AI-first GCC
Data platform design, pipeline architecture, quality frameworks, feature stores, and governance - the foundation that makes enterprise AI possible at scale.
80%
of AI effort is data work
99.5%
target pipeline reliability
3 zones
bronze / silver / gold
Hours
not weeks to onboard data
Industry surveys repeatedly show that 70-80% of the effort on enterprise AI projects goes into data work - sourcing, cleaning, joining, transforming, and governing. When that work is reactive and per-project, every new use case starts from zero. Velocity stalls and unit cost rises.
A modern data platform changes this. Reusable pipelines, governed data products, quality contracts, feature stores, and lineage turn data from a per-project chore into a shared asset. Each new AI use case starts from a higher base, ships faster, and costs less.
NeoIntelli designs and builds the data platform layer - on AWS, Azure, GCP, or hybrid - tuned to AI workloads, integrated with governance, and engineered for the scale GCCs actually operate at.
Deliverables
01
Cloud-native, lakehouse-pattern architecture with ingestion, storage, compute, serving, and governance designed for both analytics and AI workloads.
02
Reliable, tested batch and streaming pipelines with CI/CD, lineage, contract testing, and automated quality checks across the medallion zones.
03
Automated profiling, validation rules, anomaly detection, freshness SLOs, and a data quality scorecard integrated into the developer workflow.
04
Curated, governed feature stores and vector stores that make AI use cases reusable, consistent, and faster to deliver.
05
Catalog, lineage, access controls, masking, classification, and compliance-ready evidence for DPDPA, GDPR, and sectoral regulation.
06
Self-serve analytics for business teams on top of governed data, with semantic layer, BI integration, and trust signals built in.
01
Inventory data sources, current platform, pipeline reliability, quality posture, governance maturity, and AI use-case demand.
02
Design the target platform - lakehouse pattern, medallion zones, ingestion, governance, feature store, and serving - sized for current and 3-year scale.
03
Implement platform foundations, migrate priority pipelines, stand up governance and quality frameworks, and onboard the first AI use case.
04
Scale to enterprise coverage, embed FinOps, automate quality and lineage, and operate the platform as a shared product with named owners.
Every use case rebuilding its own pipelines is the single largest waste in enterprise AI.
Datasets without named owners drift in quality and lose trust quickly.
Quality bolted on after pipelines exist is 10x more expensive than quality designed in.
Choosing platforms before defining data products creates expensive stacks that nobody uses.
Cloud data spend grows quietly until it becomes a board issue. Tagging and budgets need to be there from day one.
DPDPA, GDPR, and sector rules now constrain how data can be used. Late governance forces re-architecture.
Pipeline reliability above 99.5% on critical flows
Data product onboarding measured in hours, not weeks
Quality scorecard live on every critical dataset
Feature reuse across AI use cases above 50%
Cloud data spend under managed FinOps with monthly review
Compliance evidence automated for DPDPA and applicable regulation
We work across AWS, Azure, GCP, and hybrid setups - and recommend based on your existing investments, regulatory constraints, and AI workload mix.
Through automated profiling, validation contracts, anomaly detection, freshness SLOs, and integration into CI/CD - so quality is measured continuously, not at audit time.
Both. We build greenfield platforms and modernise existing ones - migrating legacy ETL, on-prem warehouses, and fragmented pipelines into cloud-native lakehouse architectures.
Data engineering provides the reliable, governed, AI-ready foundation that ML and GenAI models depend on - training data, feature stores, vector stores, evaluation datasets, and grounding sources for RAG.
We are tool-agnostic but frequently use Databricks, Snowflake, BigQuery, Spark, dbt, Airflow, Delta Lake, Apache Iceberg, Kafka, and cloud-native services - chosen to fit the stack and team.
Through catalog, lineage, classification, masking, access controls, and automated evidence collection aligned to DPDPA, GDPR, HIPAA, and your enterprise policies.
A feature store is a managed, governed library of model-ready features that can be reused across AI use cases. Most centers with more than three production models benefit from one.
Yes. Vector store selection, embedding strategy, chunking, indexing, and hybrid retrieval are all part of the modern data platform for RAG-based GenAI.
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