Answers your team can trust, with the source attached.
Sphere builds RAG systems on your real documents and data: every answer cites the clause, page or record it came from, and people only see what they're allowed to see. A tax advisory firm went from kickoff to production in five weeks.
Book a RAG architecture review
Book a RAG architecture review
Tell us which documents your team searches most. We'll map the sources, access rules and retrieval approach before you commit.
Technology partners
AWS
Google Cloud
Microsoft Azure
Databricks
Snowflake
Anthropic Claude
Search was never the hard part. Trust is.
Pointing a model at a folder of PDFs takes an afternoon. Getting answers people will act on takes the right retrieval, current documents, permissions and a way to prove the answers are right.
Sphere builds the right-hand column. Here's how.
See the four phasesFrom scattered documents to cited answers.
We start with the questions your people actually ask, then build retrieval around your documents, not a generic template.
- Weeks 1–2
Discovery & data audit
We map where the knowledge lives, who can see what and the questions people ask most. Then we turn those questions into a test set.
You getA source map, the retrieval architecture and a fixed scope. - Weeks 2–4
Ingestion & retrieval
We build the ingestion pipeline, tune chunking to your document types and set up hybrid search with metadata filters, so the right passage comes back first.
You getRetrieval running on your real content, with a quality baseline. - Weeks 4–8
Evaluate & ship
We benchmark precision, faithfulness and answer relevance, wire in permissions and audit logging, and roll it out where your team already works.
You getA production RAG system that cites its sources. - Week 8 onward
Run & grow
We monitor answer quality and usage, add new sources as they come online and hand your team the runbooks.
You getQuality dashboards and a system your team can extend.
RAG systems in production today.
Knowledge systems Sphere took live, and the numbers their owners report.
from kickoff to production
A tax advisory firm put every jurisdiction's guidance one question away.
Hybrid vector and keyword retrieval, filtered by jurisdiction, with role-based access, evaluation gates and an audit trail designed in from the start.
Read the tax advisory storyestimated faster contract research
Navy Pier gets clause-backed answers from its whole contract library.
An Azure platform on top of SharePoint ingests leases and amendments, tracks which terms are currently in force and answers plain-language questions with clause-level citations.
Read the Navy Pier storyfaster technical search
Technicians get answers from the current SOP revision, with the citation.
A RAG assistant extracts knowledge from GxP drawings and SOPs, answers with current-revision citations and connects to the CMMS and QMS ticketing systems.
Read the life sciences storysaved every year from faster onboarding and retained expert knowledge
PetroLedger kept its retiring experts' knowledge.
A Digital Twin Knowledge Platform captures how senior experts work and gives every new hire instant, grounded answers from the company's own policies and documents.
Read the PetroLedger storyFrom the people who signed off on it.
What client leaders say about the AI systems Sphere put into production for them.
“Sphere deployed a production-ready RAG pipeline in five weeks. […] This was not a generic AI implementation; it was an enterprise knowledge system designed for a highly regulated professional services environment.”
“Sphere's AI team has been a true strategic partner in helping us elevate our AI capabilities and modernize critical business operations. […] An exceptional partner for companies looking to operationalize AI and drive measurable ROI.”
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool.”
“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.”
“Sphere was a valuable partner in helping us advance our AI-native product lifecycle initiatives. Their combination of product thinking, engineering expertise, and execution discipline enabled the delivery of impactful solutions.”
“Sphere's AI-powered software and product engineering capabilities enabled us to achieve our objectives faster while improving operational efficiency across the software development lifecycle.”
Architects, not account managers.
Your first call is with the people who design Sphere's production AI systems, and they stay with the project after you sign.
Built products and led high-scale engineering teams at HP Software, Gett and Google before leading Sphere's technology practices.
Architects enterprise AI platforms that turn complex operations into secure, production-ready systems built to hold up at real-world scale.
Led high-scale operational systems and modernization at Gett and bp pulse. Now builds automation-driven AI platforms and intelligent workflows.
More than a decade designing and scaling software from first MVP to enterprise-level systems across FinTech, transportation and real-time streaming.
Built on your stack. Owned by you.
Deployed to your AWS, Azure or Google Cloud account. Your data doesn't leave your environment.
Repositories, documentation and runbooks are yours from day one. Nothing only Sphere can maintain.
The architecture keeps the model replaceable, so moving from one provider to another is a config change, not a rebuild.
OpenAI
Anthropic Claude
Google Gemini
Mistral
Llama
AWS Bedrock
Azure OpenAI
Google Cloud
Databricks
Snowflake
Salesforce
SAP
NetSuite
Dynamics 365
HubSpot
Atlassian
Kubernetes
Docker
Terraform
monday.comModels, clouds, business systems and deployment tooling we implement on today.
21 years of engineering. Our top 20 clients stay 7+ years.
Independent recognition for the AI work Sphere delivers, from Clutch reviewers to global AI award juries.
Top Artificial Intelligence Company · US 2025
Top AI Code Generation Company · US 2025
Top Chatbot Company · US 2025
Top Recommendation Systems Company · US 2025
Top AI Text Generation Company · Florida 2025
Top App Development Company · Manufacturing 2025
Global Generative AI Award · 2026 Winner
Best in AI for Documentation Validation · 2026
Best in AI-Powered Invoice Auditing · 2026
Global Recognition Awards · 2026
Global Tech Award Winner · 2026
Most Innovative Technology Consulting Firm · 2026
What teams ask before they start.
Don't see yours? Ask it on the review. That's what it's for.
Book a RAG architecture reviewWhat is RAG, and why not just use ChatGPT on our files?
Retrieval-augmented generation finds the right passages in your own documents first, then has the model answer only from them, with citations. A generic chatbot on uploaded files has no permissions, no version awareness and no way to prove its answers are right.
How do you stop it from making things up?
The model answers from retrieved passages and cites them, so every claim can be checked. Before each release we measure retrieval precision, faithfulness and answer relevance against a test set built from your real questions.
Will people see documents they're not allowed to see?
No. Retrieval applies the permissions users already have in SharePoint, your drives and other sources, and every query is logged for audit.
How long does it take?
Most systems reach production in about eight weeks. With a well-defined scope it can be faster: a tax advisory firm went from kickoff to production in five weeks.
Will we be locked into a model or a cloud provider?
No. We work across OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI and open-source models like Llama and Mistral, and we design the system so the model can be swapped later. It runs in your cloud account and you own the code.
What does it cost?
It depends on the number of sources, document volume and where the answers need to appear. The first phase ends with a fixed scope and a fixed price for the build.
Your knowledge is already there. Let's make it answer.
Book a RAG architecture review with a Sphere AI architect. You'll leave with:
- A map of your sources and access rules
- The retrieval approach for your document types
- A timeline and a fixed scope for the build
Book a RAG architecture review
Tell us what your team needs to find. We'll reply within one business day to set a time.