Data pipeline development
Data pipeline development that connects SaaS apps, databases, files and APIs, using managed connectors where they exist and custom ingestion code where they do not.
Machine Learning & Data
Data engineering services for AI that give your models a solid foundation.
Our data engineering services for AI build the pipelines, storage and quality checks that every serious model depends on. We get the right data, cleaned and documented, to your data scientists and applications on time, every time.
- Batch and streaming pipelines
- Built-in data quality
- Lakehouse and warehouse
01 Overview
The data layer that makes AI projects possible
Ask any data scientist where their time goes and the answer is rarely modeling. It is chasing exports, reconciling IDs across systems, and discovering that a column quietly changed meaning six months ago. When AI projects slip, weak data foundations are usually the cause. Fixing that means treating data as a product: sources ingested on a schedule, transformations under version control, tests on every table, and clear ownership when something breaks.
We provide data engineering services for AI that are designed with downstream models in mind, not just reports. That means preserving history for point-in-time training sets, building feature store development patterns so training and serving agree, and supporting unstructured data like documents and images alongside tables. Whether you need ETL and ELT pipelines into Snowflake, a data lakehouse implementation on Databricks, or real-time data streaming with Kafka, we build in your cloud with infrastructure as code and hand over full documentation.
02 Capabilities
Data Engineering for AI capabilities.
End-to-end Data Engineering for AI: strategy, engineering and the production operations that keep it working.
Warehouse and lakehouse architecture
Data lakehouse implementation and warehouse design on Snowflake, Databricks or BigQuery, with sensible layering from raw to curated tables and cost controls from day one.
Transformation and modeling
ETL and ELT pipelines written in dbt and SQL or Spark, version controlled and tested, producing clean, well-named tables that analysts and models can rely on.
Real-time streaming
Real-time data streaming with Kafka, Kinesis or Pub/Sub and stream processors like Flink or Spark Structured Streaming, feeding low-latency features and event-driven AI applications.
Feature stores and ML datasets
Feature store development and point-in-time correct training sets, so models learn only from information that was available at prediction time and avoid hidden leakage.
Data quality and governance
Automated tests, freshness checks, lineage and access controls using tools like Great Expectations and dbt tests, designed around GDPR and SOC 2 requirements for data handling.
03 How we deliver
From first call to production.
Every Data Engineering for AI engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Unifying scattered customer data
Combine CRM, billing, product usage and support data into a single customer view with consistent IDs, ready for churn models, segmentation and personalization.
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UC-02
Preparing documents for LLM retrieval
Ingest, clean, chunk and embed documents from shared drives, wikis and ticketing tools on a schedule, keeping retrieval indexes in sync with source permissions.
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UC-03
Streaming IoT and sensor data
Collect high-frequency telemetry from devices and machines, process it in real time for alerts, and store history efficiently for predictive maintenance training.
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UC-04
Modernizing a legacy data warehouse
Migrate brittle stored procedures and nightly scripts to a modern, tested cloud stack, running old and new side by side until results reconcile.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Ingestion and streaming
- Apache Kafka
- Fivetran
- Airbyte
- AWS Kinesis
- Debezium
Processing and orchestration
- Apache Spark
- dbt
- Apache Airflow
- Dagster
- Apache Flink
Storage and quality
- Snowflake
- Databricks
- Google BigQuery
- Delta Lake
- Great Expectations
06 Industries
Built for your sector.
We deliver Data Engineering for AI for regulated and fast-moving industries alike.
Why do AI projects need dedicated data engineering?
Models are only as good as the data feeding them, and AI adds requirements that reporting pipelines often ignore: historical snapshots for training, consistency between training and live features, and handling of unstructured content. Without that groundwork, data scientists spend most of their time wrangling data and models break when sources change. Data engineering makes AI work repeatable.
Should we choose Snowflake, Databricks or BigQuery?
Each is a strong platform. Snowflake is popular for SQL-centric analytics, Databricks suits teams doing heavy Spark and ML work, and BigQuery fits naturally on Google Cloud. The right choice depends on your existing cloud, team skills, workloads and budget. We compare them against your actual use cases rather than pushing a single favorite.
What is the difference between ETL and ELT?
ETL transforms data before loading it into the warehouse, while ELT loads raw data first and transforms it inside the warehouse. Modern cloud platforms make ELT the common default because it keeps raw history and lets transformations evolve with tools like dbt. We still use ETL patterns where data must be filtered or masked before landing.
Do we need real-time streaming or is batch enough?
Many use cases work well with hourly or daily batch, which is simpler and cheaper to run. Streaming earns its complexity when decisions must happen in seconds, such as fraud checks, live personalization or equipment alerts. We map each use case to the freshness it truly needs and often combine both in one architecture.
Can you work with our existing data team and tools?
Yes. We frequently extend an existing stack rather than replacing it, adding pipelines, tests or a feature layer where gaps exist. We follow your coding standards, work in your repositories and review processes, and document everything so your engineers can own it. Knowledge transfer is built into the plan, not left to the final week.