Your data pipelines break in silence. A supplier changes their file format. An API goes down. A schema shifts. By the time someone notices, you've lost hours or days of data—and business decisions get made on incomplete information.
Traditional ETL fails because it requires constant manual intervention. Gen AI changes the game: pipelines that monitor themselves, detect failures, communicate with suppliers to request missing data, and self-heal when schemas change. We've built pipelines that achieve 99.8% data availability with minimal human oversight.
How self-healing pipelines work
Our Gen AI pipelines don't just ingest data—they actively monitor data sources, detect problems, communicate with suppliers, and fix issues automatically. Real-time visibility into every data source with autonomous error recovery.
Automated Monitoring & Detection
Pipelines continuously monitor data sources for anomalies: missing files, schema changes, API failures, late deliveries. When something breaks, the system detects it immediately and triggers automated recovery workflows before data loss impacts downstream systems.
Autonomous Supplier Outreach
When data is missing or late, AI agents automatically email suppliers, reference SLAs, escalate to managers when needed, and track responses. No more manual follow-ups. The pipeline handles supplier communication autonomously while maintaining audit trails for compliance.
99.8%
- Data Availability (Self-Healing)
15+
- Average Response Time (Supplier Outreach)
Write-Audit-Publish pattern
Data flows through three stages: Write (ingest from sources), Audit (Gen AI validates quality, detects anomalies, triggers supplier communication if needed), Publish (make available to downstream systems only after validation passes). This ensures broken data never reaches production.
Real-Time Supplier Visibility
See every data source in real-time: on-time vs late, quality scores, communication history with suppliers. When problems arise, you have complete audit trails showing what the system detected, what actions it took, and what responses it received.
Technology stack
Modern data engineering tools combined with Gen AI for semantic understanding. Python and PySpark for data transformation, Airflow for orchestration, OpenAI/Anthropic for intelligent monitoring and supplier communication.
Data Processing: Python, PySpark, Pandas, SQL
Orchestration: Apache Airflow, Dagster, Prefect
Storage: PostgreSQL, Snowflake, Databricks, S3, BigQuery
Monitoring: Custom dashboards, real-time alerting, audit logs
Gen AI: OpenAI GPT-4, Anthropic Claude for semantic validation and supplier communication
$65M
Exit value from products we've built
We built Healthcare Insights—a healthcare finance analytics platform acquired by Premier Inc. for $65 million in 2015.
- Battle-tested in production
- Proven track record with exits
Frequently asked questions
Pipelines continuously monitor data sources for anomalies (missing files, schema changes, late deliveries). When problems are detected, Gen AI agents automatically diagnose the issue, attempt fixes (retry connections, adapt to new schemas), and if needed, email suppliers requesting missing data or clarification. Complete audit trails track every action.
Agents automatically escalate. If a supplier doesn't respond within defined SLAs, the system escalates to their manager, references contract terms, and alerts your team. Meanwhile, the pipeline flags the data gap so downstream systems know not to make decisions based on incomplete information.
Traditional ETL breaks silently and requires manual intervention to fix. Our pipelines actively monitor, detect problems in real-time, communicate with suppliers autonomously, and self-heal when possible. The write-audit-publish pattern ensures broken data never reaches production. You get 99.8% data availability vs typical 85-90% with traditional ETL.
Typical engagement: 8-12 weeks for initial pipeline setup. Week 1-2: Map data sources and define quality rules. Week 3-6: Build ingestion, monitoring, and supplier communication workflows. Week 7-8: Testing and validation. Week 9-12: Deploy to production with monitoring dashboards. Ongoing maintenance starts at $5,000/month depending on data volume.