Sphere Partners
Enterprise RAG solution

Your data. Your AI. Zero compromises.

Deploy a private AI knowledge base on your own infrastructure in 6–8 weeks. The Retrieval-Augmented Generation pipeline runs on your documents, your LLM, your compliance rules — without sending a byte to a third-party server.

Book a 30-min demoSee How It Works
0Bytes that leave your cloud
4–8Weeks to production
AnyLLM, any data source
Private cloud · SOC 2 Ready
HIPAA CompliantSOC 2 ReadyGDPR ReadyRBAC enforced

Organizations around the world trust us

ideel
JFrog
Clearcover
91 Seconds
PHC
NextCapital
DigitalOcean
Enova
bp
Groupon
CreditNinja
Navy Pier
DoorDash
Gett
Experify
ideel
JFrog
Clearcover
91 Seconds
PHC
NextCapital
DigitalOcean
Enova
bp
Groupon
CreditNinja
Navy Pier
DoorDash
Gett
Experify
The Problem

Generic AI doesn't know your business

LLMs are powerful — but they're missing the one thing that makes your company unique: your data. Here's what happens when AI runs without it.

Hallucinations & inaccurate answers

Without your data, models fabricate answers, cite wrong policies, and erode employee and customer trust at scale.

Fine-tuning is cost-prohibitive

Training a custom model on your data costs hundreds of thousands of dollars and is obsolete the moment your data changes.

Direct connections create security risk

Connecting your data directly to third-party LLM platforms exposes proprietary information and creates compliance nightmares.

What is Enterprise RAG?

Enterprise Retrieval-Augmented Generation connects a large language model to your proprietary data and runs entirely within your security perimeter — never exposing data to external providers. The key distinctions: data isolation, permission enforcement, and source citations for verification.

6–8 weeks

Kickoff to production. A proven deployment cadence refined across 5+ enterprise rollouts.

~1/10th the cost

Versus comparable fine-tuning. No retraining as your data changes — RAG retrieves at query time.

Zero data exposure

Inside your private cloud. Queries, retrieval, and generation all run within your security perimeter.

Any LLM

GPT-4, Claude, Llama, Mistral, custom. Swap models freely — no provider lock-in.

How It Works

RAG: AI that retrieves before it generates

Instead of guessing, Sphere's solution retrieves the exact, current information from your data sources — then generates precise, grounded answers. Every time.

1
User asks
Natural language question via chat, app, or API
2
Query embedding
The query is transformed into a semantic vector for similarity matching
3
Retrieval
The most relevant content chunks are fetched from your data sources
4
Context assembly
Retrieved content is ranked, filtered, and assembled into an LLM prompt
5
Grounded answer
The LLM generates an accurate, cited answer from your real data

Full RAG Architecture

Your Data
  • Salesforce
  • SharePoint
  • S3
  • SQL
  • Docs
Step 1
User Query
Step 2
Query Processor
Embeds the question into a semantic vector
Step 3 · Retrieval
Vector Search
Semantic similarity
Structured Retrieval
SQL, APIs, CRM, ERP
Doc Retrieval
PDFs, wikis, reports
Step 4
Context Builder
Ranked + filtered into an LLM prompt
Step 5
LLM Generator
Private cloud · any model (GPT, Claude, Llama, Mistral)
Output
Grounded Answer + Sources
Private Cloud
  • Your infrastructure
  • No data leaks
  • SOC 2 · HIPAA · GDPR

Connects to your existing data sources

SalesforceSharePointConfluenceSQL DatabasesS3 / GCSSAP / ERPREST APIsPDFs & DocsSlack / TeamsCustom Sources

From slide-deck to working pilot in 6 weeks.

We move fast, think big, and deliver alongside you — turning bold ideas into real progress, quickly and visibly.

Book a 30-min demo
Solution Architecture

Enterprise-grade, end to end

Four integrated layers — interfaces, platform, data integration, and your sources — all deployed within your private cloud or on-premise environment.

Layer 1 — User Interfaces
Web chatSlack / TeamsREST APIYour app / UISDK embed
Layer 2 — Sphere RAG Platform
API gateway + authOrchestration engineVector storeLLM connectorRBAC + governanceAudit loggingObservabilityCost dashboard
Layer 3 — Data Integration Layer
Pre-built connectorsIngestion & chunkingEmbedding pipelineWebhook triggers
Layer 4 — Your Existing Data Sources
SalesforceSharePointSQL / DatabasesS3 / GCSConfluenceSAP / ERPREST APIsPDFs & Docs

Fully deployed on your private cloud or on-premise — no data ever leaves your environment.

Key Benefits

What you get with Sphere RAG

Eliminate hallucinations

Every answer is grounded in retrieved, verifiable content from your data — with source citations your teams can trust.

Real-time, always current

No retraining needed. As your data changes, answers update automatically — no stale AI, no maintenance windows.

Full data sovereignty

Everything runs within your private cloud or on-premise environment. Your data never touches third-party servers.

Any LLM, any model

Works with GPT-4, Claude, Llama, Mistral, and any future model — you're never locked into a single provider.

Role-aware responses

Permission-aware RBAC ensures each employee only sees what they're authorized to see — enforced at the retrieval layer.

Fraction of the cost

RAG costs a fraction of fine-tuning or custom model training, with faster time-to-value and no retraining overhead.

Book a Demo
Use Cases

Built for every team, every industry

Sphere RAG adapts to your workflows — from customer support to engineering, sales to compliance.

Instant policy answers

Support agents get precise answers to complex policy questions drawn directly from your internal documentation — no tab-switching, no delays.

Customer-facing AI assistant

Deploy a public chatbot powered by your product knowledge base that gives accurate, on-brand answers 24/7 — without hallucinating.

Ticket resolution acceleration

Automatically surface relevant past tickets, runbooks, and escalation guides to reduce average handle time significantly.

Multilingual support

Combine your proprietary knowledge with LLM translation capabilities to serve global customers in their native language — accurately.

Security & Governance

Enterprise security is not an add-on. It's the foundation.

Sphere has full security and governance infrastructure already in place — from role-based access control to audit logging and PII masking. We built this for enterprise from day one.

Private cloud / on-premise deployment
Your data never touches a third-party server. Deploy in AWS, Azure, GCP, or your own data center — fully within your security perimeter.
Role-based access control (RBAC)
Retrieval respects your existing permissions model — users only see data they're authorized to access, enforced at the retrieval layer.
Full audit logging
Every query, retrieval event, and response is logged for compliance, debugging, and governance review.
PII detection & masking
Automatic detection and redaction of personally identifiable information before data enters the LLM context window.
SSO / enterprise identity integration
Integrates with Okta, Azure AD, and any SAML/OIDC provider — no new identity management layer required.

Certifications & Compliance

SOC 2 ReadyHIPAA CompliantGDPR ReadyISO 27001 AlignedCCPA Ready

Deployment Options

AWS VPCAzure PrivateGCP VPCOn-Premise

Supported LLMs

GPT-4 / o-seriesClaudeLlama 3MistralCustom / BYOM
How to Get Started

From kickoff to production in weeks, not months

Sphere's proven deployment process removes the guesswork. We've refined every step across 5+ successful enterprise deployments.

01 / DISCOVER
Needs assessment
We map your use cases, data sources, and success metrics in a structured discovery session with your team.
02 / DESIGN
Architecture & data plan
We design the retrieval architecture, data integration strategy, and security model tailored to your environment.
03 / DEPLOY
Build & launch
We implement connectors, vector pipelines, and LLM integration — with rigorous testing and validation before go-live.
04 / GROW
Ongoing managed support
Continuous monitoring, model updates, and new data source integrations as your AI footprint expands.
Customer Results

Deployed. Proven. Delivering ROI.

Our enterprise customers share what changed when their proprietary data met AI.

“Sphere's platform provided true role-based access control (RBAC), enterprise AI governance, and configurable AI guardrails that enabled us to securely scale Retrieval-Augmented Generation (RAG) use cases across the organization with confidence. Their ability to support private cloud deployment, secure AI architecture, and enterprise-grade governance capabilities was ultimately the deciding factor for us.”

Aleks Gimelshteyn
VP, Security Systems Architect, Enfusion

“The business case for Sphere's enterprise RAG solution was clear from the start. We replaced a projected $400K AI model fine-tuning initiative with a secure Retrieval-Augmented Generation (RAG) solution deployed in just six weeks. Unlike traditional fine-tuned AI models that quickly become outdated, Sphere's RAG architecture continuously stays current as our enterprise data evolves — without the cost and operational overhead of retraining models. The combination of rapid deployment, lower AI implementation costs, secure enterprise integration, and real-time access to trusted organizational knowledge was the true differentiator for us.”

Michael Minkovich
Enterprise Security Architect, JM Family Enterprises

“Sphere's enterprise RAG solution transformed how our support team accesses and delivers critical knowledge. Our teams can now answer complex policy and compliance-related questions in seconds instead of minutes, dramatically improving operational efficiency and response times. The improvement in AI answer accuracy, consistency, and knowledge retrieval quality was immediate and measurable. Sphere's Retrieval-Augmented Generation (RAG) platform enabled us to provide faster, more reliable support experiences while ensuring responses remained grounded in trusted enterprise data.”

Ken Gatz
CEO, ProSeeder

Common questions

RAG is an AI architecture that retrieves relevant information from a specified data source before passing it to a large language model, so the answer is grounded in real, current data rather than the model's training corpus.
Consumer tools use public training data; enterprise RAG uses proprietary company data, runs inside security perimeters, enforces access permissions, and provides source citations.
Fine-tuning permanently bakes knowledge into model weights — it's expensive (typically $200K+ for an enterprise project), takes months, and becomes outdated the moment your data changes.
Choose RAG for regularly changing knowledge (policies, product docs, CRM data), source citations, or multiple data sources. Choose fine-tuning for style adaptation or narrow skills independent of factual knowledge.
Cloud RAG products (ChatGPT Enterprise, Microsoft Copilot, Glean) send your queries and retrieved data through a vendor's infrastructure. Private RAG, like Sphere's solution, runs the entire pipeline inside your own environment.
Yes. Sphere RAG deploys in AWS, Azure, GCP, or fully on-premise including air-gapped environments without external connectivity.
No. The entire RAG pipeline runs inside your private cloud or on-premise infrastructure. Your data and queries never pass through Sphere's servers or any third-party LLM provider.
Sphere RAG is LLM-agnostic. We support OpenAI GPT-4 and o-series, Anthropic Claude, Meta Llama 3, Mistral, and any custom or open-source model you deploy.
Most Sphere RAG deployments go from kickoff to production in 6–8 weeks. This includes discovery, architecture design, connector setup, testing, and go-live.
Enterprise RAG typically costs significantly less than fine-tuning, well below the $200K typical for enterprise fine-tuning projects, with no ongoing retraining expenses as data changes.
Salesforce, SharePoint, Confluence, SQL databases, Amazon S3, Google Cloud Storage, SAP, REST APIs, PDFs, Word documents, Slack, Microsoft Teams, and any custom source with an API.
Sphere RAG enforces your existing permissions at the retrieval layer. When a user submits a query, the system only retrieves documents and data the user's identity is authorized to access.
RAG dramatically reduces hallucinations because every answer is grounded in retrieved content from your real data, with source citations users can verify.
Sphere RAG is HIPAA compliant, SOC 2 ready, GDPR ready, ISO 27001 aligned, and CCPA ready. Because the platform deploys inside your own infrastructure with no data egress to third parties, it inherits the compliance posture of your existing environment.
Building a production-grade enterprise RAG system in-house typically takes a 4–6 person team 9–12 months, whereas Sphere delivers equivalent capability in 6–8 weeks.

Turn your data into your AI advantage

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