Before you can train models, you need pipelines that deliver clean, timely, trustworthy data from every corner of your enterprise. We build the data infrastructure that makes AI possible.
Discuss your data pipeline needsYour enterprise has the data. It's scattered across 30 systems in 15 formats with no validation layer. Customer data in Salesforce, transactions in your ERP, product data in a custom database, behaviour data in event logs. Getting all of that clean, consistent, and flowing to where your ML models need it is the unglamorous work that makes AI possible. Skip it and your models train on garbage.
Batch and streaming pipelines from every enterprise source. Databases, APIs, SaaS platforms, file systems, event streams, IoT sensors. We connect to what you have, in the format it's in, without requiring your source teams to change anything.
Raw data becomes ML-ready features. Cleaning, normalisation, aggregation, temporal features, cross-source joins. We build feature stores so computed features are reusable across models. Compute once, use everywhere.
Schema enforcement on every record. Anomaly detection on incoming data distributions. Completeness checks, freshness monitoring, and automated alerts when data quality drops. Your models never train on corrupted or stale data because the pipeline catches problems before they propagate.
Pipelines connect to your existing data warehouse, data lake, feature store, and ML training infrastructure. We work with Snowflake, BigQuery, Databricks, Redshift, S3, and custom systems. The output is a production data platform that serves both analytics and AI workloads.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting.
Timeline
6-10 weeks
Team
1-2 senior data engineers + 1 ML engineer (to ensure pipelines serve model requirements)
Deliverables
Production data pipelines, feature store, data quality monitoring dashboards, alerting rules, pipeline documentation, runbook for operations
After launch
Optional retainer for pipeline maintenance as new data sources are added
A retail company wanted to build demand forecasting models but their data was fragmented. Point-of-sale data in one system, inventory in another, promotions in spreadsheets, weather data from an external API, and historical sales in a legacy database with 8 years of accumulated format changes. We built a unified data pipeline that ingested from all five sources, normalised formats, computed 40+ features (rolling averages, seasonal patterns, promotion impact windows, weather correlations), validated data quality at every stage, and delivered a clean feature set to their ML training environment daily. Their data science team went from spending 70% of their time on data preparation to spending 90% on model development.
Representative of a typical engagement.
Usually yes. Analytics queries and ML training have different requirements. Analytics needs aggregated, human-readable data. ML needs granular, feature-engineered data at the record level. We build ML-specific pipelines that sit alongside your existing analytics infrastructure. We don't replace it, we extend it.
Apache Spark, Apache Kafka, Apache Airflow, dbt, Fivetran, and cloud-native tools depending on your environment. We select based on your data volume, latency requirements, existing infrastructure, and team familiarity.
Streaming pipelines using Kafka or cloud-native equivalents (Kinesis, Pub/Sub). Features are computed in near-real-time using stream processing frameworks. For true real-time inference, we build feature stores that serve pre-computed features with sub-millisecond latency alongside real-time features computed at request time.