Our Services
Data Engineering
Data pipelines and infrastructure to power your AI initiatives.
What is this?
Understanding Data Engineering
AI systems are only as good as the data they are built on. If your data is scattered across different systems, stored in unstructured formats, or inconsistently labelled, your AI will produce unreliable results. Data engineering is the work of cleaning, structuring, and organising your data into a reliable foundation. This includes building pipelines that keep the data fresh, setting up vector databases for semantic search, and creating the infrastructure that lets your AI agents actually find and use your information.
Core Capabilities
Our Process
How We Solve It
Data Discovery
We audit all your existing data sources — databases, files, APIs, spreadsheets — and identify quality issues, gaps, and consolidation opportunities.
Architecture Design
We design the data pipeline architecture with the right tools for your scale. You see the full blueprint, including data flows and storage choices, before we build.
Pipeline Development
We build the ETL pipelines, configure the databases, and run the initial data migration. All pipelines include monitoring and error handling from day one.
AI Layer Setup
We configure the vector database and embedding pipelines that connect your cleaned data to your AI agents and applications.
Real Examples
What This Looks Like in Practice
A law firm has 10 years of contracts stored as PDFs across shared drives. We build a pipeline that reads every PDF, extracts the text, chunks it intelligently, generates embeddings, and loads it into a vector database — enabling instant semantic search across 50,000 documents.
A retail company wants to consolidate data from their POS system, e-commerce platform, and warehouse software into a single analytics layer. We build the nightly ETL pipeline and data warehouse.
A SaaS company wants to power a RAG-based support agent with their documentation. We set up the full vector infrastructure with hybrid BM25 and semantic search so the agent finds the right answer even when queries are phrased differently from the documentation.
How It Works Technically
System Architecture
Raw Data Sources
DBs / Files / APIs / PDFs
ETL Pipeline
Airflow + dbt transforms
Data Warehouse
Snowflake / PostgreSQL
Vector Store
Pinecone / Weaviate
AI-Ready Layer
Agents query here
Tech Stack
Real Impact
Case Study
Context
A legal services firm with 10 years of contracts and case files stored as unstructured PDFs.
The Problem
Lawyers were spending 2-3 hours per case manually searching through old contracts and precedents. The data existed but was completely unsearchable. A simple keyword search returned too many irrelevant results.
What We Built
We built a full RAG infrastructure that ingested 50,000+ documents, generated embeddings, and enabled semantic search. Lawyers now find relevant precedents in under 30 seconds.
50,000+
Documents Indexed
30 seconds
Search Time
2.5 hours
Hours Saved per Case
See It Live
Interactive Demo
Watch a real step-by-step simulation of this service working through an actual business scenario from start to finish.
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Get Started
Want this working in your business?
Book a free 30-minute audit. We will look at your workflows and tell you exactly how we would build this for you.