Sphere wins 2026 Global Recognition Award
Sphere Partners
Data Intelligence

Turn scattered data into a trusted intelligence layer.

Sphere helps teams connect, govern, and modernize the data behind reporting, automation, RAG systems, agents, and operational decisions without forcing a full replatform.

Choose a starting pointSee the data story
CRM
Customer and pipeline recordsduplicate
Shared drives
Policies, decks, and documentsstale
Legacy systems
Operational and transaction datasiloed
Dashboards
Current reporting with fewer manual requeststrusted
AI systems
RAG and agents grounded in approved sourcesgoverned
Operations
Consistent data for daily decisionsusable
IngestConnect sources and normalize records
GovernApply access, ownership, and lineage
ActivateServe AI, BI, and workflows
2-3 weeksData readiness assessment
6-12 weeksFirst production pipeline
Existing stackBuild on what you already use where possible
AI + BI readyDesigned for dashboards, RAG, and automation
Where the work usually starts

The problem is rarely a lack of data.

Most teams have plenty of data. The harder issue is that the data is duplicated, stale, siloed, or difficult to trust when it matters.

01

Different systems tell different stories

Customer, product, financial, and operational records drift across tools, creating manual reconciliation work.

See the before and after
02

Reports depend on overloaded teams

Business users wait on data teams for standard answers instead of using trusted self-service reporting.

View proof
03

Access and ownership are unclear

Teams need to know who owns each source, who can access it, and how changes are tracked.

Explore governance
04

AI inherits the data problem

RAG systems, agents, and dashboards are only as reliable as the source layer beneath them.

Choose a starting point
The data story

From scattered sources to a governed intelligence layer.

The goal is not to replace every system. The goal is to make the data behind those systems reliable, traceable, and usable for the teams and tools that depend on it.

Before

CRM exportduplicates
Shared documentsstale
Legacy databaseno owner
Support ticketssiloed
Manual report queueslow

After

Automated ingestionconnected
Change-aware indexingcurrent
Access and lineage controlsgoverned
Unified reporting layerusable
Ready for AI and analyticstrusted

This is the foundation that makes reporting, RAG, agents, and workflow automation safer to deploy. When the data layer is governed, the systems above it become easier to trust.

Production proof

From report backlog to self-service insight.

A strong Data Intelligence page needs to show what changes operationally, not just list capabilities.

Global healthcare retailer

Turning recurring report requests into an on-demand reporting platform.

Sphere built a self-service reporting system with dynamic dashboards so business users could answer common questions without waiting on a manual reporting queue.

Before

Standard questions required a new ticket and depended on an already stretched data team.

After

Common reports became accessible through a centralized platform with reusable dashboards and cleaner data access.

10quick reports available at launch
0tickets needed for standard reporting requests
What Sphere builds

The practical layers behind better data.

Each layer supports a clearer operating model: cleaner pipelines, stronger governance, better reporting, and a safer path to enterprise AI.

Data engineering

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

Start here

Governance and lineage

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

Questions

Modernization

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

See the story

AI and analytics readiness

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

Talk to Sphere
Choose your starting point

Start with the level of help your team needs now.

Not every organization needs a rebuild. Some need clarity first. Others need a focused team to build the governed layer and put it into production.

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 weeksFixed scope
Request assessment
Most direct

Embedded data engineering pod

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

6-12 weeksProduction build
Talk to a data engineer
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 dataGoverned sources
Plan AI readiness
How the engagement runs

Audit first. Build second. Replatform only when it is truly needed.

Sphere starts with the business use cases, then maps the data, systems, owners, and controls required to support them.

01

Assess

Review your data sources, reporting pain points, system dependencies, and priority use cases.

02

Prioritize

Rank the highest-impact gaps in freshness, fragmentation, access, lineage, and ownership.

03

Build

Implement the pipelines, integrations, dashboards, controls, and monitoring needed for production use.

04

Scale

Extend the governed layer to additional teams, data sources, dashboards, and AI-enabled workflows.

Questions before you start

Practical answers before the first workshop.

Usually, no. Sphere typically works with the systems already in place and adds the missing governance, pipeline, access-control, and reporting layers. Full replatforming is only recommended when the current environment cannot support the business goal.

RAG systems and agents depend on trusted source material. Data Intelligence work helps prepare approved sources, permissions, freshness rules, lineage, and retrieval-ready structure before AI is connected to critical business information.

The assessment maps source systems, data owners, access requirements, reporting gaps, freshness risks, and priority use cases. The output is a practical roadmap ranked by impact and implementation effort.

A readiness assessment is typically 2-3 weeks. A first production pipeline often falls in the 6-12 week range, depending on the number of systems, integrations, and governance requirements involved.

Find out what your data layer can support next.

Start with a readiness review, a focused engineering pod, or an AI-readiness plan for RAG, agents, and operational reporting.

Request a Data Readiness ReviewTalk to a Data Engineer

Let's map your data intelligence layer

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