Data teams at four companies discovered something game-changing: they could eliminate most of the friction slowing down their dbt™ development with DinoAI. Here's how they did it—and the dramatic results they achieved.

The Results at a Glance

• Mr. & Mrs. Smith: Eliminated workflow fragmentation, automatic sync across 100+ models

• Zego: 30-40% productivity improvement, zero onboarding time for new warehouses

• Motive: 50% faster debugging, week-long migrations completed in hours

• The Sharing Group: Smart automation over manual file operations, 100% file coverage on bulk migrations

Mr. & Mrs. Smith: LookML Without the Learning Investment

The Challenge: Eleas Sanbar, Director of Data & Insights at Mr. & Mrs. Smith, faced a common but critical bottleneck: maintaining LookML files for Looker while focusing on SQL development. As a luxury travel company curating boutique hotels worldwide, Mr. & Mrs. Smith needed sophisticated analytics to understand customer preferences and booking patterns. However, constantly switching between SQL model development and LookML file maintenance was fragmenting the team's workflow and slowing down delivery of business insights.

The DinoAI Solution: DinoAI transformed how Eleas's team handled the SQL-to-LookML workflow, automatically understanding code structure and making intelligent decisions about how to maintain consistency across both systems.

"The most impressive part of DinoAI is that I don't have to tell it what I've changed. When I'm working through a dbt™ model with 100 dimensions exposed, I can just say 'update the LookML file' and it automatically knows what needs to be updated without me having to remember what I've done or track every change."

The Results:

• Uninterrupted development flow: Developers stay focused on SQL logic without context switching

• Automatic synchronization: LookML files updated intelligently based on SQL changes

• Smart context awareness: DinoAI identifies what changed across complex models with 100+ dimensions

Why It Worked: DinoAI understood the relationship between SQL models and LookML requirements, eliminating the manual overhead of keeping both systems in sync while preserving developer focus on business logic.

Zego: Instant Expertise Across Data Warehouses

The Challenge: Jannan Gunaratnam, Senior Analytics Engineer at Zego, joined with BigQuery experience but needed to work in Snowflake immediately. Zego, a leading insurtech company providing flexible car insurance across Europe, required different syntax and optimization patterns, and Jannan faced pressure to contribute quickly to their data platform. Warehouse migration usually means weeks of research, documentation reading, and trial-and-error learning—time Zego's fast-moving analytics team couldn't spare.

The DinoAI Solution: Instead of studying Snowflake documentation, Jannan simply asked DinoAI contextual questions like "How do I optimize this for Snowflake vs BigQuery?"

"We use Snowflake here, in my previous company we used BigQuery. So obviously, syntaxes are also different... it's very useful in terms of just quickly asking DinoAI—in Snowflake how can I do this compared to BigQuery and it will just save me time to kind of look on Google."

The Results:

• 30-40% productivity improvement: "I could probably say maybe thirty percent as a percentage, thirty, forty percent"

• Zero learning curve: Contributing to production models from day one

• Strategic focus: Time spent on optimization strategy instead of syntax research

Why It Worked: DinoAI connected directly to Zego's repository and Snowflake instance, providing answers specific to their actual data structure, not generic examples.

Motive: From Python Complexity to dbt™ Mastery

The Challenge: Caitlin Gowdy, Senior Data Engineer at Motive, needed to migrate their complex "headcount model" from fragile Python/Pandas code to dbt™ with Jinja. Motive, a fleet management and logistics platform serving over 120,000 customers, relied on this model to track month-over-month employee data and sales metrics, but it was slow and prone to breaking in its Python implementation. Caitlin didn't know Jinja syntax and couldn't afford weeks of learning time while maintaining critical business reporting.

The DinoAI Solution: Instead of learning Jinja first, Caitlin used DinoAI as a real-time translator—pasting Python code and getting working Jinja output instantly.

"I don't know how to write in Jinja, but I want to be able to produce this quickly enough that I'm not sitting there remotely learning another language... Just taking Python code and pasting it in there and I'm like, write this in SQL or in Jinja, and it takes ten seconds and it's done."

The Results:

• 50% reduction in debugging time: "That's really cut down my debugging time by a lot, at least by half"

• Week-to-hours transformation: Complex migrations that used to take a week now finish in just a few hours

• Team standardization: Established automated .dinorules that generate 12 standardized date fields from 3 inputs

Why It Worked: DinoAI understood Motive's existing code patterns and warehouse schema, so translations weren't generic—they followed the team's established conventions.

The Sharing Group: Smart Automation for Project Management

The Challenge: Jim de Clercq, BI Manager at The Sharing Group, needed to handle dbt™ project maintenance—updating deprecated configurations, migrating YAML structures, and ensuring consistency across hundreds of files. With MyWheels, The Sharing Group has a leading shared mobility platform operating across Europe, managing complex data pipelines that track vehicle usage, customer behavior, and operational metrics. Manual updates are error-prone and time-consuming, but essential for project health and compliance across TSG's multi-market operations.

The DinoAI Solution: When Jim needed to migrate dbt™ freshness configs, DinoAI created a Python script to handle the bulk operation instead of updating files individually.

"It creates some kind of Python script for me to run rather than trying to do it manually... which is a pretty smart solution rather than scanning through and actually manually moving all the files."

The Results:

• 100% coverage confidence: Ensured no files were missed in bulk operations

• Intelligent problem-solving: DinoAI chose script generation over manual changes for efficiency

• Workflow integration: Everything accomplished within Paradime without external tools

Why It Worked: DinoAI recognized the pattern in Jim's request and chose the most reliable approach, demonstrating intelligence beyond simple code generation.

The DinoAI Difference: Why These Results Are Possible

Jannan Gunaratnam explains what makes DinoAI different:

"The good thing about this is obviously because it connects to your repository... it can give you a kind of more defined answer specific to your use case, right? Like if you go to ChatGPT... it wouldn't understand kind of what you're trying to do unless you prompt it specifically." — Jannan, Zego

Warehouse-Aware Intelligence

Unlike generic AI tools, DinoAI connects directly to your warehouse and repository  to understand your actual tables, columns, and relationships. This means generated code works with your real data structure, not hypothetical examples.

Repository Context

DinoAI reads your existing dbt™ project to understand naming conventions, file structures, and coding patterns. When it generates new code, it follows the standards your team has already established.

Analytics Engineering Focus

Built specifically for modern data workflows—dbt™, SQL, YAML, Jinja, and warehouse optimization. It understands the tools and processes that matter to analytics engineering teams.

What These Teams Learned: Best Practices

Start with Context, Get Better Results

Provide DinoAI with specific files and examples rather than vague requests. As Eleas notes: "When I found challenges, it's often asking too much at the same time with too much vagueness... you get a much better output" when you break complex tasks into smaller, specific steps.

Use .dinorules for Team Standards

All successful teams established .dinorules early to capture their preferences—from YAML formatting to naming conventions to code structure patterns.

Think Workflow Integration, Not Just Code Generation

The biggest wins came from integrating DinoAI into existing workflows rather than treating it as a standalone tool.

The Bigger Picture: Compound Benefits

When you eliminate syntax friction, everything else accelerates:

• Faster iteration: Debug logic instead of syntax → More experimentation → Better solutions

• Higher quality: Automated consistency → Fewer bugs → More reliable pipelines

• Strategic focus: Time on business problems → Greater impact → Better outcomes

• Team scaling: Codified best practices → Consistent quality → Faster onboarding

Ready to eliminate development friction for your data team? Learn more about DinoAI or try it out for free!