SphereIQ - Governed Enterprise AI Implementation on AWS

SphereIQ Enterprise AI Implementation on AWS

Sphere provides professional services to design, configure, integrate, and deploy SphereIQ enterprise AI solutions within customer-controlled AWS environments. SphereIQ is a governed enterprise AI operating system that connects enterprise systems, builds organizational knowledge, models operations, enables AI agents and workflows, and applies governance across AI interactions.

One Platform, Five Integrated Layers SphereIQ brings together five purpose-built layers: Connect - Integrate enterprise systems through 200+ connectors, APIs, streaming, and an MCP hub to unify data across the organization. Company Brain - Build a governed knowledge foundation with enterprise RAG, citations, semantic retrieval, and knowledge graphs so AI responses are grounded in approved enterprise information.

Enterprise Twin - Model systems, processes, people, and dependencies to give AI workflows contextual understanding of how the organization operates. AI Factory - Configure agents, workflows, evaluations, and human approval gates to automate business processes with appropriate oversight. Governance - Apply policies, guardrails, audit records, PII controls, and AI cost attribution across AI interactions.

Why Sphere Sphere brings 21 years of enterprise engineering experience and has served more than 300 clients. This implementation experience helps organizations move from fragmented AI experiments to governed, production-ready AI operations on AWS.

AWS Deployment Options SphereIQ Dedicated provides a single-tenant implementation within the customer's AWS environment. SphereIQ Private supports customer-controlled cloud deployments for organizations with additional regulatory, security, or data-residency requirements.

Depending on architecture, implementations can use Amazon Bedrock, Amazon ECS or Amazon EKS, Amazon EC2, Amazon RDS, Amazon S3, AWS Identity and Access Management, AWS Key Management Service, AWS Secrets Manager, and Amazon CloudWatch.

SphereIQ is model-agnostic. Model access can be configured through Amazon Bedrock, self-hosted models, or other customer-approved providers based on security, governance, performance, and data-handling requirements.

What Sphere Delivers Services can include AWS architecture design, platform deployment, identity configuration, enterprise integration, connector configuration, knowledge ingestion, enterprise RAG, agent and workflow implementation, evaluation gates, governance policies, human approval workflows, monitoring, testing, documentation, and knowledge transfer.

Five-Phase Implementation Approach

1. Assess - Review the customer's AWS environment, enterprise systems, AI use cases, knowledge sources, security requirements, and governance priorities.
2. Architect - Define the AWS architecture, SphereIQ deployment model, integrations, identity controls, model strategy, governance requirements, and roadmap.
3. Connect and Configure - Deploy SphereIQ, connect approved systems and knowledge sources, configure permissions, and establish the shared data and knowledge foundation.
4. Build and Validate - Configure Company Brain, Enterprise Twin, AI agents, workflows, evaluations, governance policies, and human approval steps for prioritized use cases.
5. Deploy and Enable - Move validated workflows into production with monitoring, documentation, knowledge transfer, and optimization as scoped.

Optional Entry Points Customers can begin with a focused engagement before committing to a broader platform implementation.

AI Spend Diagnostic - A one-week assessment of the customer's AI landscape to identify optimization and governance opportunities.

Enterprise Twin Scan - A six-week engagement that models systems, processes, and dependencies to inform AI priorities and implementation planning. Both entry points produce deliverables that can feed directly into a broader SphereIQ implementation.

Security and Governance SphereIQ supports SSO/SAML, RBAC, SCIM provisioning, audit records, policy enforcement, PII controls, and governed model and agent access. Compliance depends on the customer's deployment, configuration, processes, and regulatory obligations.

The exact AWS architecture, services, integrations, deployment model, deliverables, and implementation scope are defined before the AWS Marketplace private offer is issued. AWS infrastructure, model inference, and AWS service usage charges are separate from Sphere's professional services fees unless specifically included in the private offer.

Get Started The engagement begins with a working session to review the customer's systems, AI use cases, AWS environment, security requirements, and deployment priorities. Sphere then defines the recommended implementation path, scope, deliverables, and private-offer structure.