Solutions
A single source of truth for the whole organisation. BigAI consolidates every source system into a governed lakehouse so that every report and every AI model downstream runs on the same trusted numbers.
Signals
Finance, Sales and Marketing each compute revenue their own way from their own source, and the numbers never match.
Joining data across systems is still done by hand in spreadsheets — error-prone and impossible to reproduce.
Analysts query the ERP or core system directly, slowing transactions during peak hours.
No catalogue, no lineage — a serious problem the moment an auditor asks where a number came from.
Capabilities
Almost every failed AI project BigAI has been asked to review shares one root cause: the model was built on data nobody could trust. Not a modelling error — an input problem. Missing records, conflicting definitions, or data that arrives too late to act on.
A data platform fixes the foundation. Once there is a single source of truth, everything downstream — reporting, forecasting, AI assistants — becomes cheaper and faster to build.
Projects run in 6–8 week increments. Each increment brings another group of sources onto the platform and hands over a reporting pack that is usable immediately. It is slower at the start, but almost no project has to be rebuilt later.
The hardest part is never the technology. It is getting departments to agree on what each metric means — the most time-consuming step and the one that creates the most durable value.
Connect ERP, CRM, POS, HRM, IoT, files and third-party APIs. Batch loads and real-time CDC both supported.
Bronze / Silver / Gold tiers on open table formats — Iceberg or Delta Lake — separating raw from curated data.
Automated rules for completeness, validity and duplication, with alerts the moment a pipeline produces anomalies.
A company-wide data dictionary; trace any dashboard figure back to the source table it came from.
Row and column level access control, masking of sensitive fields, full access logs — aligned with Vietnam Decree 13/2023 on personal data protection.
Pipeline monitoring, autoscaling, partition and storage format tuning so infrastructure cost does not grow exponentially.
Architecture
The standard BigAI blueprint, adjusted to the scale and infrastructure constraints of each organisation.
1 · Sources
2 · Ingestion
3 · Layered lakehouse
4 · Governance
5 · Consumption
Outcomes
Ranges aggregated across delivered BigAI projects. Specific targets are agreed during the assessment phase.
Swipe to see the full table
| Metric | Before | After | Improvement |
|---|---|---|---|
| Time to a consolidated report | 5–12 working days | Automated, ready every morning | −70% |
| Manual data assembly hours | ~160 hours/month | ~35 hours/month | −78% |
| Metric variance across departments | 3–8% | under 0.5% | −90% |
| Time to onboard a new source | 4–6 weeks | 3–5 days | −80% |
Case studies
From 14 disconnected sources and manual reconciliation to a single governed lakehouse serving both regulatory reporting and internal analytics.
Read case studyReplacing store-manager intuition with per-SKU forecasting that accounts for seasonality, weather and the promotion calendar.
Read case studyFAQ
A BigAI solution engineer will review your current data estate with you, identify the highest-value problem to solve and sketch a realistic roadmap. No commitment.