Service

AI Data Infrastructure

The data foundation your AI systems actually need to work.

We design and build the data pipelines, vector databases, and embedding architectures that power reliable AI applications. Bad data is why most AI projects fail — we make sure yours doesn't.

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10x

Query performance improvement

99.9%

Pipeline uptime

Real-time

Data freshness

Overview

What we actually build

AI is only as good as the data behind it. Most AI projects stall not because of the model, but because of messy, siloed, or unstructured data. We build the infrastructure layer — pipelines that ingest, clean, chunk, embed, and index your data — so your AI systems have the reliable, up-to-date foundation they need to perform.

Who this is for

Engineering teams, data leads, and CTOs at companies building AI applications who need reliable, production-grade data infrastructure.

Data Pipeline Development

ETL/ELT pipelines that ingest from any source, clean, transform, and load into your target systems.

Vector Database Architecture

Pinecone, Weaviate, Chroma, or pgvector — we design and implement your semantic search layer.

Embedding Infrastructure

Automated chunking, embedding generation, and indexing pipelines for your documents and data.

Data Quality & Governance

Validation, deduplication, and monitoring to ensure AI systems always work from clean data.

Streaming & Batch Processing

Real-time event streaming or scheduled batch processing depending on your freshness requirements.

Observability & Alerting

Pipeline monitoring with automated alerts for failures, drift, and data quality issues.

Use Cases

What clients use this for

Building RAG pipelines over enterprise document repositories

Semantic search infrastructure for product catalogues

Unified data layer connecting fragmented SaaS tools

Real-time event streaming for AI-powered applications

Data warehouse modernisation for analytics and AI

Automated data sync between operational and analytical systems

Process

How we deliver it

Data Audit

We map your data sources, assess quality, and identify gaps that would undermine AI performance.

Architecture Design

We design the full data architecture — pipelines, storage, indexing, and access patterns.

Build & Test

We build and validate the pipelines with production-representative data loads.

Deploy & Monitor

Production deployment with monitoring, alerting, and documentation for your team.

FAQ

Common questions

Do we need to migrate our existing data?

Not necessarily. We can build pipelines that work alongside your existing systems rather than requiring a full migration.

Which vector databases do you recommend?

It depends on scale and existing stack. We use pgvector for Postgres shops, Pinecone for managed simplicity, and Weaviate for hybrid search needs.

How do you handle PII and sensitive data in pipelines?

We implement anonymisation, access controls, and encryption at the pipeline level and can deploy entirely within your infrastructure.

Can you work with our existing data warehouse?

Yes — Snowflake, BigQuery, Redshift, Databricks. We build around what you have.

Ready to get started?

Tell us about your project — we'll scope it, answer your questions, and show you exactly how we'd approach it.

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