Sphere Partners

Custom RAG Development Services

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

Talk To Our Experts

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

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 They hallucinate, miss important details, and fine-tuning is costly and quickly outdated.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

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, 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.

Download

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. 1. Discovery & Assessment

    Understand business context, data assets, and KPIs.

  2. 2. Data Audit

    Identify high-value data sources, define ingestion rules.

  3. 3. Architecture Blueprint

    Design retrieval and generation workflows.

  4. 4. Prototype Build

    Implement test environment with real queries.

  5. 5. Integration & Security Setup

    Deploy to production with governance controls.

  6. 6. Training & Handover

    Enable teams to manage content and measure ROI.

  7. 7. Optimization & Scale

    Add new data, refine prompts, expand across departments.

Hear from

our clients
Lee Ebreo

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 and if we did not partner with Sphere to help us achieve our goals.

Selah Ben-Haim

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).

Ben Crawford

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.

Mark Friedgan

Mark Friedgan

CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. As an offshore team, they have been an integral part of our organization and we plan to continue growing with them.

René Pfitzner

René Pfitzner

Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and help push our product forward. They are easy to work with, tech-savvy and proactive.

Bruce Burdick

Bruce Burdick

Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. I've found that they are true partners in the success of our business.

Jemal Swoboda

Jemal Swoboda

CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise, but more importantly, they have helped us build a culture of excellence within our team.

Arthur Tretyak

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… and for a fraction of the cost. Our experience with Sphere has been exceptional.

Lee Ebreo

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 and if we did not partner with Sphere to help us achieve our goals.

Selah Ben-Haim

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).

TOP AI CODE GENERATION COMPANY UNITED STATES 2025

TOP AI TEXT GENERATION COMPANY FLORIDA 2025

TOP APP development COMPANY manufacturing 2025

TOP artificial intelligence COMPANY united states 2025

TOP chatbot COMPANY united states 2025

TOP recommendation systems COMPANY united states 2025

Sphere in Numbers

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

Get the Right Talent now

0

Years of Excellence

0+

Projects Delivered

0

Countries

Globally diverse, community-focused

0+

Clients

top 20 average 8+ years

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

Get in Touch

Latest from Our AI Blog

How to Choose an AI Software Development Company (And What to Watch Out For)

Not all AI software development companies are equal. Learn what separates firms that truly build with AI from those that just use the word. Includes real questions to ask and red flags to avoid.

Agentic RAG vs Traditional RAG vs ChatGPT

Agentic RAG costs 3-10× more than traditional RAG and adds 2-5× latency. Here's when each approach wins in 2026 — with the numbers Progress and others leave out.

The 12 Best Enterprise RAG Platforms and Tools in 2026

Compare 12 leading enterprise RAG platforms in May 2026 — Glean, SphereIQ, Cohere, Vectara, AWS Bedrock, LangChain, LlamaIndex and more. Pricing, compliance, sovereignty trade-offs.

When to Replace Your SaaS Tools with Custom AI Software (And How to Know It's Time)

SaaS made sense a decade ago. For many businesses today, custom AI-powered software delivers better ROI, faster. Here’s how to know when to make the switch, and how to do it without disrupting your operations.

We'd love to hear from you!

Please provide your contact details, and our team will get back to you promptly.

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, and 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: Discovery & Assessment to clarify use cases, users, and KPIs; Data Audit to locate high-value data sources and define ingestion rules; Architecture Blueprint to design retrieval, ranking, and generation flows; Prototype Build to implement a working RAG assistant with real queries; Integration & Security Setup to connect to your stack with proper controls; Training & Handover to enable your teams to manage and govern content; Optimization & Scale to 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.), and 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, and 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.

Get Started Today

Contact Us Now