Custom RAG
 Development Services

Stop relying on generic AI models. Deploy RAG inside your infrastructure for accurate, private, real-time insights.

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 They hallucinate, miss important details, and fine-tuning is costly and quickly outdated.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.

Businesses choose RAG because it:

  • 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.

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.

Technical Knowledge Assistant

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%.

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%.

Medical Knowledge Search

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, so answers stay relevant and stable.

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 (“show issues with payment delays”) or precise (“order #58273”), 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. The setup stays 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. However, the real impact comes when it’s implemented with precision. Each RAG solution from Sphere helps you unlock measurable efficiency, accuracy, and speed while keeping your data secure and compliant.

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.

We Work With Your AI Stack

Sphere’s Data & AI engineers are fluent in the tools that power today’s most advanced retrieval-augmented systems, and ready to integrate them directly into your operations.

Our stack includes:

  • LLMs and Frameworks: OpenAI GPT, Claude, Mistral, Llama 3, Gemini, Mixtral
  • Vector Databases: Pinecone, Weaviate, FAISS, Milvus, Chroma, Elastic Search
  • Data & Storage: Snowflake, Databricks, PostgreSQL, Azure Cognitive Search
  • Pipelines & Orchestration: LangChain, LlamaIndex, Haystack, Prefect, Airflow
  • Infrastructure & Security: AWS Bedrock, Azure OpenAI Service, Google Vertex AI

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.

Our Process for Custom RAG Development

Your data is your differentiator. Sphere builds custom Retrieval-Augmented Generation systems that ground
AI in your proprietary content, so every answer becomes accurate, contextual, and valuable.

1. Discovery & Assessment

Understand business context, data assets, and KPIs.

2. Data Audit

Identify high-value data sources, define ingestion rules.

3. Architecture Blueprint

Design retrieval and generation workflows.

4. Prototype Build

Implement test environment with real queries.

5. Integration & Security Setup

Deploy to production with governance controls.

6. Training & Handover

Enable teams to manage content and measure ROI.

7. Optimization & Scale

Add new data, refine prompts, expand across departments.

Hear from Our Clients

Sphere Partners
Selah Ben-Haim VP of Engineering at Prominence Advisors

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 (including the rescue of a project that was previously bungled by another vendor).

Sphere Partners
Ben Crawford Senior Product Manager at Enova Financial

I would expect to be delighted. It’s been a really positive experience, working with Sphere, and I would expect you to have the same.

Sphere Partners
Mark Friedgan CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. They bring innovative and well-considered solutions, consistently surpassing my expectations.

Sphere Partners
René Pfitzner Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and work with us.

Sphere Partners
Bruce Burdick Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. Their combination of offshore talent, pricing, and shift offsetting is hard to beat. They provide crucial augmentation to our in-house team. We simply couldn't achieve our production ambitions without their service.

Sphere Partners
Jemal Swoboda CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise to complete their tasks successfully, with the team easily scaled in either direction. The deliverables are always high-quality.

Sphere Partners
Arthur Tretyak Founder and CEO at IntegraCredit

With Sphere, we were able to migrate in half the time it would take to train an additional FTE…

Sphere Partners
Lee Ebreo VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system. We augmented our development team capabilities using Sphere’s developer, who works very well with our Dev Lead in Chicago. Sphere’s developer was an expert in the new system, and continues to be an expert as we evolve it.

TOP AI CODE
Generation COMPANY
UNITED STATES 2025

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Generation COMPANY
florida 2025

TOP APP development COMPANY
manufacturing 2025

TOP artificial intelligence COMPANY
united states 2025

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COMPANY
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TOP recommendation systems COMPANY
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20

Years of Experience

230

Delivered Projects

200+

Senior Specialists

94%

Satisfaction Rate

Sphere in Numbers

We understand that actions speak louder than words and numbers
but here are some key facts about us.

Discover more of what we’ve built to drive growth and innovation.

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Frequently asked question

Retrieval-Augmented Generation (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.

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.

Typical use cases include:

  • Regulatory and policy intelligence for compliance teams
  • Technical knowledge assistants for engineering and field operations
  • Customer-facing AI support that actually understands your product stack
  • Medical and clinical knowledge search on top of approved guidelines
  • Internal knowledge bases for sales, operations, and leadership

If your teams are wasting time searching across disconnected systems, a custom RAG pipeline can make that knowledge instantly accessible.

Sphere follows a defined RAG development process:

  1. Discovery & Assessment – Clarify use cases, users, and KPIs.
  2. Data Audit – Locate high-value data sources and define ingestion rules.
  3. Architecture Blueprint – Design retrieval, ranking, and generation flows.
  4. Prototype Build – Implement a working RAG assistant with real queries.
  5. Integration & Security Setup – Connect to your stack with proper controls.
  6. Training & Handover – Enable your teams to manage and govern content.

Optimization & Scale – Add new sources, tune prompts, and expand usage.

We work with leading models and platforms, including OpenAI GPT, Claude, Mistral, Llama 3, Gemini, and Mixtral, and integrate with tools like LangChain, LlamaIndex, Haystack, Pinecone, Weaviate, FAISS, Milvus, Chroma, Snowflake, Databricks, Elastic, Azure Cognitive Search, AWS, Azure, and Google Cloud. Your RAG system can run on public cloud, hybrid, or private deployments.

Instead of letting the model “guess,” RAG retrieves relevant passages from your indexed content and injects them into the prompt. The model is instructed to answer only using that context. This grounding dramatically reduces hallucinations, makes answers consistent with your policies, and provides links or citations back to original sources.

Sphere designs RAG systems with enterprise security from day one:

  • Role-based access control and data permissioning
  • Integration with SSO/IdP and existing security policies
  • Redaction of sensitive fields where required
  • Full logging, audit trails, and query monitoring
  • Architectures aligned with GDPR, HIPAA, SOX, and internal governance

Your data stays in your environment or cloud tenancy; we design around your regulatory requirements.

You don’t need perfectly clean data to begin. You do need:

  • Clear priority sources (policies, manuals, knowledge base, tickets, logs, etc.)
  • Access to those systems (file shares, CMS, CRM, data warehouse, etc.)
  • Alignment on which users and workflows will benefit first

Sphere handles extraction, cleaning, chunking, embedding, and indexing of structured and unstructured content as part of the engagement.

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.

Common RAG KPIs we track with clients include:

  • Reduction in search and research time per request
  • Higher first-contact resolution rates in support
  • Faster onboarding for new staff
  • Fewer escalations to expert teams
  • Increased usage of internal knowledge assets

We define target metrics in the Discovery phase and instrument the system so you can see impact over time.

Yes. RAG pipelines can index content in multiple languages and use multilingual or language-specific models. We can configure language-aware retrieval, domain- or region-specific indexes, and routing to different LLMs based on user, locale, or query type.

  • Vector search finds similar documents or passages based on embeddings.
  • RAG combines vector (and keyword) retrieval with an LLM that reads the retrieved context and generates a final answer.
  • Fine-tuning alters model weights using labeled examples, which is slower, costly, and harder to govern.

For most enterprise knowledge use cases, RAG delivers faster, safer value while keeping the base model unchanged.

We typically work as an extension of your team: architecture and design alongside your data engineers, MLOps, or platform team; knowledge and governance with business and compliance stakeholders. The goal is to leave you with an understandable, maintainable RAG stack—not a black box.

Yes. Most clients begin with a single high-value use case (e.g., 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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