats, medium GPU priority, API access with webhook support, role-based access control, and full agentic workflow support.
$2,500.00
0%
Enterprise
Enterprise Purpose-built for large-scale enterprise deployments requiring maximum performance, security, and control, this plan includes unlimited custom AI models, unlimited LLM calls and queries, unlimited document uploads and storage, unlimited user seats, highest GPU priority, access to all base models, dedicated infrastructure, custom integrations, custom deployment options, SSO with full RBAC and audit logging, SLA guarantees, and a dedicated support team.
You choose between two contract tiers for private deployment of this AI platform. The Startup tier sets defined limits: three custom AI models, a monthly cap on LLM calls, concurrent queries, document uploads, storage, and user seats, plus medium GPU priority. The Enterprise tier removes these caps, offering unlimited models, calls, queries, uploads, storage, and seats, along with highest GPU priority, dedicated infrastructure, and custom deployment options. Pricing scales by capacity and control: Startup fits fixed team needs, while Enterprise suits large-scale deployments needing dedicated resources and service guarantees. Both are billed as contracts by unit.
Top-of-mind questions for buyers
What counts as one LLM call and one query for the Startup tier limits?
An LLM call is a single request sent to a language model. The Startup tier caps these at 10,000 per month. A concurrent query is one active request running at the same time as others. The Startup tier allows 10 to run simultaneously. The Enterprise tier removes both limits entirely.
What happens if my team grows beyond the Startup tier limits on seats, storage, or document uploads?
The Startup tier fixes caps on user seats, storage, uploads, models, and calls. Exceeding these requires moving to the Enterprise tier, which removes all caps and adds dedicated infrastructure. The switch between contract tiers is not automatic. You would need to select the Enterprise contract to gain unlimited capacity.
Do the storage and GPU limits apply if I deploy on my own AWS infrastructure?
The storage and GPU priority figures apply to the vendor-hosted deployment. Instances hosted on your own cloud provider are not subject to those specific limits. This affects the Startup tier caps most directly, since the Enterprise tier already offers dedicated infrastructure and custom deployment options.
Request a private offer to receive a custom quote.
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Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Read the guide first. It covers the cluster prerequisites you create before
installing (a default StorageClass + CSI driver, an ingress controller, a
namespace, and a TLS secret for your hostname; plus, only for KMS auto-unseal,
a cloud KMS key + IAM role), followed by install, verify, upgrade, and
uninstall steps. Prerequisite commands use Amazon EKS as the worked example;
substitute equivalents on other Kubernetes distributions.
The same page includes the full values template. Copy it to my-values.yaml and fill in:
- global.hostname your public DNS name (everything derives from it)
- email your transactional email provider
- inference endpoints your OpenAI-compatible LLM / embedding endpoints
- any SSO / connector integrations (all optional)
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
The 4MINDS CARE Assessment helps AWS customers identify their highest-value AI use case, evaluate data readiness, and define a practical roadmap to pilot in weeks, not months. Move from "we should do something with AI" to "here's the right first use case and how we execute it on AWS
Give any MCP-compatible AI agent instant access to your fine-tuned models, knowledge graphs, and datasets on the 4MINDS platform. 30+ production-ready tools for knowledge graph querying, model inference, document analysis, and dataset management through the Model Context Protocol (MCP).
A comprehensive investor relations knowledge management system that extracts, processes, and prioritizes content from multiple sources, providing interactive, responses via a chat interface with an integrated feedback.
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