The Data Exploration Bottleneck
Data scientists spend weeks manually exploring massive datasets, writing exploratory SQL, and producing repetitive notebooks just to hypothesize which signals might improve model performance.
The problem
Real-Time ML Production Isn't Broken. Feature Discovery Is.
Building real-time ML applications requires two distinct capabilities: knowing what features to build, and knowing how to serve them. Today, the first is the critical bottleneck. While modern cloud infrastructure has mastered real-time serving pipelines, identifying the data signals that actually drive high-performing models remains slow, manual, and human-dependent.
The Slipped Value Window
Because validation happens late and manually against business outcomes, competitive advantages are lost and projects stall before a single feature ever reaches production.
Idle Serving Infrastructure
Databricks provides world-class serving primitives — Spark Real-Time Mode, Lakebase Online Feature Store, and Model Serving. These powerful systems sit completely idle until the right features are defined to flow through them.
The Gap to Production
The disconnect between manual data science exploration and production feature serving is where the majority of enterprise real-time ML projects stall.
Solution overview
Meet Tredence's T-Discovery
T-Discovery is an agentic AI accelerator that bridges the gap between raw data and real-time production deployment. It introduces an autonomous intelligence layer called Milky Way that tells your native infrastructure exactly what features to serve, while Databricks' core engines handle how to serve them.
How it works
Discover
Domain experts describe business objectives in plain language — for example, "What drives customer churn?" The Milky Way agentic layer autonomously profiles signals and explores your lakehouse data to surface candidate features.
Validate
The engine automatically tests, scores, and ranks feature hypotheses against labeled historical outcomes, selecting only the candidates with verified mathematical lift.
Generate
T-Discovery auto-generates production-ready feature specifications, including optimized SQL pipelines, rich metadata, and primary and foreign key definitions.
Deploy
The deploy orchestrator automatically publishes pipelines directly into Unity Catalog feature tables, Spark Real-Time Mode compute, and Lakebase online stores.
Solution capabilities
Automate the Hardest Parts of Real-Time Feature Engineering
T-Discovery combines autonomous data reasoning with your existing Databricks ecosystem to eliminate manual pipeline development.
Agentic Hypothesis Discovery
Replace weeks of manual notebook exploration with goal-driven agents that systematically mine lakehouse tables for hidden signals.
Automated Feature Spec Generation
Instantly produce clean, enterprise-grade SQL, primary keys, and schema definitions tied directly to Unity Catalog metadata.
Native Databricks Orchestration
Deploy features directly to Spark Real-Time Mode, Unity Catalog feature tables, and Model Serving endpoints without manual configuration.
Lakebase Online Optimization
Automatically stream and surface optimized features through Lakebase, the managed, autoscaling PostgreSQL OLTP engine within Databricks.
Continuous Monitoring & Re-Discovery
Built-in loops monitor for feature drift and staleness, automatically re-triggering the hypothesis discovery phase to keep models accurate over time.
Zero Data Egress Security
T-Discovery runs entirely within your own secure Databricks workspace, with no external dependencies and full data privacy.
Reference Architecture
An Intelligence Layer on Top of Your Native Databricks Stack
T-Discovery's Milky Way agentic layer plugs into Unity Catalog, Spark Real-Time Mode, Lakebase, and Model Serving — generating and deploying the features your real-time engines were built to run.
Click the diagram to zoom
Business & Performance Impact
Measurable Outcomes. Guaranteed.
90%+
Reduction in Time-to-Feature
Collapse feature engineering cycles from weeks of manual scripting to hours of automated agent execution.
Zero
Egress Security Architecture
Maintain strict data privacy compliance by running all agent exploration entirely within your native cloud security boundaries.
Max
Infrastructure ROI
Unlock the full value of Databricks Lakebase and Spark Real-Time Mode by populating them with optimal data streams from day one.
FAQs
Questions Enterprises Ask Before They Start
How does T-Discovery interact with my existing Databricks setup?
T-Discovery acts as an orchestration and intelligence layer that runs completely within your workspace. It doesn’t replace Databricks. It generates the code, schemas, and pipelines that feed directly into Unity Catalog, Spark Real-Time Mode, and Lakebase.
What is Lakebase, and why does T-Discovery use it?
Lakebase is the high-performance online feature store layer. It is a managed PostgreSQL OLTP engine that handles auto-scaled, low-latency lookups for real-time inference applications.
Do our data scientists lose control over what features get built?
Not at all. Domain experts and data scientists provide the initial business objectives and maintain full oversight. T-Discovery handles the heavy lifting of writing SQL, validating mathematical lift, and establishing primary keys, freeing data scientists to focus on strategy and model architecture.
Request a Demo
See T-Discovery in Action
Every enterprise sits on data signals it never operationalizes. See what's possible when intelligent agents discover, validate, and serve your features automatically — natively on Databricks.