Introducing DinoAI v3.0: The First End-to-End Data Engineering Agent
Go from ticket to pull request in under five minutes - from Slack, Teams, or wherever you work.
Kaustav Mitra
·
5
min read
Today, we're launching DinoAI v3.0 - our most advanced release yet, and a fundamental shift in what a data engineering platform can be.
If you've been following Paradime, you know the journey. In 2024, we launched the first version of DinoAI - a chatbot you could ask questions about your data stack. In 2025, we shipped agent mode - a copilot that wrote code alongside you. With v3.0, we're going much further: DinoAI is now a true background agent that works as a skilled analytics engineer and data engineer on your team. With this launch, Paradime becomes a fully agentic operating system for data engineering.
The bottleneck nobody talks about
Over the past year, we've watched hundreds of teams use DinoAI to code, build models, triage pipelines, and fix issues. And we learned something important: coding speed alone isn't the bottleneck. The real bottleneck is the lead time to actually get work done.
Think about how most data teams operate today. A request comes in from a stakeholder. It gets translated into a ticket. The ticket gets prioritized into a sprint. If it doesn't make the cut, it waits for the next one. Each sprint is roughly two weeks, so a simple ticket - something as straightforward as adding a few columns to a report - can easily take ten to twenty days to ship in a medium-to-large enterprise.
From the business's perspective, everything is delayed. In a normal quarter, you get maybe ten working weeks. If a finance or sales stakeholder requests a dashboard update that takes ten to twenty days to deliver, you've just lost a third of the quarter waiting on a single piece of work.
That's what we're changing. Businesses should move at the speed of light - unconstrained by sprint cycles, ticket queues, or the traditional SDLC.
What DinoAI v3.0 actually is
DinoAI v3.0 is a true background agent that lives where you work. You can invoke it from Slack today. You can invoke it from Microsoft Teams today. And coming very soon, you'll be able to invoke it from Claude, Claude Code, ChatGPT, inside Paradime, and from your phone. We're removing every barrier to using DinoAI from any system your team already spends time in.
Here's what that unlocks: what used to take ten to twenty days now takes minutes.
Imagine it's the week before month-end close. Someone in finance needs a few new columns added to a dashboard report. Traditionally, that creates hours of stress, coordination across teams, and - if it isn't planned perfectly - days or weeks of waiting.
With DinoAI v3.0, that finance team member doesn't even need access to Paradime. They go into a Slack channel where the DinoAI agent is installed, describe the problem in plain language, follow the agent's chain of thought, verify the output, and go from ticket to pull request in less than five minutes.
You're compressing what used to take ten days into five minutes. That's a 90–99% improvement in efficiency - an unprecedented level of velocity and time compression for a data organization.
Under the hood
Inside Paradime, DinoAI now gets a separate entity of its own. There's a dedicated settings panel for the agent, where you connect its code repository, data warehouse, and all the integrations it should have access to - GitHub, your cloud warehouse, column-level lineage across dbt™, Looker, Tableau, and Power BI, and documentation from Confluence, Jira, Linear, Google Drive, Notion, and more.
On the back of that, the agent builds its own context graph. Whenever anyone invokes DinoAI, it automatically pulls from that graph - choosing the right information and the right context to get the work done.
When you trigger a task from Slack, Paradime spins up a secure, isolated sandbox on the cloud inside your Paradime infrastructure. All the work happens within that sandbox. When the task is complete, the sandbox terminates. Multiple people across your organization can work with DinoAI simultaneously - each in their own thread, each in their own sandbox - receiving tabular output, code diffs, and the final pull request right inside the Slack thread.
"Why not just use Claude Code?"
It's the question we get asked more than any other. We've spent months benchmarking it, and here's what the data shows.
One of the metrics we track most closely at Paradime is acceptance rate - the percentage of AI-generated output a user actually accepts as work they'd ship.
On pure analytics and data engineering tasks:
Vanilla Claude: ~30–35% acceptance rate
Claude with skills layered on top: ~60–65%
DinoAI (specialist data engineering agent): ~94%
Why such a dramatic gap? Because DinoAI knows when to bring in which context, and how to use it optimally. To a general-purpose LLM, a Confluence page, a GitHub repo, and a Jira ticket all have equal weight - so it pulls from all of them, inflating the context window, driving up cost, and producing suboptimal output.
DinoAI understands which context matters for which task. It efficiently manages the context window and routes intelligently across models - reasoning-heavy work goes to Opus, straightforward tasks go to Haiku. You get better results, faster, at the same cost per token.
Benchmarked against the industry
And if you strip enterprise context out of the equation entirely and run a pure benchmark - dbt Labs' ADE-Bench - the results are just as clear:
System | Tasks resolved | Score |
|---|---|---|
Paradime DinoAI (Sonnet 4.6) | 38 / 43 | 88.37% |
Altimate AI | 32 / 43 | 74.42% |
Cortex Code CLI | 28 / 43 | 65.12% |
dbt Labs (ADE-Bench team) | 25 / 43 | 58.14% |
Claude Code (baseline) | 17 / 43 | 39.53% |
DinoAI is the undisputed leader for completing analytics and data engineering tasks. And here's the kicker: dbt Labs themselves - the team that built the benchmark - scored 58.14% on their own test. DinoAI beat them by more than 30 points.
Zoom in further and the picture gets even clearer. Across the 258 individual tests that make up the 43 tasks, DinoAI passed 254 of them - a 98.45% test pass rate. It's like having a mid-senior analytics engineer on your team - the kind that would cost more than $100,000 a year - available 24/7, at a fraction of the cost.
Read the full benchmark report →
Every workflow. Every warehouse.
With this launch, we've gone way beyond our traditional focus on dbt™. DinoAI v3.0 now supports:
dbt Core™ and dbt Cloud™
Apache Spark
Stored procedures
SQL
Python
Airflow, Dagster, and Prefect workflows
Essentially, every possible data engineering workflow that exists in any organization, anywhere in the world.
On the warehouse side, DinoAI works with Snowflake, BigQuery, Redshift, Databricks, Trino, Starburst, ClickHouse, SQL Server, Microsoft Fabric, DuckDB, MotherDuck, and more. You don't need to be on a bleeding-edge warehouse to take advantage of agentic features - as long as you have a code repository and a warehouse we support, the agent works for you.
What this means for data teams
DinoAI v3.0 fundamentally changes what your data team spends its time on.
You're no longer constrained by a queue of low-value data requests. DinoAI handles those completely. Your team is freed up to focus on high-value, strategic work - learning new skills, expanding impact beyond analytics into ML, experimentation, forecasting, and all the things business functions desperately need but your team never had time for.
And it means something even bigger: everyone in the organization can now be a data engineer. It's not a specialist skill anymore. Technical and non-technical users alike can take full advantage of the data platform - without burdening the data team.
Getting started
For existing customers, the agent is available in your Paradime instance from tomorrow. Head to the new agent settings panel under your data warehouse connections, set up which warehouse and repository the agent should use, install the updated Slack app, and you're ready to go.
For new customers, it's just four steps:
Sign up for Paradime
Set up your development environment
Set up your agent environment
Start using DinoAI from Slack
Pricing
We haven't finalized the exact mechanics yet, but one thing is certain: pricing will be usage-based, not per-seat. We want you to experience the product before making a long-term commitment.
For the next 30 days, we're running an open beta - no usage limits, no minimum commitment. Sign up and start using it straight away.
What's next
A few things already on the roadmap:
Expanded invocation surfaces - use DinoAI from Microsoft Teams, ChatGPT, Claude, Claude Code, and more
Activity logs & audit trails - a full activity log of every agent action, available within Paradime for enterprise customers
Multi-repository support - the agent will be able to work across multiple code repositories in a single environment
Final thoughts
With the advent of tools like Claude Code, the cost of generating code is effectively near zero. What truly differentiates teams now isn't the code they generate - it's the quality of that code, how quickly they get to that quality, and how quickly they can unlock and optimize organizational processes around data.
DinoAI v3.0 is our answer. It's a background agent available 24/7 - so for global teams, data team availability is no longer a blocker. If you need something done, you trigger it whenever you need it.
At Paradime, we're thrilled to be the first company building a complete, end-to-end data engineering agent - one that unburdens data teams while increasing the productivity of the entire business by orders of magnitude.
We can't wait to see what you build with it.
Ready to try DinoAI v3.0? Sign up for the 30-day open beta →
