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Paradime.io provides a centralized analytics workspace built on top of dbt to help data teams manage data transformation and analytics tasks in one place. It integrates with dbt so users can run models, monitor runs, view data lineage, and collaborate on projects from a single platform. Its focus on tight dbt integration and an all-in-one analytics workflow helps teams avoid juggling multiple tools. The goal is to speed up analytics delivery and make it easier for teams to work together from development to deployment.
Industries
Data & Analytics
Enterprise Software
Company Size
1-10
Company Stage
Seed
Total Funding
$630K
Headquarters
London, United Kingdom
Founded
2020
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Total Funding
$630k
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Paradime | Omni. Paradime now integrates with Omni Kaustav Mitra Jun 22, 2026 I'm finally happy to announce that Omni integration is live on Paradime. Automating data engineering with AI agents requires having a shared context so that its agents can build data models and update semantics in a single workflow. Speaking with the Omni team about how Paradime Labs, Inc. can bring value to its joint customers across mid-market and enterprise accounts, it was a no-brainer. With this integration, joint customers will have the Omni context in Paradime, including lineage, data catalog, and semantic code context all in a single place. At Paradime, Paradime Labs, Inc. has built its agents that can build and run data models, self-heal broken pipelines and automate tech debt work. Its agents spin up secure, and isolated sandboxes and then traverse across multiple code repos and systems to make connected and coordinated changes. This is a big differentiator compared to closed apps like dbt Cloud(TM), where you only have dbt(TM) context compared to Paradime where the context spans the entire data platform. For example, customers can use the Paradime MCP with the BI context from a single place and ask questions about Omni, analytics, and the code behind from a single, governed MCP endpoint. Similarly, customers can use the Paradime agents to automate working on technical debt. As an example, customers are already building AI-first workflows to automate data modelling, metric creation, and semantic layer updates to take an agent-first approach to data operations. So that anyone across the business can request new data that requires new data models and the whole system can then be built in an autonomous way. This brings automation to both doing data modelling and running data pipelines. If you are exploring automating data engineering with agents, then please reach out to Paradime Labs, Inc. or sign up for a free trial - Paradime Labs, Inc.'d love to talk.
Self-Healing Pipelines: the next generation of data orchestration. Paradime Labs, Inc. is excited to announce Self-Healing Pipelines in Paradime Bolt - the next generation of data orchestration in the age of AI. Kaustav Mitra Apr 28, 2026 A few months ago, Paradime Labs, Inc. said that "tomorrow" pipeline fixes would be fully automated. Today, that tomorrow is here. From dbt(TM) orchestrator to end-to-end pipeline platform. Paradime Bolt started life as a state-aware dbt(TM) orchestrator - chosen by teams who wanted reliability without complexity. It was never designed to replace Airflow, Dagster, or Prefect. It was designed to complement them: easy to use, state-aware for dbt(TM), capable of running any Python or API command, and available 24/7. But over the past year, Paradime Labs, Inc. has watched its customers stretch what they run on Bolt. Ingestion jobs. Feature stores for ML pipelines. API-driven processes. Modern data teams don't just transform - they orchestrate complex, multi-system pipelines that span dbt(TM), Python, Spark, and APIs across the entire stack. Users are now building agentic pipelines too! So Bolt evolved with them. Bolt is no longer just a dbt(TM) orchestrator. It's an end-to-end orchestration layer for any data pipeline, at any scale, from a five-person startup to a complex enterprise. But scale doesn't change one inevitability: pipelines fail. The MTTR problem, revisited. Pipeline failures are inevitable. The question has never been if - it's how fast can you recover? Mean Time to Repair (MTTR) is the metric that decides whether a failure is a blip or a crisis. A pipeline that breaks at 2 AM and takes six hours to fix means stale dashboards by morning, missed SLAs by lunch, and a frustrated CEO by dinner. In its last release, Paradime Labs, Inc. made a bold projection: 80% of pipeline errors should be fixable by automated coding agents. Paradime Labs, Inc. shipped AI failure summaries directly into Slack and MS Teams, and Paradime Labs, Inc. wrote: "With the release of AI fixes in Slack, today the workflow will be: view the fix in Slack, click a button to open Paradime's AI-native IDE, and ask DinoAI to implement the repair... Tomorrow, this will become a fully automated process." Today is that tomorrow. Introducing Self-Healing Pipelines. Paradime Labs, Inc. is excited to announce Self-Healing Pipelines in Paradime Bolt - the next generation of data orchestration in the age of AI. Self-healing pipelines introduce two new capabilities to Bolt: 1. Fix from Slack (and soon MS Teams) - when a pipeline fails, the failure summary in Slack now comes with a Fix with DinoAI button. One click spins up a DinoAI background agent that: * Reads the failure logs * Walks across every connected repository (dbt(TM) mesh, Spark jobs, Looker/Omni semantic layers) * Generates the fix * Runs your dbt(TM) tests to validate * Opens a pull request ready to merge - all without you opening your laptop 2. Self-Healing Mode - for pipelines you want truly hands-off, you can now opt in any Bolt schedule with a two-line self_healing block in your paradime_schedules.yml. When a self-healing pipeline fails, DinoAI is triggered automatically. No buttons. No human in the loop until the PR is ready for review. You still keep practitioner control: DinoAI prepares the change, runs the tests, and opens the PR - but the merge is yours. See it in action. How self-healing works. Here is what happens between a failure and a green pipeline: Here's what a real self-healing run looks like in Slack - root cause, before/after diff, validation, and a PR ready to merge: The agent works across multiple repositories. If your dbt(TM) mesh has three repos and the failure requires changes both upstream and downstream, DinoAI traces the dependency chain and fixes all the right places in a single, coherent pull request. Same goes for hybrid setups - dbt(TM) in one repo, Looker or Omni in another, a Spark pipeline in a third. DinoAI traverses all of them. Hours and days | minutes. The compounded effect is dramatic. Here's how a typical 2 AM pipeline failure plays out before and after self-healing: What used to consume an engineer's night and most of the next morning now resolves itself before standup. Works with what you already have. A question Paradime Labs, Inc. hear constantly: "I'm already running Airflow, Dagster, or Prefect. Do I need to migrate everything to Paradime to get self-healing?" The answer is no. Bolt now ships with orchestration triggers for every major platform - Airflow, Dagster, Prefect, Azure Data Factory, Google Cloud Composer, and more. You can trigger your existing pipelines from Paradime, and as soon as they finish, the logs flow back into Bolt. From there, DinoAI takes over. It analyzes logs, traces them back to your code repository, queries your data warehouse for context, and recommends a fix. Paradime Labs, Inc. support any code-based framework - dbt(TM), Spark, Python, or your own meta-framework. The only requirement is that your pipelines live in code (not in a UI-only tool) and that Paradime is connected to your warehouse and your repositories. You don't migrate. You just plug in. The impact: up to 90% MTTR reduction. Teams using Bolt have already reported up to 70% MTTR reduction compared to alternative orchestrators. With self-healing pipelines, Paradime Labs, Inc. project this number to climb to up to 90% - particularly for the routine, deterministic failures that account for the majority of pipeline incidents. This is a step-change in how data orchestration works in the age of AI. It's a redefinition of what an orchestrator should do. The orchestrator of the future doesn't just run pipelines. It heals them. Try Self-Healing Pipelines today. Start your 14-day free trial of Paradime Bolt and experience self-healing pipelines for yourself.
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 Apr 8, 2026 Today, Paradime Labs, Inc. is launching DinoAI v3.0 - its 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, Paradime Labs, Inc. launched the first version of DinoAI - a chatbot you could ask questions about your data stack. In 2025, Paradime Labs, Inc. shipped agent mode - a copilot that wrote code alongside you. With v3.0, Paradime Labs, Inc. is 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, Paradime Labs, Inc. has watched hundreds of teams use DinoAI to code, build models, triage pipelines, and fix issues. And Paradime Labs, Inc. 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 Paradime Labs, Inc. is 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. Paradime Labs, Inc. is 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(TM), 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 Paradime Labs, Inc. get asked more than any other. Paradime Labs, Inc. has spent months benchmarking it, and here's what the data shows. One of the metrics Paradime Labs, Inc. 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: 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. Every workflow. Every warehouse. With this launch, Paradime Labs, Inc. has gone way beyond its traditional focus on dbt(TM). DinoAI v3.0 now supports: * dbt Core(TM) and dbt Cloud(TM) * 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 Paradime Labs, Inc. 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. Paradime Labs, Inc. haven't finalized the exact mechanics yet, but one thing is certain: pricing will be usage-based, not per-seat. Paradime Labs, Inc. want you to experience the product before making a long-term commitment. For the next 30 days, Paradime Labs, Inc. is 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 its 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, Paradime Labs, Inc. is 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. Paradime Labs, Inc. can't wait to see what you build with it.
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Industries
Data & Analytics
Enterprise Software
Company Size
1-10
Company Stage
Seed
Total Funding
$630K
Headquarters
London, United Kingdom
Founded
2020
Find jobs on Simplify and start your career today