About Us

Tempora turns enterprise data into agent-ready DS/ML workflows

Tempora gives AI agents a structured way to prepare enterprise data for data science and ML — replacing sprawling agent-generated scripts with compact workflows for data analysis and model development.

Tempora connects directly to your existing data sources — helping preserve governance, lineage, and control while enabling agents to produce workflow code that is easier to review, debug, and maintain.

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We're Rethinking Data Preparation for AI Agents

Enterprise data science and ML is rapidly moving from hand-crafted Python scripts and Jupyter notebooks to agent-generated workflows. But agents shouldn't have to generate complex data-prep pipelines from scratch for every new project.
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Tempora provides agents with a powerful data-prep layer for working with enterprise data — producing compact and reliable workflows that are easier to review, debug, and maintain.

Our Core Principles

Data Prep Is Infrastructure, Not Agent-Generated Glue Code

AI agents should not have to rebuild complex data-prep logic from scratch for every project. Tempora gives agents tested data-preparation tools for creating concise, robust workflows that data scientists and ML engineers can review, debug, and maintain.

Integration, Not Duplication

Agentic DS/ML workflows should not depend on unmanaged exports or duplicated datasets. Tempora connects directly to enterprise data sources, allowing agents to prepare analysis- and ML-ready data while preserving governance, lineage, and control.

Lineage for Agent-Driven Workflows

When agents generate ad hoc scripts, data-prep logic can become difficult to trace and reproduce. Tempora keeps datasets, transformations, targets, and batches tied to structured workflows, making agent-driven DS/ML easier to inspect, reproduce, and trust.

Self-Hosted for Controlled Agent Access

Tempora runs in your environment, not ours. Deploy it in your VPC or on-premises to give agents a controlled data-prep interface — exposing only the data and operations each workflow requires, without handing agents open-ended access to the underlying data store.

About Us

Nick Hughes

Founder & CEO

Nick founded Tempora to address the lack of purpose-built tools for agentic data science and ML. Before Tempora he worked as an independent ML consultant where he experienced first-hand the complexity in preparing data for ML projects.

Previously, Nick was the CEO and co-founder of Lecida – an industrial AI startup, and before that led early ML efforts at iRhythm Technologies. He holds a DPhil in Machine Learning and Signal Processing from the University of Oxford and undertook postdoctoral research at Stanford University.