AI Data Infrastructure

The data plane behind AI products — feature stores, vector databases, and pipelines that keep models fed with fresh, trustworthy data.

AI Product Teams SaaS Companies Growing Startups

Overview

AI products live and die by the quality of the data underneath them. Whether you’re training a classifier, fine-tuning an LLM, or serving a retrieval-augmented generation (RAG) app, the data plane needs to deliver fresh, lineage-tracked, evaluated data on a schedule the model can rely on. We design that plane end-to-end.

Reference Architecture

flowchart LR
Sources[("Sources<br/>(Events, DBs, Docs)")] --> Stream["Streaming<br/>(Kafka)"]
Sources --> Batch["Batch<br/>(Airflow)"]
Stream --> Lake["Lakehouse<br/>(Delta / Iceberg)"]
Batch --> Lake
Lake --> Features["Feature Store"]
Lake --> Embed["Embedding Pipeline"]
Embed --> Vector["Vector DB<br/>(Pinecone / Weaviate / pgvector)"]
Features --> Serving["Model Serving"]
Vector --> Serving
Serving --> Eval["Evaluation<br/>& Drift Monitoring"]
Eval -.-> Lake

Engagement Model

We typically start with a 4-week architecture sprint that produces a reference design, a build sequence, and a working proof of concept on one critical pipeline. From there we scale the pattern across the rest of the data plane in monthly increments.

What's Included

Reference architecture for the AI data plane (batch + streaming)
Feature engineering pipelines and lineage
Vector database selection and embedding ingestion pipelines
Evaluation datasets, golden sets, and drift monitoring
Cost and latency budgets for production inference

Technologies

Snowflake
Databricks
PostgreSQL
Apache Kafka
Apache Airflow
Python

Related Services

LLM Data Architecture
Retrieval, embedding, and evaluation pipelines for production LLM applications — from RAG to fine-tuning.
Data Pipeline Modernization
Replace fragile cron jobs and legacy ETL with declarative, observable pipelines on Airflow and dbt.
Analytics Engineering
Turn raw warehouse data into trusted, well-modeled datasets that analysts, executives, and downstream apps can rely on.

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