Portfolio
Working examples, ordered from fastest start to deepest investment.
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.
Demo
1. SME Business Cockpit
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 for reporting, an AI question layer, and a write-back application — all on the same semantic model.
Result: one trusted picture of the business, updated automatically, readable on a phone.
Anonymized example
2. Data Platform on Microsoft Fabric
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.
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.
Anonymized example
3. Data Platform on Databricks
Business problem: the same long-term goal as above, for companies with larger data volumes or a stronger engineering ambition.
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.
Prototype
4. Data Solution Blueprint
Business problem: mature data teams build many data products, and every new one repeats the same manual setup: pipelines, quality rules, models, permissions.
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.
Service offering
5. Data Governance
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?”
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.
Want to see which example fits your situation?
Start with the business question, not the tool.
Synnoia 