Data Engineering & Analytics

Your data is an asset. Treat it like one.

Businesses generate enormous amounts of data — in documents, databases, third-party APIs, and spreadsheets — and most of it sits unprocessed. We build the pipelines that extract, transform, and deliver that data as clean, queryable, actionable intelligence.

What we build

• ETL pipelines — batch and streaming data ingestion from heterogeneous sources into PostgreSQL or data warehouses
• PDF extraction — automated parsing of company accounts, financial reports, and carbon emission disclosures using PDFMiner and AI-assisted Gemini OCR
• JSON schema-based data extraction — structured extraction of specific data points (financials, ESG data) from unstructured documents using LLM prompting
• Pandas transformation pipelines — data cleaning, normalisation, aggregation, and feature engineering at scale
• OpenSearch integration — full-text search indices, semantic search with vector embeddings, and aggregation dashboards
• GCP prediction batches — cost-effective large-scale inference using Google Cloud batch prediction
• Data quality monitoring — automated checks, anomaly detection, and alerting via CloudWatch or Sentry

AI-assisted data extraction

We have built pipelines that extract financial data points and carbon emissions metrics from thousands of company accounts using LLM-based extraction — combining PDF OCR, JSON schema prompts, and batch processing APIs to keep costs low and accuracy high.

Search infrastructure

Whether you need full-text search across millions of records or semantic similarity search using embeddings, we design and manage OpenSearch clusters optimised for your query patterns and data volume.