Try it now

See exactly what we'd build for you.

Pick your industry and every data source you use. We'll generate the specific pipeline, dashboards, and reports Madison would deliver — metrics included.

01 · Your industry

02 · Your data sources

Pick an industry first.

Choose your industry, select one or more data sources, then generate your preview.

Pipeline

Live · the path your data takes.

Service offering

A comprehensive suite,
three ways in.

Read each pillar top-down for the business outcome, or follow the tags for the exact stack and the roles that deliver it. Every pillar maps to a layer of the refinery above.

01 · Foundations

Data Engineering & Infrastructure

Reliable, trusted data — ready the moment a decision needs it.

Delivered by Data Engineer · Platform Engineer · Analytics Engineer

  • Modern Data StackOne cloud platform your whole company runs on.Snowflake · Databricks · BigQuery
  • Automated PipelinesData refreshes itself — no manual exports, no 2am breakages.dbt · Apache Airflow · ELT
  • Data LakehouseLake economics with warehouse speed and governance.Delta Lake · Iceberg · S3
  • Governance & Quality as a ServiceBad data is caught before it reaches a dashboard.observability · lineage · automated tests
02 · Intelligence

Advanced Analytics & ML

Predict and shape what happens next, not just report what already did.

Delivered by Data Scientist · ML Engineer · Data Analyst

  • Predictive AnalyticsKnow who'll churn and what they're worth, before they leave.churn · LTV · cohort · retention models
  • Customer 360 & PersonalizationOne view of each customer across every source.CRM + web + social unified · marketing automation
  • Generative AI IntegrationGenAI that does the work, not just chats about it.doc summaries · review sentiment · knowledge bases
  • MLOpsModels stay as accurate in month nine as on day one.monitoring · drift detection · retraining · MLflow
03 · Productized

Specialized “Productized” Offerings

Fixed scope, fast proof — value you can see before you commit.

Delivered by Data Engineer · Data Analyst · Data Scientist

  • 15-Day Proof of ConceptOne real problem solved and ROI proven in two weeks.scoped sprint · churn / fraud / segmentation
  • Industry DashboardsReady-to-run BI tuned to your sector.e-commerce · fintech · logistics
  • Data Lakehouse ArchitectureThe full blueprint, delivered as a package.reference architecture · migration plan
  • Data-as-a-Service (DaaS)Curated datasets, ready to query on day one.market research · enrichment feeds

How we engage

Principles first, and the
stack that backs them.

  • 01

    Start small, prove ROI

    A 15-day proof of concept on one real problem beats a six-month roadmap built on assumptions. We earn the next phase.

  • 02

    Automate the pipes

    Manual ETL breaks quietly. We move you to automated ELT so reliability is the default, not the heroics.

  • 03

    Catch it before the dashboard

    Observability sits in the pipeline, not the post-mortem. Bad data should never reach a decision-maker.

  • 04

    Ship models that act

    From "what happened" to "how to win" — and we stay on for MLOps so the model is as good in month nine as on day one.

The stack, by layer
IngestFivetran · Apache Kafka · custom extractors
LakehouseSnowflake · Databricks · BigQuery · S3 · Oracle
Transformdbt · Apache Airflow · Spark
SciencePython · scikit-learn · XGBoost · MLflow
ServePower BI · Tableau · reverse-ETL · model APIs
Governcatalog · lineage · quality & access reviews

Case studies

Shipped, measured,
still running.

Case 01Well-being / Fitness

Data warehouse architecture

Design and run a scalable data warehouse for a fitness platform with 300,000+ monthly active users, feeding marketing and finance with trustworthy reports.

OutputMarketing & finance reports
Input
MongoDB, HubSpot, Stripe, Google Analytics, Intercom, Salesforce
Stack
AWS EC2, Fivetran, Airflow, Databricks, MongoDB
Team
5 people
Duration
1 year
60%↓time-to-insight across recurring finance, marketing & product reports
5–10%↓weekly and monthly churn via lifecycle automation
+10%YoY retention from a 14-day churn early-warning system
100%stability at 300K+ MAU
BronzeSilverGoldBITIME-TO-INSIGHTbeforeafter · −60%300,000+ MAU · 100% uptime
medallion → BImasked · client metrics
Case 02Logistics

Data lake platform management

A global technology-and-services supplier whose departments each needed data for reports, dashboards, and AI/DS work — from one governed, regional-to-global platform.

OutputProcessed, governed data
Input
ERP, MS Office, IoT data, other platforms
Stack
Oracle DB, Apache Kafka, Solace, AWS, Power BI, Tableau
Team
4 people
Duration
1 year
1unified lakehouse managing the end-to-end lifecycle of global data
Highavailability and security across regional sources
Autoingestion from enterprise, user-generated, IoT & content sources
ERPMS OfficeIoTOtherDATA LAKEReportingDashboardsExplorationAI / DSCentralized · global · governedETL · SAP BODS/SLT · Event hub · Apache Kafka
sources → lake → appsdata management & governance
Case 03Fintech

Credit fraud detection

Credit-default prediction is core to consumer-lending risk. Accurate, real-time scoring means better lending decisions, smoother customer experience, and sustainable profit.

OutputReal-time fraud classification
Input
Customer data from multiple sources
Stack
DB, Fivetran, Databricks, Airflow, scikit-learn, XGBoost
Team
4 people
Duration
4 months
38%↓fraud on average vs businesses without the system
<100msend-to-end scoring latency
20%↓false positives
100%stability at 300K+ MAU
RISK 0–100low · approvemedium · MFAhigh · blockXGBoost · Isolation Forest · feedback loop
real-time risk scoring−38% fraud
Case 04Fitness · PoC

User segmentation by demographic

A four-week proof of concept: turn raw fitness-app data into clean demographic segments — coaches and clients by country, lead source, and spend — so growth and marketing can target with evidence.

OutputDemographic user segments
Input
Fitness-app customer data
Stack
MongoDB, GitHub, Fivetran, Databricks, Airflow
Team
3 people
Duration
4 weeks
4,780coaches and 21.84K clients segmented
119Min coach spend mapped by source & country
4 wkfrom raw data to a working segmentation dashboard
REGISTRATIONS BY COUNTRY2,6241,628336VietnamUSAUnknwnU.K.SGOther4,780 coaches · 21.84K clients
segments by demographic* confidential data masked

Work with us

Have an idea we can
turn into reality?

Bring us the decision you're trying to make. We'll scope a 15-day proof of concept on one real problem — and prove the ROI before anyone signs up for a roadmap.

Start a 15-day PoC →Visit madison-technologies.com
Contact us[email protected]Tell us the decision you're trying to make — we reply within a business day.
CallMalaysia+60 12 619 8412Singapore+65 8201 4068Vietnam+84 934 988 681
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