Capability
Data Platform
AI is only as good as the data graph behind it. We build pipelines, lakehouses, and analytics that make GenAI features trustworthy and fast.
Summary
Pipelines, warehouses/lakes, analytics, and GenAI-ready data foundations that keep AI honest.
Problem
What teams are stuck with
Scattered CSVs and siloed SaaS exports make every dashboard a one-off project.
GenAI teams invent ad-hoc extracts that drift from finance and ops truth.
Offerings
What we deliver
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01
ETL/ELT pipelines
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02
Warehouse and lakehouse design
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03
Analytics and BI
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04
GenAI data foundations
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05
Streaming and real-time serving
Approach
How we work it
Establish sources of truth, contracts, and freshness SLAs before modeling features.
Design for both BI and retrieval — same entities, different serving layers.
Govern PII and access so AI products inherit the same rules.
Outcomes
What good looks like
- Trusted metrics shared across teams
- Fresh features for AI and products
- Lower cost of every new dashboard or agent
Sub-services
Inside Data Platform
- Data Pipelines & ETL/ELT Reliable ingestion and transforms from SaaS, databases, files, and events into governed stores.
- Warehouses & Lakehouses Modern warehouse/lakehouse architectures for analytics, ML features, and AI corpora.
- Analytics & BI Metrics layers, dashboards, and decision-ready analytics for operators and executives.
- GenAI-Ready Data Foundations Corpus prep, feature stores, embeddings pipelines, and eval datasets for production AI.
- Real-time Streaming & Serving Event streams and low-latency serving for product features, alerts, and AI context.
Related
Keep exploring
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