Use Case
Features your models can trust
Define feature tables in dbt™ with tests, lineage, and docs. Refresh them on a Bolt schedule and hand them to SageMaker or your own training pipeline when they are ready.
Products
Define, schedule, hand off
The feature pipeline from SQL to training job.
Feature tables in dbt™
Versioned, tested, documented.
- Build point-in-time features as incremental models with tests for leakage and nulls.
- Column-level lineage from the feature back to the raw event.
- Docs generated and searchable in the catalog.
Bolt to training
Trigger the pipeline when the feature models finish.
- Run AWS SageMaker Pipelines, Lambda, Step Functions, Dataproc, or Cloud Run after the feature models finish.
- Webhooks on run completion for anything else.
MCP and SDK
Warehouse context in Cursor, Claude Code, and Copilot.
- Trigger agent runs, stream their output, and query the catalog and lineage from Python with the SDK.
- Get the same context in Cursor, Claude Code, ChatGPT, and Copilot through the MCP server. Sign in with OAuth and keep your own role and permissions.
Workflows
From event to trained model
How ML teams run features through Paradime.
Define features as models
Write feature logic in SQL with DinoAI in Agent mode. A DinoAI agent infers tests from the real data. It catches leakage, nulls, and duplicate keys before the table reaches training.
Refresh on schedule
Incremental feature models run on a Bolt schedule after upstream loads, with freshness checks on every source.
Hand off to training
Trigger the SageMaker pipeline or your own job from Bolt when the features complete, with the result posted to Slack.
Trace every prediction
When a model drifts, trace the feature table back to the raw event to see what changed.
Platform
Fits your stack
Connect your warehouse, git provider, and the tools around them. Keep data inside your own perimeter.
Built to fit your tools and teams
Snowflake, BigQuery, Redshift, Databricks, and a dozen more warehouses. Fivetran, Airbyte, and dlt for loading. Looker, Tableau, Power BI, and Sigma for BI. Slack, Teams, PagerDuty, and Datadog for alerts. Anything else through the API, CLI, or Python SDK.
Enterprise-grade security
SOC 2 Type II, single sign-on, custom roles, audit logs, and the option to deploy the data plane inside your own AWS, GCP, or Azure account.
Customers
Teams that build ML data platforms on Paradime
Efficiency and speed measured on real platforms.
PushPress builds AI-powered data platform with 3x efficiency with Paradime
Motive accelerates analytics engineering by 10x with Paradime
Customer.io boosts dev speed 25%+ and cuts costs 20%+ with Paradime
Resources
Go deeper
Trigger SageMaker pipelines from Bolt schedules after your dbt™ models complete.
Manage Bolt runs, the catalog, lineage diffs, and DinoAI agents from Python.
Expose your warehouse, dbt™ catalog, lineage, and Bolt to any MCP client.
Join industry leaders winning with DinoAI agents
Data engineers and analysts at the most AI-native startups and enterprises use Paradime's DinoAI agents everyday to become market leaders.