Enterprise AI, cloud modernization, cybersecurity and platform engineering.
Data and analytics

Decisions are only as good as the data underneath them.

Dashboards no one trusts, numbers that don't reconcile, and "self-service analytics" that turned into a swamp of conflicting reports. Then someone wants to build AI on top of it, which makes the mess worse.

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Business outcomes

From conflicting reports to numbers people trust.

We build governed data foundations (clean pipelines, clear ownership, controlled access) using Microsoft Fabric, Power BI, BigQuery and the right tool for your stack. The same foundation that gives trustworthy reporting is what makes AI on your data safe.

Trustworthy foundation

Clean pipelines, clear ownership and governed access, so the numbers reconcile and leadership actually trusts them.

One source of truth

A shared data model ends the "five reports, five different totals" problem.

Governed access

Sensitive data is classified and access-controlled, which is also what makes AI on your data safe.

Ready for AI

The same foundation that powers reliable reporting is what a RAG or analytics AI can safely stand on.

Related services

Capabilities connected to this solution.

These services can be delivered independently or combined into a larger roadmap.

Data & Analytics

Pipelines, warehouses and governed access that turn scattered data into numbers your leadership actually trusts.

Microsoft Fabric

Unify data engineering, warehousing and BI on Microsoft Fabric: one governed platform instead of a stitched-together stack.

Power BI

Power BI done properly: a trustworthy data model underneath, governed sharing, and reports people actually use.

Microsoft Purview

Know where your sensitive data is, classify it, and protect it: data governance and compliance with Microsoft Purview.

Azure OpenAI

Deploy enterprise Azure OpenAI with Private Link, Entra ID authentication, content filtering, and hybrid RAG architecture aligned with US regulations.
Industry fit

Built for industries where decisions ride on the data.

Data volume and sensitivity differ by sector. We map governed pipelines and access models to what each industry actually reports on.

01

Retail & E-commerce

Sales, inventory and customer data unified into one governed model so promotions and demand decisions are based on numbers that reconcile.

 02

Manufacturing

Production, quality and supply data brought into a governed foundation that plant and enterprise teams can both trust.

 03

Logistics

Fleet, warehouse and delivery data governed and unified so operational reporting stops relying on manually reconciled spreadsheets.
Delivery path

From scattered reports to a governed foundation everyone trusts.

Each phase leaves a working, documented piece of the data foundation, not just a modeling exercise.

  1. Map current data sources, ownership gaps and where numbers disagree, and prioritize the reports that matter most.

    • Data source & ownership audit
    • Priority report list
    Typical duration: 2-3 weeks

  2. Design the governed pipeline, data model and access controls using Microsoft Fabric, Power BI or the right tool for your stack.

    • Pipeline & data model design
    • Access control model
    Typical duration: 3-4 weeks

  3. Build the pipelines, classify sensitive data and stand up the reports that replace the conflicting versions.

    • Working governed pipelines
    • Classified & access-controlled data
    Typical duration: 6-10 weeks

  4. Reconcile the new reports against known-good numbers and confirm access controls hold before wider rollout.

    • Reconciliation results
    • Access control test results
    Typical duration: 1-2 weeks

  5. Hand over documentation and ownership so the foundation stays trustworthy as new data sources are added.

    • Data foundation documentation
    • Ownership & governance model
    Typical duration: Ongoing
Delivery model
5 Phases
Milestone-Gated Delivery Model

Every pipeline ships with clear ownership and access rules, so the foundation stays trustworthy as new sources are added, not just on day one.

01
Discover Scope, Baseline Map & Constraints
02
Architect Target Architecture & Threat Model
03
Implement IaC Automation & Landing Zones
04
Validate Security Audit & Compliance Gate
05
Operate Runbooks & Practical Handover
Documented Deliverable & Formal Go/No-Go Decision Gate No code or infrastructure moves to production without passing automated compliance checks and client review.
Zero Vendor Lock-In Delivered into client Git & cloud accounts
Infrastructure-as-Code Terraform / Bicep repeatable blueprints
Operational Runbooks Knowledge transfer & team enablement
Ways to start

Start with the reports that matter, then govern the rest.

Discuss your data foundation
Technology

Platforms we build on.

Microsoft Fabric Power BI Azure OpenAI Microsoft Purview BigQuery Azure Data Factory
Related proof

Representative delivery pattern.

Case studies are shared where details can be published without exposing confidential client information.

  • Challenge: AI agents needed read access to production data, but there was no governed way to expose it without risking sensitive fields being over-shared.
  • Approach: Data access was routed through a mediated gateway with scoped, read-only permissions, with sensitive fields masked before reaching the calling agent.
  • Outcome: Agents could query production data safely, with every access logged and traceable back to the identity that requested it.
Related case study

AI agents needed real tools. The security team needed to sleep at night.

AI agents got only the tools they need, through one gateway, with every call logged.

Least privilegeAccess model Entra IDIdentity APIMGateway
Read the story
FAQ

Questions we hear from data and analytics leads.

Short answers to common planning questions for Data & Analytics.

We already have Power BI. Why do we need a foundation project?

Power BI is the reporting layer; the problem is usually underneath it, in pipelines and ownership. We fix the foundation so the reports built on top of it are actually trustworthy.

How long until our dashboards stop disagreeing with each other?

For a well-defined set of priority reports, typically 6-10 weeks from governed pipeline to reconciled, trusted numbers.

Is our data ready for AI, or do we need to fix it first?

Most environments need governed access and classification in place first. That is also what this engagement builds, so AI can follow safely rather than making the mess worse.

Do we have to move everything to Microsoft Fabric?

No. We use Fabric, Power BI, BigQuery or your existing stack depending on what fits, and design the governance model to work with what you already run.

Who owns the data model after handover?

Your team, with documentation and a clear ownership model we hand over, or us under managed data operations if you want ongoing support.

Free first call

Tired of five reports with five different totals?

Tell us which numbers you don't trust. We'll show you what a governed foundation looks like before you build anything else on top of it.

Book a Consultation