Large language models introduce new possibilities for transforming, enriching, and understanding data. Tasks that once required brittle hand-written rules are becoming tractable: extracting structure from clinical notes, standardizing messy source data, mapping between systems that never agreed on a schema.
Successful AI systems require more than models. They require reliable pipelines, evaluation, monitoring, and continuous improvement. A model that is right 95% of the time is a liability in a production data system unless the engineering around it can find, measure, and correct the other 5%.
AI is also changing the pace of the work itself. As companies adopt AI development, code ships faster than ever, and fragile data pipelines get exposed more than ever. PromptFormr builds pipelines to scale for that AI-native world: pipelines that carry their own tests, evaluation, and monitoring, so they hold up as the rate of change goes up.
PromptFormr explores how traditional data engineering and AI can work together to create systems that become more reliable over time.
PromptFormr is a data engineering company exploring how AI changes the future of data systems.
Themes What guides the work
AI as a component of data systems
Models are pipeline components: versioned, tested, and monitored like any other transformation.
Production reliability
Data that downstream teams, clinicians, and agents can trust every day, not just a working demo.
Evaluation-driven development
Golden datasets and regression suites define what correct means for the data, so changes ship with evidence.
Continuous improvement
Feedback loops that detect, evaluate, and improve pipeline behavior over time.
Human + AI engineering workflows
AI writes the first draft of the work. An engineer reviews and approves what ships.
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