Data Infrastructure

Data Infrastructure That Makes AIand Decisions Possible.

AI systems are only as reliable as the data behind them. We build the infrastructure layer that most companies skip — ETL pipelines that handle dirty data, vector databases that make enterprise knowledge searchable, and RAG systems that ground AI outputs in your real information.

ETL Pipelines
RAG Systems
Vector Databases

ETL Pipeline for Fragmented ERP Data

A multi-site industrial group runs three different ERPs. We build a unified ETL pipeline that extracts, normalizes, deduplicates, and loads data into a central analytical database — giving leadership a single version of truth.

Single source of truth

RAG System for Technical Documentation

An engineering company maintains 40,000 pages of technical manuals. We build a Retrieval-Augmented Generation system with vector indexing and AI-powered search where field engineers get source-cited answers in seconds.

HoursSeconds

Real-Time Data Platform for E-Commerce

A D2C brand sells across its own shop, Amazon, and wholesale partners. We build a data platform with real-time ingestion from all channels and automated reporting that reconciles revenue and inventory daily.

Daily reconciliation

Knowledge Base Infrastructure for AI Agents

A professional services firm has knowledge trapped in 15 years of reports and emails. We build ingestion pipelines, embedding models, and vector storage that turns institutional memory into a queryable knowledge base.

15 years indexed
1

Data Audit

Map your data landscape, sources, and quality gaps

2

Pipeline Architecture

Design ETL flows, schemas, and transformation logic

3

Production

Build and deploy pipelines with automated testing

4

Monitoring

Data quality alerts, pipeline health, and continuous optimization

0%

Automated data management

0k

Pages indexable

0 weeks

To working prototype

Technologies
Python / dbt
Apache Airflow
PostgreSQL / pgvector
Pinecone / Weaviate
LangChain
Supabase
OpenAI Embeddings
Docker / AWS / Hetzner

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