Use Case
Fix failures before anyone is paged
When a production run fails, DinoAI reads the logs and finds the cause. It opens a pull request with the fix in the Slack thread where the alert landed. You review and merge.
Products
Three levels of self-healing
Turn it on for a schedule, read the diagnosis, or write your own healer.
Self-healing schedules
Self-healing on any schedule.
- Turn on self-healing per schedule and name the agent that runs on failure.
- The session starts in the failure's Slack thread when the run fails.
- The agent sees prior attempts, so it never opens a duplicate pull request.
Pipeline Insights
Read a summary, not a log file.
- Every failed run gets a plain-language summary of what failed, why, and where to look.
- Click Fix with DinoAI on the run page or the Slack post. It opens a session that proposes the fix as a pull request.
Programmable Agents
Write your own healer.
- Build in Agent Studio or as YAML in the repo, with a tool allowlist per agent.
- Set an agent on failed TurboCI checks. It pushes a fix and escalates instead of retrying.
- Example recipes cover Dagster failures, MongoDB schema drift, and an incident agent that delegates to specialist agents.
Workflows
From failure to merged fix
What happens when a production run breaks.
The run fails
You get the failure in Slack or Teams with the run and error context. If you connected PagerDuty, Datadog, incident.io, or New Relic, an incident opens there too.
DinoAI diagnoses
The agent reads the logs, then checks upstream sources and recent commits. DinoAI finds the failing model and the breaking change.
DinoAI opens a fix PR
DinoAI opens a pull request with the proposed change and tests, linked in the failure thread. It goes through TurboCI like any other pull request.
You review and merge
Nothing ships without approval. Merge to deploy, or retry from the point of failure once the fix is in.
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 keep production running on Paradime
Uptime and runtime measured on real production schedules.
How to successfully run 400 models every hour with a 100% uptime
Emma Cuts Pipeline Runtime In Half With Paradime
Three reasons why Capital on Tap uses Paradime
Resources
Go deeper
Turn on self-healing on a Bolt schedule. A DinoAI agent then diagnoses a failed run and opens a fix pull request.
A DinoAI agent that diagnoses a failed run and opens a fix pull request. It checks prior attempts so it never opens a duplicate.
An agent that fixes failed TurboCI checks. It pushes one fix commit to the pull request branch, then escalates.
An orchestrator that runs a log analyzer, a query profiler, and an owner notifier in parallel, then posts one summary.
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.