Your data. Your AI.
Answers your
teams can trust.
Deploy a private RAG system that connects your documents, systems, and policies to AI without sending proprietary data outside your environment.
Generic AI does not know your business.
Enterprise RAG gives AI access to the current, approved knowledge your teams already rely on, while keeping security, permissions, and source verification intact.
Answers are inconsistent
Base models guess when they cannot see your policies, procedures, product details, or customer context.
Knowledge is scattered
Critical information lives across documents, wikis, CRMs, ERPs, databases, tickets, and team tools.
Security cannot be optional
Enterprise AI needs private deployment, role-aware retrieval, audit logs, SSO, and governance from the start.
Sources are hard to verify
Teams need cited answers they can trace back to approved documents, systems, and records.
AI that retrieves before it generates.
Instead of asking an LLM to guess, Sphere RAG finds the right source material first, builds the context, and generates an answer grounded in your real data.
User asks
A question comes through chat, Slack, Teams, app UI, or API.
Query is processed
The question is embedded and prepared for semantic and structured retrieval.
Content is retrieved
The system searches approved documents, systems, databases, and knowledge sources.
Context is assembled
Relevant content is ranked, filtered by permissions, and prepared for the model.
Answer is grounded
The LLM responds with source-backed information users can verify.
Lower cost, faster deployment, current answers.
Sphere RAG avoids the cost and maintenance burden of fine-tuning by retrieving current enterprise knowledge at query time. As your data changes, answers stay current without retraining the model.
Proven outcomes for enterprise knowledge work.
See how Sphere helps teams turn scattered documents, policies, and institutional expertise into secure AI systems that improve speed, consistency, and operational visibility.
Faster ramp-up for PetroLedger.
Sphere built a generative AI onboarding platform for a global financial services firm, helping preserve expertise, speed training, and create $1.2M in annual savings.
Faster research for US Tax Services AG.
Sphere built a jurisdiction-aware RAG system that reduced document research time from six hours to seven minutes and improved retrieval accuracy by 66% in five weeks.
Trusted by leaders building secure AI.
Sphere helps enterprise teams move from AI ambition to governed, scalable systems that protect data, improve oversight, and deliver measurable operational value.
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool. Their team built a secure and scalable AI infrastructure that allows us to capture institutional knowledge, protect sensitive data within our environment, and continuously expand our AI capabilities with confidence.”
“Sphere combined AI expertise with disciplined engineering execution. Their Precision-Driven Engineering framework helped us implement Generative AI in a secure, scalable, and standardized way across teams while accelerating modernization of a critical legacy platform. The result was faster delivery, stronger oversight, and measurable operational efficiencies.”
“Using the SphereIQ AI Governance Platform, we were able to accelerate enterprise AI adoption while maintaining strong AI risk management and responsible generative AI governance at scale. Capabilities such as the AI Registry and Vendor Registry gave our teams centralized visibility and control across all AI use cases, helping ensure alignment with our governance frameworks, security standards, and compliance requirements. By partnering with Sphere Inc., we replaced manual oversight processes with an integrated and automated AI governance platform. Together, our teams centralized AI use case inventory management, automated governance assessments, and aligned AI workflows with existing IT, security, and compliance operations. SphereIQ's integrations improved lifecycle visibility across the organization, while executive dashboards and KPI reporting provided leadership with greater transparency into AI adoption progress, operational efficiency, and enterprise risk management.”
Deploy where your policies require.
Sphere RAG can be delivered as a custom build, SaaS deployment, VPC deployment, private cloud environment, or on-premise installation depending on your security and compliance needs.
Custom build, SaaS, VPC, or on-premise
Deploy the solution where your policies require, from managed SaaS to customer-controlled infrastructure.
Role-aware retrieval
RBAC enforces existing permissions so users only receive answers based on data they are allowed to access.
BYOK and audit logging
Use your own keys, connect enterprise identity, and log every query, retrieval event, response, and source.
Any LLM, zero inference markup
Use GPT, Claude, Llama, Mistral, custom models, or future providers without hidden markup on inference usage.
Choose the delivery model that matches your risk profile.
Enterprise RAG should fit the way your organization governs data, identity, keys, vendors, and infrastructure.
Custom build
Purpose-built around your workflows, data sources, permissions, and integration requirements.
SaaS
A faster managed path when your policies allow an external service model.
VPC
Deploy inside your cloud boundary with tighter network, identity, and security controls.
On-premise
Run within your own infrastructure when data residency, security, or policy requirements demand it.
Four layers, deployed inside your environment.
Sphere handles the interfaces, orchestration, retrieval, connectors, governance, and observability needed to make RAG usable in production.
User interfaces
Web chat, Slack, Teams, REST API, embedded UI, or your existing application.
Sphere RAG platform
API gateway, auth, orchestration, vector store, LLM connector, RBAC, audit logs, and observability.
Data integration
Connectors, ingestion, chunking, embedding pipeline, and update triggers.
Your sources
Salesforce, SharePoint, SQL, S3, Confluence, SAP, REST APIs, PDFs, docs, and custom systems.
Built for teams that depend on trusted knowledge.
RAG is most valuable anywhere employees or customers need accurate answers from changing enterprise content.
Customer support
Give agents precise policy, product, and troubleshooting answers without switching between systems.
Compliance and legal
Retrieve approved policies, contracts, standards, and regulatory guidance with cited sources.
Sales enablement
Surface product details, pricing rules, case studies, proposal language, and objection responses.
Engineering knowledge
Make runbooks, architecture docs, past tickets, and technical decisions easier to access.
HR and onboarding
Help employees find benefits, policies, training paths, and role-specific information faster.
Finance operations
Answer process, reporting, ERP, and approval questions from governed internal sources.
From discovery to production in weeks.
Most teams reach production in six to eight weeks. Sphere handles architecture, security review, connector setup, and go-live so your team can stay focused on the use case.
Discover
Map use cases, users, data sources, security requirements, and success metrics.
Design
Define the retrieval architecture, connectors, permissions, model strategy, and deployment environment.
Deploy
Build the pipeline, connect sources, test answers, validate governance, and launch the first production workflow.
Common questions
Turn enterprise knowledge into trusted AI answers.
Deploy private RAG with the security, citations, permissions, and production architecture your organization needs.
Book a 30-min RAG DemoTurn your data into your AI advantage
Book a 30-minute demo with our team. We'll map your use case, walk through the architecture, and show you what's possible — with your data, in your environment.