We build the pipelines, warehouses, and dashboards that turn scattered operational data into live, decision-ready intelligence — without oversized tooling or enterprise price tags.
We know the full landscape. We'll recommend the stack that fits your scale and use case — not oversell enterprise tooling you don't need.
We prove value on one high-impact use case first, then scale with full stakeholder buy-in.
We've unified data from APIs, FTPs, email reports, and scrapers across SG, MY, and CN simultaneously.
We train motivated employees to own, extend, and self-serve the analytics tools we build — easy-to-learn tools only.
We follow a medallion architecture — extract raw data, clean it in progressive layers, and surface it where your team can act on it daily.
We use Pipedream to pull data on a defined schedule from wherever your business data lives — APIs, databases, files, emails, and the web.
All extracted data lands in Google Cloud Storage in its original format — a reliable, auditable copy of the truth before any transformation.
Raw data is cleaned into a silver layer — validated and typed. Business logic then shapes it into a gold layer of KPI-ready tables.
The gold layer feeds live dashboards refreshed daily, or triggers downstream automations — email alerts, chatbots, export jobs.
Powerful enough for enterprise needs, accessible enough for your team to learn, and cost-effective at SME scale.
Cloud-based integration platform with 2,000+ connectors. Runs 24/7 with no local machine required. Supports multiple trigger types and Python for complex custom logic.
Serverless data warehouse that scales to petabytes. Pay-per-query pricing keeps costs predictable for SMEs while delivering enterprise-grade performance.
Durable, low-cost object storage for raw data landing. Supports any file format and integrates natively with BigQuery for direct querying.
Microsoft's industry-leading BI tool. Ideal when your team already lives in Microsoft 365 — familiar, broadly supported, and powerful for self-service analytics.
Google's cloud-native reporting tool. Best for shareable, web-based dashboards accessible by clients or external stakeholders — no licence required.
Used for transformations that go beyond no-code tools — custom parsing, business rule enforcement, and more complex pipeline logic.
Many teams default to RPA tools for data automation. Here's why a cloud-based approach delivers more for ongoing data engineering workloads.
| Feature | ✅ Cloud-based (Pipedream) | ❌ Local software / RPA |
|---|---|---|
| Deployment | Runs 24/7 in the cloud — no machine dependency | Requires local installation, a dedicated machine, and ongoing upkeep |
| Triggers | New email, file upload, scheduled time, or webhook — all supported | Mostly manual — someone must press a button to run a process |
| Integration | 2,000+ API and SaaS connectors out of the box | Limited to software the RPA vendor explicitly supports |
| Custom logic | Flexible Python scripts for complex transformations | Rigid VB or C# scripting — harder to read and maintain |
| Cost model | Predictable SaaS subscription; no hardware dependency | Licence per bot or machine; cost escalates as automation scope grows |
From multi-outlet restaurant chains to food conglomerates — reliable, automated data infrastructure across industries.
A restaurant group operating across Singapore, Malaysia, and China needed one source of truth from disparate POS systems. We built ingestion pipelines covering APIs, FTP transfers, daily email reports, and web scraping — all centralised into BigQuery.
Management needed a live view of sales performance across outlets — but data was siloed in POS systems. We automated extraction, piped it through BigQuery, and delivered a Power BI dashboard refreshed daily. Decision latency dropped from weekly reports to same-day visibility.
An institutional caterer with multiple client sites needed to consolidate POS data and share performance dashboards with clients separately from internal ops. We aggregated outlet sales into BigQuery and built distinct dashboards for each audience — all refreshed daily.
JR Group spans ready-to-eat meals, institutional catering, and hot food vending machines. Manually reconciling sales data from 50+ POS systems was time-consuming and error-prone. We automated extraction from web-based systems and CSV files into a clean, summarised BigQuery output.
Pilot projects are hard to get off the ground. We use a structured process that builds confidence at every stage before committing to full rollout.
Let's Talk →We brainstorm data use cases with your organisation, then study your existing data sources — databases, spreadsheets, third-party systems — to assess feasibility and prioritise what's most valuable to deliver first.
We identify the use case that is both high-impact and low-effort — the sweet spot for a pilot. Delivering a real result quickly convinces stakeholders and end-users that the data product is worth investing in.
Only after the pilot is live and validated do we proceed with the full-scale solution — additional data sources, more complex transformations, and expanded dashboards across your organisation.
We don't disappear after go-live. We train motivated employees to be power users — able to build their own reports, modify data models, and extend the solution independently.
Whether you're starting from scratch or untangling a mess of spreadsheets, we'll identify the right use case and build from there.
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