Custom RAG Development
Services
Stop relying on generic AI models. Deploy RAG inside your infrastructure for accurate, private, real-time insights.
Organizations around the world trust us
Why Businesses Choose RAG
Out-of-the-box AI models don’t know your business. They hallucinate, miss important details, and fine-tuning is costly and quickly outdated. Moreover, sending sensitive data directly to third-party LLMs creates security, compliance, and privacy risks.
RAG takes a different approach when your data becomes your differentiator. Unstructured knowledge — documents, PDFs, CRM records, manuals, tickets, reports — is indexed and retrieved in a controlled way, then used by the model to generate answers. This gives you an AI layer that understands context, respects access rules, and stays current as your content evolves.
- Turns proprietary data into a competitive advantage
- Gives LLMs memory, precision, and business context
- Keeps sensitive information under your security and governance controls
- Increases the value of existing LLMs without constant retraining
- Updates automatically as new content, files, and systems are added
Enterprise Use Cases for Your RAG
Regulatory Intelligence
Compliance teams face overlapping regulations and internal policies scattered across thousands of documents. Research across them is time-consuming. RAG connects all internal and external regulatory texts, so you can find instant, source-cited answers right in the chat.
Business Gains with RAG
~35% faster research, up to 30% shorter audit preparation cycles.
Technical Knowledge Assistant
Field engineers rely on decades of fragmented manuals, ERP logs, and service notes, so troubleshooting often turns into guesswork and tab-hopping. RAG unifies this technical knowledge and returns precise, step-by-step procedures based on similar historical fixes.
Business Gains with RAG
Up to 30% faster issue resolution, around 20% fewer repeat incidents on the same assets.
Customer-Facing AI Support
Product support teams drown in repetitive questions, while legacy chatbots fail to understand context or new releases. RAG connects product docs, release notes, and community threads into one source of truth that powers an AI assistant with accurate, up-to-date answers.
Business Gains with RAG
Self-service resolutions grow by ~20–30 percentage points, human ticket volume per customer drops by ~15–20%.
Medical Knowledge Search
Healthcare professionals struggle to quickly align internal protocols with the latest clinical guidelines during time-critical cases. RAG retrieves verified, specialty-specific guidance and similar anonymized cases directly in their workflow.
Business Gains with RAG
Time to find relevant clinical information decreases by ~50%, guideline adherence improves by ~10–15%.
Where Sphere Helps
Data Preparation and Ingestion
Your wikis, CRMs, ticketing tools, and file stores are connected, cleaned, and broken into small, searchable chunks. The accelerator keeps them in sync as content changes.
Retrieval Pipeline Engineering
Get the full retrieval flow that addresses your everyday pains. Our team takes care of how queries are interpreted, how context is selected and ranked, how much to pull, and how it's wrapped for the model.
Vector Store Architecture
We set up and run the vector store — the “memory” behind your assistant — so content is stored, tagged, and retrieved fast at real traffic levels, without you choosing engines or indexes.
Hybrid Search (semantic + keyword)
Search that understands natural questions and still finds exact IDs, codes, and phrases. One query can be fuzzy or precise, and the assistant handles both in one place.
Integration with Leading LLMs
The retrieval layer is connected to OpenAI, Anthropic, Azure OpenAI, or your private models through one simple interface. Changing or adding a model later is a configuration change, not a new project.
Governance and Security
Existing access rules are respected end-to-end, sensitive fields are masked, and every answer can be traced back to its sources. Aligned with GDPR, HIPAA, SOX, and your internal policies.
Unlock Every Benefit of RAG
Retrieval-Augmented Generation transforms your existing knowledge base into a strategic advantage — when implemented with precision.
No Hallucinations
Answers stay anchored in your internal sources instead of the model’s guesses.
Real-Time Data
Ask questions in chat and get live answers from your systems and documents.
Enterprise Security
Respects existing roles, permissions, and keeps sensitive data private and traceable.
Personalised Outputs
Each user sees only the data and actions allowed by their role.
No Retraining Needed
RAG uses your data with existing LLMs instead of expensive retraining cycles.
Team-Wide Insights
Product, ops, and leadership access the same knowledge base through one assistant.
Our Process for Custom RAG Development
Your data is your differentiator. Sphere builds custom RAG systems that ground AI in your proprietary content, so every answer becomes accurate, contextual, and valuable.
Discovery & Assessment
Understand business context, data assets, and KPIs.
Data Audit
Identify high-value data sources, define ingestion rules.
Architecture Blueprint
Design retrieval and generation workflows.
Prototype Build
Implement test environment with real queries.
Integration & Security
Deploy to production with governance controls.
Training & Handover
Enable teams to manage content and measure ROI.
Optimization & Scale
Add new data, refine prompts, expand across departments.
Is Your Data Ready for Retrieval-Augmented AI?
RAG depends on clean, connected, well-structured knowledge. If your content lives in PDFs, emails, manuals, or legacy systems, the right preparation turns all of it into a powerful retrieval layer. Use our whitepaper to identify gaps in your data landscape and prepare your organization to deploy AI with confidence.
DownloadClient case study · Financial services
PetroLedger saved $1.2M/year — and cut onboarding from 12 months to 5
With 40% of senior staff nearing retirement, PetroLedger faced critical institutional knowledge loss. Sphere built a RAG-powered Digital Twin platform that converted decades of expertise into an AI employees could query directly — policies, ERP workflows, and compliance guidelines, all cited from source documents. Rolled out enterprise-wide in six months.
Business Gains with RAG
120% faster onboarding · 90% knowledge retention · $1.2M saved annually · 100% compliance adherence
Read the full case study →We Work With Your AI Stack
Sphere’s Data & AI engineers are fluent in the tools that power today’s most advanced RAG systems.
Hear from our clients
“These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.”
Lee Ebreo
VP of Engineering, CreditNinja
“Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park.”
Selah Ben-Haim
VP of Engineering, Prominence Advisors
“With Sphere, we were able to migrate in half the time it would take to train an additional FTE… and for a fraction of the cost. Our experience with Sphere has been exceptional.”
Arthur Tretyak
Founder & CEO, IntegraCredit
Frequently Asked Questions
What is Retrieval-Augmented Generation (RAG) and why do enterprises need it?+
RAG is an AI architecture where a large language model retrieves information from your verified data sources — documents, PDFs, wikis, CRM, tickets, logs — before generating an answer. For enterprises, RAG reduces hallucinations, improves answer accuracy, and aligns AI outputs with real business logic instead of public internet training data.
How is a custom RAG solution different from a standard chatbot or generic LLM?+
A generic chatbot or LLM answers based mostly on its training corpus. A custom RAG solution connects directly to your internal content, indexes it in a vector database, and retrieves relevant passages at query time. Every answer is grounded in your own documentation, policies, and records, with citations and full traceability.
How long does it typically take to launch a RAG pilot in production?+
For a focused use case with a well-defined data scope, many clients see a working RAG assistant in a few weeks, not months. Timelines depend on data complexity, integrations, and security approvals, but the RAG approach is usually much faster than retraining or fine-tuning a large model from scratch.
How do you handle security, governance, and compliance?+
Sphere designs RAG systems with enterprise security from day one: role-based access control, SSO/IdP integration, redaction of sensitive fields, full audit trails, and architectures aligned with GDPR, HIPAA, and SOX. Your data stays in your environment; we design around your regulatory requirements.
Can we start with a small RAG pilot before scaling across the enterprise?+
Yes. Most clients begin with a single high-value use case — regulatory research, support knowledge, or engineering documentation. Once the pilot proves value and governance, we extend the same RAG foundation to additional departments, data sources, and workflows.
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