AI Consulting Service

Data Infrastructure & Pipeline

The data foundation that makes reliable AI possible.

Overview

AI is only as good as the data feeding it. Most organizations have data scattered across CRMs, spreadsheets, legacy databases, and SaaS tools — none of it ready for AI.

We design and build the pipelines, warehouses, and governance frameworks that turn fragmented data into a clean, queryable, AI-ready foundation.

Our infrastructure work spans extraction, transformation, storage, vector embeddings, retrieval, and security — all designed with both current and future AI use cases in mind.

How We Work

Our Approach

01

Data Inventory

Map every source of data in your organization — apps, databases, files, third-party APIs. Identify what's clean, what's duplicate, and what's missing.

02

Architecture Design

Design the pipelines, warehouse schema, and access layer. Choose between modern warehouse, lakehouse, or hybrid based on your scale.

03

ETL Implementation

Build the extraction and transformation jobs. Schedule, monitor, alert on failures. Ensure data lands clean and on time.

04

Vector & Search Layer

Add embedding indexes and retrieval infrastructure so AI agents can search your data semantically, not just by keyword.

05

Governance

Implement access controls, audit logs, lineage tracking, and quality monitoring. Pass compliance reviews without scrambling.

06

Documentation

Leave your team with clear runbooks, schema docs, and on-call playbooks so they can operate and extend without us.

Output

What You Get

  • End-to-end data architecture diagram
  • Production ETL pipelines with monitoring and alerting
  • Centralized data warehouse with documented schema
  • Vector database for AI retrieval and search
  • Access controls, audit logging, and lineage tracking
  • Team runbooks and on-call documentation
Fit Check

When to Engage Us

Data is everywhere

Your data lives in 12 different systems and nothing connects. Every analysis is a multi-day SQL adventure.

AI-ready foundation

You want to deploy AI but your data is not in a state where any model can reliably use it. The plumbing comes first.

Compliance pressure

Auditors are asking about data lineage, access logs, and retention — and right now you cannot answer cleanly.

Building RAG or AI search

You need a production-grade retrieval system over your documents, tickets, or internal knowledge base.

Next Step

Ready to talk through data infrastructure?

Start with a free AI readiness assessment, or reach out directly to scope an engagement.

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