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4Minds on Amazon Web Services (AWS) is a contextual AI platform that runs inside the AWS environment you already use. It connects to your data in Amazon S3, Amazon RDS and Amazon Redshift, builds a GPU-accelerated knowledge graph of your business, and grounds models from Amazon Bedrock or Amazon SageMaker in that context, so AI keeps pace as your business changes.

4Minds adds a contextual intelligence layer on top of the AWS services you already run. It reads data from Amazon S3, Amazon RDS, Amazon Redshift and AWS Lake Formation; uses your models in Amazon Bedrock and Amazon SageMaker; and authenticates through AWS Identity and Access Management (IAM). You keep your applications and skip separate extract, transform and load (ETL) pipelines.

No, you don't need to move your data out of AWS to use 4Minds. 4Minds connects to your data where it already lives in Amazon S3, Amazon RDS, Amazon Redshift and AWS Lake Formation. In a customer-managed deployment, the platform, knowledge graph and fine-tuned weights all stay inside your AWS account.

You deploy 4Minds on AWS through AWS Marketplace. The customer-managed option installs into your Amazon Elastic Kubernetes Service (EKS) cluster, with all settings captured in a single configuration file for auditing.

Yes, 4Minds works with Amazon Bedrock and Amazon SageMaker. It connects to Amazon Bedrock models and Amazon SageMaker endpoints through scoped IAM permissions, so you can bring your own model. Bedrock models receive reasoning grounded in your knowledge graph. Open-weight models hosted on SageMaker get that same grounded reasoning, plus continuous adaptation, with training jobs running on SageMaker infrastructure.

4Minds connects natively to these AWS data sources: Amazon S3 buckets and S3-compatible APIs, Amazon RDS relational databases, Amazon Redshift, and AWS Lake Formation data lakes. Regional S3 endpoints keep access fast and local. AWS Glue and Amazon AppFlow handle data that needs transformation, and AWS Lambda with Amazon EventBridge triggers processing when new data lands.

Yes, you can switch AI models on AWS without retraining on 4Minds. 4Minds keeps your knowledge graph, memory and reasoning layer separate from any single model. You can move between Amazon Bedrock foundation models, SageMaker-hosted models and open-weight models you run yourself without rebuilding that context.

4Minds handles identity and access on AWS using IAM role federation over OpenID Connect (OIDC), so it never holds long-lived AWS credentials. It authenticates with short-lived credentials from AWS Security Token Service (STS), scoped by the trust policy and permission boundary you attach. Amazon Cognito covers other identity models, and IAM enforces least-privilege access down to individual buckets, keys and instances.

You control the encryption keys when you run 4Minds on AWS. The AWS Key Management Service (KMS) holds the encryption keys, including customer-managed keys when policy requires you to control rotation and revocation. AWS Secrets Manager stores credentials and connection strings with automatic rotation. 4Minds encrypts data in transit with Transport Layer Security (TLS) 1.3 across every layer of the platform.

You can monitor and audit 4Minds on AWS using AWS CloudTrail and Amazon CloudWatch, so your security team audits it with the same tools it uses for other AWS workloads. The platform adds its own reasoning traces and logging across the pipeline, so you can see how each output was shaped, checked and delivered, then trace it back to the source data.

You can start a free trial of 4Minds on AWS by selecting “Try for free” on the 4Minds AWS Marketplace listing to deploy in one click. You can go from data to a tuned model in minutes. For a deployment plan built around your AWS environment, security requirements and use cases, schedule a demo or talk to a 4Minds expert.