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Peeyoosh Pandey
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6 min read
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Technology
Smita Chimurkar
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3 min read
Our Success Stories
Challenge
One of Australia’s major banks faced fragmented, non-standardized data governance across divisions, with inconsistent data flows, manual governance processes, no interoperability across data systems, and a data estate that lacked the context and quality needed for AI adoption or regulatory compliance.
Solution
Hoonartek implemented the OneGov Divisional Data Governance Platform on GCP, a centralized PaaS enabling divisions to manage their own Business Glossaries, Reference Data, and Data Quality Rules within an enterprise governance framework, with automated DQ pipelines, metadata harvesting, and event publishing via Google Pub/Sub.
Results
10M+
Data elements harvested across Teradata, BigQuery, Datastage, and DBT
3
Divisions onboarded with role-based access and governed self-management
1,000+
Metadata change events published monthly for downstream consumption
Challenge
A leading B2B and B2C logistics company ran analytics on Amazon Redshift accessible only to SQL specialists, processed Proof of Delivery documents entirely manually, had no unified view across three siloed operational datasets, and lacked centralized governance or audit traceability across queries and document workflows.
Solution
Hoonartek built two AI agents on GCP: an NLP Analytics Bot enabling plain-English querying of AWB profitability and lane-level data in BigQuery, and a POD Verification Agent using Document AI and OCR to automate extraction, validation, confidence scoring, and exception routing, with full IAM governance and audit logging across both workstreams.
Results
300
PODs processed in the pilot with 80% OCR accuracy target
35
Natural language questions answered without SQL expertise
Zero
Manual SQL required for business users to query operations data
Challenge
A leading semiconductor materials supplier was losing approximately 187 engineering hours per week to manual OCAP root cause analysis, with engineers reviewing hundreds of process parameters per case through intuition-based methods, taking 30–60 minutes per case across ~250 weekly events.
Solution
Hoonartek built an AI-powered OCAP intelligence platform on GCP using BigQuery, Vertex AI, and a multi-agent architecture, automating data ingestion, ML-driven root cause diagnosis, and plain-language explanation of results, reducing per-case analysis from 45 minutes to minutes.
Results
80–90%
Of OCAP analysis automated end-to-end
150+
Engineer hours saved per week
Minutes
Per case analysis time, down from 30–60 minutes
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- July, 2025
Award-Winning Data Platform Modernisation: Telecom Giant Migrates to Snowflake on AWS
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- July, 2025