Data Intelligence Platform & Readiness

Nobody can tell you who owns that table. That’s the actual problem.

Not a lack of data — a lack of lineage, ownership, and access rules on the data you already have. Sphere maps the gap in 2-3 weeks and ships a governed pipeline in 6-12, on the stack you’re already running.

4.9

“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 and continuously expand our AI capabilities with confidence.”

Ilya Kaminsky, CIO, Summit Financial Management

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If the real blocker is ownership and lineage, not another dashboard — start with governance.

Master data management, access control, and pipeline work on AWS, Azure, or Google Cloud — no forklift migration required.

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Four Things Have to Be True Before Any Data Initiative Sticks

Data Engineering

Ingestion, pipelines, warehousing, real-time processing, and integrations that connect the systems your teams already use.

Data pipeline development
Integration services
Real-time processing

Governance and Lineage

Ownership, access rules, source tracking, and auditability so teams understand where data came from and who should use it.

Master data management
Enterprise data governance
Access & lineage controls

Modernization

Legacy migration, cloud platforms, data lakes, and architecture improvements when the current environment cannot support scale.

Data warehouse modernization
Cloud data platforms
Legacy system migration

AI and Analytics Readiness

Prepare trusted data for dashboards, RAG systems, agents, and decision-support tools that need reliable source material.

Self-service analytics platforms
RAG data preparation
Data observability

Three Ways In, Depending on What You Actually Know Right Now

Most teams don’t need a rebuild — they need someone to tell them, honestly, whether the gap is technical or organizational. That’s a different scope than “build us a data platform.”

Lighter

Data Readiness Assessment

Map your data landscape, identify the highest-risk gaps, and create a practical remediation plan before a larger build begins.

2-3 weeks · Fixed scope

Most Direct

Embedded Data Engineering Pod

A Sphere team builds the governed pipeline, integrations, dashboards, and monitoring inside your environment.

6-12 weeks · Production build

AI Foundation

RAG and Agent Readiness

Prepare approved sources, access rules, indexing, and lineage before enterprise AI is connected to critical business knowledge.

AI-ready data · Governed sources

Sphere vs. Standing Up an In-House Team

The fastest path to governed data usually isn’t a new hire req — it’s a fixed-scope engagement that tells you exactly what to build before you commit a headcount budget.

2-3 wks

Sphere fixed-scope readiness assessment

6-9+ mo

Typical timeline to hire/ramp an in-house data governance/engineering team

80%

Data & analytics governance initiatives Gartner predicts will fail by 2027

How an Engagement Actually Runs, Week by Week

Weeks 1-3

Data Readiness Assessment

Map the data landscape, rank gaps by risk and owner, and produce an actionable remediation plan — not a maturity-model deck.

Weeks 3-6

Choose Your Path

Move into an Embedded Data Engineering Pod or RAG and Agent Readiness track, scoped to the systems actually causing the pain.

Weeks 5-12

Build, Govern, Integrate

Ship the governed pipeline, master data management, access controls, and integrations inside your existing cloud environment.

Week 12+

Monitoring & Handoff

Data observability, lineage dashboards, and knowledge transfer so your team owns and operates the governed layer with confidence.

The Proof

Trusted By

Not a logo wall for its own sake — these are teams that had a specific data problem and needed it fixed, not re-explained.

Client Organizations Served

Countries

Net Promoter Score (2026)

On-Time Delivery

In Their Own Words, Not Ours

Real clients, real engagements — not stock testimonials.

Sheldon Gilbert

CEO, Proclivity Media

John Krauze

VP of Product, Nextcapital

Chirag Pancholi

CEO, WiseMuv

Steven Maloney

CEO, Pianoamigo.com

Ntiedo Etuck

CEO, Your Guru

Featured Awards

93% on-time delivery. 21+ years of enterprise data engineering.

TOP AI CODE GENERATION COMPANY UNITED STATES 2025

TOP AI TEXT GENERATION COMPANY FLORIDA 2025

TOP APP DEVELOPMENT COMPANY MANUFACTURING 2025

GLOBAL RECOGNITION AWARDS 2026

TOP ARTIFICIAL INTELLIGENCE COMPANY UNITED STATES 2025

TOP CHATBOT COMPANY UNITED STATES 2025

TOP RECOMMENDATION SYSTEMS COMPANY UNITED STATES 2025

The report queue disappeared. Nobody filed a ticket to make that happen.

A healthcare retailer’s data team stopped fielding the same 10 requests every week once the reporting layer could answer them itself. That’s the actual measure of a data platform working — fewer requests, not a bigger dashboard.

Talk to a Data Engineer

Technologies We Leverage

We Build on the Stack You Already Run

No forced replatform — Sphere adds the missing governance, pipeline, and access-control layers to your existing systems.

Data & Storage

SnowflakeDatabricksPostgreSQLAzure Cognitive Search

Governance & MDM

Informatica MDMCollibraData lineage toolingRole-based access control

Pipelines & Orchestration

AirflowPrefectdbtLangChain

AI & Analytics

OpenAI GPTClaudeGeminiElastic

Infrastructure

AWSMicrosoft AzureGoogle Cloud

What Changed When the Data Layer Got Fixed

Spotlight · Renewable Energy

SolarX: A €1.24M Penalty, Defused Three Weeks Early

Six AI desks watching €405M of contracted solar and storage value across five countries — unified data caught a 52-day schedule slip before its liquidated-damages clause could trigger.

€1.24M

Penalty exposure defused three weeks ahead of the deadline that would have locked it in

€4.8M

Liquidated-damages exposure tracked in real time across the full portfolio

Retail: 142 Minutes Recovered

An AI operations bureau automated 87 of 91 routine store decisions in one shift, returning 142 minutes to a general manager without adding headcount.

Construction: $680K/Year Bug Found

A governed semantic data layer unified six merged systems and surfaced a markup formula bug worth $680K a year — hiding in plain sight across two estimating platforms.

Retail: 47 SKUs Caught Before the Fire Sale

A markdown optimization model flagged at-risk inventory early, projecting $41,200 in recovered margin across a multi-store fleet.

Industries Sphere Serves

Where Governed Data Layers Tend to Matter Most

Financial Services / Fintech

Healthcare / Life Sciences

Retail / E-commerce

Energy & Utilities

Construction / Manufacturing

Questions We Get Before Anyone Signs Anything

Usually not — that's the answer that makes an engagement bigger, not the one that fixes the problem. Most of the time the systems are fine; what's missing is ownership, lineage, and access rules on top of them. Replatforming only gets recommended when the current environment genuinely can't support what you're trying to do.

Most governance efforts stall because they start with a framework instead of a specific, painful gap — usually a fixed-scope assessment forces that prioritization before any tooling gets bought. If the last attempt produced a policy document nobody used, the issue probably wasn't effort, it was starting with documentation instead of the two or three systems actually causing the pain.

A ranked list of specific gaps (by system, by owner, by risk) and what fixing each one would take — not a maturity-model slide. It's built to be handed to whoever approves the next budget line, not filed away.

It's usually the same root problem wearing a different label. A RAG system or agent is only as reliable as the access rules, freshness, and lineage on the data underneath it — which is exactly what a governance gap looks like from the other direction. Fixing one tends to unblock the other.

If you can't yet say which three systems are the actual problem, start with the assessment — it's the thing that tells you that. If you already know and just need it built, the pod skips straight to production work inside your environment.

It's part of the same governance layer — MDM, lineage tracking, and access control tend to be the same underlying work regardless of which one shows up first in the request.

No — the work happens on whichever of the three you're already running. Nobody's paying for a cloud migration to get a governance layer.

No. Everything is built inside your existing cloud environment — governance and access controls wrap around your infrastructure, they don't require a separate copy of your data to sit somewhere else.

Tell Us What’s Actually Broken

Not a discovery call — a working session with a Sphere data engineer to figure out whether this is a two-week fix, a twelve-week build, or something you don’t need us for at all.

A Sphere engineer responds within 1 business day · your data stays in your cloud.

4.9

Learn & Evaluate

Go Deeper

Nobody can tell you who owns that table. That’s the actual problem.

Get Your Free Data Readiness Assessment