Portfolio

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We are a young company with senior experience. The items below are working demos, prototypes, and anonymized examples from our lead consultant’s enterprise delivery experience; they show how we work, not a claimed client list.

They are ordered from the fastest start to the deepest investment. Most companies begin with the Business Cockpit: quick to deliver, immediate value from your core business. When you want to do more with data centralized from all your systems, you grow into a data platform — we offer two flavors, Microsoft Fabric and Databricks. The Data Solution Blueprint and data governance are for organizations that are already mature in data.

1. SME Business Cockpit (demo)

  • Business problem: an owner runs the company from ten Excel files and sees the real picture weeks too late.
  • Data challenge: accounting exports, sales lists, and inventory counts in different formats, nothing connected.
  • Approach: connect the existing files and systems, model the data properly, and put finance, sales, and operations on one clear set of reports — your whole business at a glance. As the next level, add the AI question layer and ask your data in plain Vietnamese or English.
  • Tools: Microsoft Fabric, Power BI, an AI question layer over the semantic model.
  • Result: one trusted picture of the business, updated automatically, readable on a phone.
  • Best for: small and family businesses taking their first data step.
  • Time to market: first version in weeks.
  • Complexity: low. Business impact: immediate — you see your core business today.

Screenshot placeholder: demo cockpit.

2. Data Platform on Microsoft Fabric (anonymized example)

  • Business problem: the Cockpit answers today’s questions. When you want to do more with data from all your systems — more departments, more history, more automation — you need a real platform under it.
  • Data challenge: source data scattered across systems, manual spreadsheet work every month, no central platform, no governance.
  • Approach: a lakehouse foundation on Microsoft Fabric with staged data layers, semantic models, and governed workspaces, delivered with documentation and handover.
  • Tools: Microsoft Fabric, Azure DevOps, Power BI as the reporting layer.
  • Result: one governed platform that feeds every report — and every future AI feature — from the same trusted data.
  • Best for: growing companies ready to invest for the long term, especially those already in the Microsoft world.
  • Time to market: first results in one to three months; the platform grows in phases.
  • Complexity: medium to high. Business impact: long-term — the foundation everything else builds on.

Visual placeholder: architecture diagram.

3. Data Platform on Databricks (anonymized example)

  • Business problem: the same long-term goal as above, for companies with larger data volumes or a stronger engineering ambition.
  • Data challenge: many sources, big or fast-moving data, and the wish to keep options open (open formats, machine learning later).
  • Approach: pipelines and data products on Databricks: ingestion, transformation, quality checks, and orchestration, built to run reliably without daily babysitting.
  • Tools: Databricks, Azure DevOps, Power BI as the reporting layer.
  • Result: a scalable engineering backbone for analytics and, when you are ready, AI.
  • Best for: companies with serious data volumes or an in-house engineering ambition.
  • Time to market: first results in one to three months; grows in phases.
  • Complexity: medium to high. Business impact: long-term — scale and flexibility.

Visual placeholder: pipeline architecture diagram.

4. Data Solution Blueprint (prototype)

  • Business problem: mature data teams build many data products, and every new one repeats the same manual setup: pipelines, quality rules, models, permissions.
  • Data challenge: keeping dozens of data products consistent with company standards without slowing everyone down.
  • Approach: a metadata-driven framework that reads definitions and generates the data artifacts automatically, to the same standard every time. Deliberately more than software: we deliver the working procedures and train your team, because a framework nobody adopts is worth nothing.
  • Tools: Databricks, Microsoft Fabric, Azure DevOps, Terraform, Bicep.
  • Result: new data products in days instead of weeks, with governance built in — and a team that knows how to use it.
  • Best for: mature data organizations on Fabric or Databricks.
  • Time to market: the framework stands quickly; the real work is adoption — procedures and training included.
  • Complexity: high. Business impact: speed and standardization for every future data product.

Visual placeholder: metadata-to-artifacts flow.

5. Data Governance (service offering)

  • Business problem: the bigger the organization, the more the question shifts from “can we build a report?” to “can we trust the numbers, and who owns them?”
  • Data challenge: unclear ownership, competing definitions, uneven quality, and access rights nobody can explain to an auditor.
  • Approach: a phased governance program: data ownership, shared business definitions, quality rules, access control, and cataloging — built into daily work, not a binder on a shelf.
  • Tools: Microsoft Purview or Unity Catalog, on the platform you already run.
  • Result: numbers people trust, faster audits, and a foundation that regulators and enterprise customers increasingly demand.
  • Best for: larger, data-mature organizations.
  • Time to market: a program over quarters, not a quick win — which is exactly why it is last on this list.
  • Complexity: the highest in this portfolio. Business impact: trust, compliance, durability.

Visual placeholder: governance operating model.