AI Data Pipelines
Clean Data. On Time. Every Time.
What We Do
The most sophisticated AI model produces unreliable outputs when the data feeding it is inconsistent, delayed, or poorly structured. Data pipelines are the infrastructure that determines whether your AI investments work or fail. We build automated pipelines that extract data from every source your business uses, apply transformation and cleaning logic that enforces quality standards, and load it into your AI systems, analytics platforms, or data warehouses on the schedule and latency your use case requires.
Real-time for operational AI. Batch for analytical AI. The right architecture for what you are actually building.
How We Work
We start with a source audit: every system that generates data relevant to your AI or analytics use cases, its data format, its update frequency, and its access method. That audit produces a pipeline architecture document that maps every data flow from source to destination. Build begins with the extraction layer, connecting to databases, APIs, file systems, and streaming sources. Transformation logic is then implemented: field mapping, type normalization, deduplication, enrichment, and quality validation rules.
Load targets are configured with appropriate schemas. Monitoring is built from day one: failed job alerts, data quality anomaly detection, schema change detection, and throughput dashboards. When something breaks, the right person knows immediately with enough context to resolve it quickly.
Why Running Start Digital
Pricing
From $7,500
Typical turnaround: 4-10 weeks
Includes
Frequently Asked Questions
Databases (PostgreSQL, MySQL, MongoDB), APIs (REST, GraphQL), file systems (CSV, JSON, XML), cloud storage (S3, GCS), and SaaS platforms (Salesforce, HubSpot, Stripe). If it has data, we can connect to it.
ETL transforms data before loading it into the destination. ELT loads raw data first, then transforms it in place. We recommend the approach that fits your data volume and transformation complexity.
Yes. We build streaming pipelines using tools like Apache Kafka or cloud-native services for use cases that require sub-second data freshness.
Automated alerts for failed jobs, data quality anomalies, schema changes, and processing delays. Dashboards show pipeline status, throughput, and error rates in real time.
A single-source pipeline delivering to one destination takes 2 to 4 weeks. Multi-source pipelines with complex transformation logic and multiple destinations take 2 to 4 months.
We select tools based on your requirements: Apache Airflow or Prefect for orchestration, dbt for transformations, Kafka or Kinesis for streaming, and cloud-native services when they are the right fit. We are not tool-dogmatic.
We implement schema change detection that alerts the team when upstream changes break downstream assumptions. Pipelines are built with schema validation at ingestion so format changes are caught before they corrupt downstream data.
Ready to get started?
Start with a $3,750 deposit. Balance due on delivery.