Data your team and your AI can trust.
Most data problems aren't a lack of data. They're a lack of ownership, lineage and access rules on the data you already have. Sphere maps the gaps in 2–3 weeks and ships a governed pipeline in 6–12, on the stack you already run.
Get a free data readiness assessment
Get a free data readiness assessment
A free, fixed-scope assessment: in 2–3 weeks we map your data, rank the gaps by risk and owner, and hand you a remediation plan.
Your call is with one of oursenior data architectsTechnology partners
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Another dashboard won't fix a trust problem.
When two reports disagree, people stop using both. Before anything new gets built, the data underneath needs owners, lineage and rules.
Sphere builds the right-hand column, on the stack you already run.
See the three ways inBefore any data initiative sticks.
Most teams are strong in one or two. Pick the one that feels shakiest.
The data actually arrives, on time, every time.
Ingestion, pipelines, warehousing, real-time processing and integrations that connect the systems your teams already use.
- Pipelines with tests and failure alerts
- Integrations with CRM, ERP and finance systems
- Batch and real-time processing where it's needed
Everyone knows where a number came from, and who owns it.
Ownership, access rules, source tracking and auditability, so teams understand where data came from and who should use it.
- Master data management
- Access and lineage controls
- Owners and stewards for critical data
The platform can carry what the business needs next.
Legacy migration, cloud platforms, data lakes and architecture improvements when the current environment can't support scale.
- Data warehouse modernization
- Cloud data platforms
- Legacy system migration
Dashboards and AI read from the same trusted source.
Trusted data for dashboards, RAG systems, agents and decision-support tools that need reliable source material.
- Self-service analytics
- RAG data preparation
- Data observability
Start where you actually are, not with a rebuild.
Most teams don't need a new platform. They need someone to tell them honestly what to fix first.
Data readiness assessment
Map your data landscape, rank the highest-risk gaps and get a practical remediation plan before a larger build begins.
- Landscape and source map
- Gaps ranked by risk and owner
- Remediation plan, not a maturity deck
Embedded data engineering pod
A Sphere team builds the governed pipeline, integrations, dashboards and monitoring inside your environment.
- Governed pipelines and integrations
- Dashboards people use
- Monitoring and handoff
RAG & agent readiness
Prepare approved sources, access rules, indexing and lineage before enterprise AI is connected to critical business knowledge.
- Approved, governed sources
- Access rules AI respects
- Traceable answers
From a map of the gaps to data people rely on.
A fixed-scope assessment first, then a build scoped to the systems actually causing the pain.
Get a free data readiness assessment- Weeks 1–3
Readiness assessment
We map the data landscape and rank the gaps by risk and owner.
You getAn actionable remediation plan, not a maturity-model deck. - Weeks 3–6
Choose your path
Move into an embedded data engineering pod or the RAG and agent readiness track, scoped to the systems causing the pain.
You getA fixed scope and price for the build. - Weeks 5–12
Build, govern, integrate
We ship the governed pipeline, master data management, access controls and integrations inside your existing cloud.
You getGoverned data in production. - Week 12 onward
Monitoring & handoff
Data observability, lineage dashboards and knowledge transfer so your team owns and runs the governed layer.
You getA team that owns it, with the dashboards to prove it works.
Data that changed decisions.
Data platforms Sphere put into production, from the published case studies.
budget deviation, from 30% to under 8%
An EPC firm stopped finding cost overruns at month-end.
Excel project trackers and ERP data were consolidated into one central model with a daily refresh, feeding predictive Power BI cost and cash-flow dashboards with variance alerts.
Read the construction storymarketing ROI
Finance, marketing and sales finally looked at the same numbers.
After an assessment and roadmap for the CFO and CIO, a Google Cloud data lake combined Salesforce CRM, SAP finance and web traffic data behind campaign-effectiveness dashboards.
Read the marketing ROI storysales increase in the first six months
A maintenance company proved its value to every customer.
An Azure data lake fed by ETL from CRM and field-service data powers customer ROI portals, internal sales dashboards and a mobile service app.
Read the maintenance storypeople using the reporting system every day
The report queue disappeared.
A self-service Tableau reporting system pulls from ERP, inventory, warehouse management and e-commerce data. Reports that used to need a ticket and a long wait are delivered instantly.
Read the retail storyWant results like these?Tell us about your project. A senior architect replies within one business day.
Get a free data readiness assessmentFrom the people who signed off on it.
Leaders who trusted Sphere with the data their business runs on.
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool. Their team built a secure and scalable AI infrastructure that allows us to capture institutional knowledge, protect sensitive data within our environment, and continuously expand our AI capabilities with confidence.”
“Sphere consistently demonstrated a high level of accountability, technical expertise, and executive partnership. Their team remained deeply invested in our business outcomes and successfully supported us through complex technology and operational challenges.”
“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 that improved our platform and accelerated innovation.”
Architects who build production data systems.
Your first call is with the people who design Sphere's data and AI platforms, 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 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.
AWS, Azure or Google Cloud, on Snowflake, Databricks or the warehouse you already have.
Owners, lineage and access rules designed in from the start, not bolted on later.
Documentation, runbooks and knowledge transfer, so nothing depends on us.
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Salesforce
SAP
NetSuite
Dynamics 365
HubSpotCloud data platforms and the business systems we connect them to.
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
Data intelligence, answered directly.
Don't see yours? Ask it on the call.
Get a free data readiness assessmentWhat is data intelligence?
Turning the data you already have into something the business can rely on: connected sources, clear owners, tracked lineage and access rules, so dashboards, reports and AI tools all read from the same trusted foundation.
What happens in a data readiness assessment?
In 2–3 weeks we map your data landscape, rank the gaps by risk and owner, and hand you a practical remediation plan. It tells you whether the problem is technical, organizational or both, before you commit to a larger build.
Our AI project is stuck on data. Can you help?
Yes. We prepare approved sources, access rules, indexing and lineage so assistants, RAG systems and agents only read data they're allowed to, and answers can be traced back to the source.
How long until something is in production?
The assessment takes 2–3 weeks. An embedded data engineering pod typically ships the governed pipeline, integrations and dashboards in 6–12 weeks.
Do we have to move to a new platform?
No. We build on the cloud and data platforms you already run, whether that's AWS, Azure, Google Cloud, Snowflake or Databricks. If a migration makes sense, we'll show you why and what it costs before anything moves.
What does it cost?
The 2–3 week readiness assessment is free. For the build, we quote a fixed price once the assessment shows what actually needs to be built.
Find out what's actually wrong with your data.
Get a free 2–3 week readiness assessment. You'll walk away with:
- A map of your data landscape
- Gaps ranked by risk and owner
- A remediation plan and a fixed scope for the build
Get a free data readiness assessment
Tell us about your data and what it needs to support. We'll reply within one business day.
Your call is with one of oursenior data architects