AI GCC in India · Data Engineering
Data platforms, batch and streaming pipelines, governed data products, RAG and vector infrastructure, evaluation datasets, lineage and data FinOps, built so every model, copilot and agent in the GCC draws on the same foundation.
Data engineering for an AI GCC builds the foundation that models, retrieval systems, agents and evaluation pipelines reuse. NeoIntelli designs the data platform, runs batch and streaming pipelines, delivers governed AI-ready data products, builds the unstructured data and vector foundation for RAG, curates evaluation datasets, establishes lineage and access control, and manages data cost. The aim is a foundation each new AI use case draws from rather than a pipeline each one rebuilds.
Architecture
The data product layer is the point. It is what lets the third AI use case cost less than the first.
STEP 01
Sources
Operational systems, documents, events, third-party feeds.
STEP 02
Data platform
Ingestion, batch and streaming, storage, transformation, quality.
STEP 03
Governed data products
Reusable, owned, documented datasets with lineage and access control.
STEP 04
AI workloads
Features, retrieval indexes, training sets, evaluation sets, agents.
Capabilities
01
Lakehouse or warehouse design, ingestion patterns, orchestration, transformation standards and the platform team model the GCC will run it with.
02
Reliable batch pipelines and streaming where latency matters, with schema management, replay and monitoring designed in.
03
Owned, documented, versioned datasets built for reuse across ML, GenAI and analytics rather than rebuilt per use case.
04
Document ingestion, parsing, chunking, metadata, embeddings and refresh pipelines that keep retrieval systems current and permission-aware.
05
Curated, versioned evaluation sets for models, prompts, retrieval and agents, treated as first-class data products with owners.
06
Feature stores where they earn their keep, vector stores sized to the retrieval workload, and the serving paths that connect them to production.
07
Classification, access control that follows the source, lineage from source to model, and the records privacy and audit teams ask for.
08
Storage tiers, compute allocation, pipeline cost per product and the reviews that keep the platform bill tied to the value it produces.
Why it is not a generic data page
Data, models, agents, evaluation, MLOps and governance are one system in an AI GCC. Data engineering is scoped against all of them.
Production consumers of the foundation are described under Generative & Agentic AI and MLOps, LLMOps & AgentOps.
Measures of success
Targets agreed per engagement. Not benchmarks, not guarantees.
Buyer questions
The consumers are models, retrieval systems, evaluation pipelines and agents as well as dashboards. That adds unstructured data, vector infrastructure, evaluation sets and lineage into training and inference, and it changes what 'done' means for a pipeline.
Only when a workload justifies it. Feature stores pay off with many models sharing features under latency constraints. Vector stores are required for retrieval at scale. Both are decisions made per portfolio, not defaults.
With contracts at the boundary of each data product, automated checks that run in the pipeline, ownership for every product and monitoring that alerts the owner, not a shared inbox.
Classification, residency mapping, access control and lineage are designed to support the client's obligations, including under the Digital Personal Data Protection Act, 2023 and applicable rules. NeoIntelli provides technology and governance support; legal interpretation sits with the client's advisers.
The client's cloud and, where sensible, the client's existing tooling. The platform design is driven by workload, skills and governance, not by a preferred vendor list.
Share the sources, the AI use cases queued behind them and where pipelines break today. We will map the platform, the data products and the governance the GCC needs to reuse them.