Byzfunder: A Second Opinion on Every Classification with Jev on MotherDuck
- Cross-checked rule-labeled bank transactions with AI, inside MotherDuck
- Flagged the gaps for analysts to fix in the rules
- Running on day one, with no new pipeline to build
Case Studies
Ducks That Lead the Way
"Every meeting now is shifted from trying to find answers to what to do about them. Users are able to make a Dive or chat with MotherDuck through Claude, find new design issues, and ask: what should we do about it?"
Ben Holtzman
Staff Design Enablement Engineer
"On top of compute-heavy warehouses, agent-driven queries would create a serious cost problem. MotherDuck’s architecture is an excellent fit for the kind of exploration and unpredictable queries that agents need to be useful."
Pablo Ferrari
Director of Data Engineering
"We now have a doctor vibe-coding artifacts and tools that can talk to all the data that the company has, run processes, and then actually go and affect them directly."
Greg Inman
Chief Technology Officer
"Product velocity is a hallmark of any hypercompetitor. MotherDuck & Omni ship product with incredible speed, and that’s why we trust them to move with us."
Kristov Paulus
Founder, Chief Investment Officer
"We used to do analytics in a MySQL database with all of our daily device, telemetry, and image processing data. There was no way to scale that further using MySQL. With MotherDuck, we’re finally starting to find patterns in our data to help customers grow produce more effectively."
Rob Teeuwen
Data Scientist
"Our data pipelines used to take eight hours. Now they’re taking eight minutes, and I see a world where they take eight seconds. This is why we made the big bet on DuckDB and MotherDuck. It’s only possible with DuckDB and MotherDuck."
Jim O'Neill
Co-founder and CTO
"MotherDuck’s innovative approach to managing data and providing easy-to-use tooling has allowed us to expand our application use cases (e.g., getting data into the client's browser) and offer more value to our customers."
Ravi Chandra
Chief Technology Officer
Explore all case studies
Hazel: Building an AI Co-Worker for E-Commerce on MotherDuck
- Hundreds of dbt models on a central MotherDuck warehouse
- Hypertenant per-customer databases via CTAS for isolation
- Local DuckDB for dev, MotherDuck for prod
GoodShip: Building an Analytics Agent for Global Logistics
- Analytics agent built on MotherDuck's remote MCP in under a week
- Hypertenancy model for full data isolation
- Sub-second queries where Postgres timed out
Building a Data Platform for the Agent Era at AheadComputing
- 74% of engineers onboarded onto MotherDuck within the first weeks of rollout
- 3 months → 1.5 hours for dashboard requests, replaced by self-service Dives
- 1 week to production
Together AI: Scaling a Data Platform while Scaling the Company
- 128 unique Hex users across product teams, C-suite, and board
- 40 read scaling replicas supporting concurrent analytics
- AI agent workloads run freely without cost spikes or rate-limiting
Cut dbt runtime from 2 hours to 7 minutes and found top-notch support
- Deployed customer-facing embedded analytics with Omni
- Replaced Snowflake evaluation with simpler auth and managed infrastructure
Gets sub-second insights enabling same-day claims filing & 95%+ acceptance
- Sub-second query performance on healthcare data that other warehouses took multiple seconds to process
- Same-day claims filing for 80-90% of appointments (previously took up to a month)
- 95%+ claims acceptance rate through rapid iteration on rejection patterns
Saves $20k per month with hypertenancy architecture on MotherDuck
- Saves $20k on Amazon Athena and Glue spend
- Achieves per-customer compute isolation and granular scaling
- Runs 14x faster queries on customer-facing datasets
Gets answers for business users with the MotherDuck MCP
- Cut batch CSV processing times from 6 hours to 6 minutes
- Built 20x faster rule generation for coding medical procedures
Cuts analytics costs by 65% while gaining SQL expressibility
- Moved analytics off of SingleStore, saving 65% on analytics costs
- Transformed analytics workflow and ML model development with MotherDuck
Accelerates retail analytics while avoiding expensive BI tools
- Moved analytics off production Postgres, enabling customer-facing reports
- Deferred thousands monthly on Tableau by improving data architecture
Reduced costs while empowering users to find their own answers
- Migrated from Postgres to MotherDuck, eliminating overnight analytics queries
- Empowered non-technical colleagues to explore data independently with the MotherDuck UI
Turns costly analytics blocker into go-to-market enabler
- Cut data pipeline processing time from 24+ hours to under 1 hour
- Enabled per-tenant data retention flexibility, letting customers choose 5-180 days without infrastructure changes
Accelerates same-day delivery analysis and decisions
- Replaced costly, high-maintenance Redshift setup with a serverless architecture
- Transformed daily operations via instant drill-downs in Looker for faster issue resolution and deeper analysis
Improved analytics query performance from minutes to seconds
- Chose MotherDuck after finding ClickHouse, Snowflake, Databricks, BigQuery too complex and expensive
- Enabled lifetime course analytics for 16,000+ courses after MongoDB queries proved impossible
Built a Data-First Investment Platform with MotherDuck and Omni
- Migrated from local DuckDB files to cloud infrastructure instantly with zero configuration changes
- Empowered non-technical investment analysts to explore complex data models without engineering bottlenecks
Reduced Analytics Pipeline from 24+ Hours to Under One Hour
- Unified analytics across MySQL, Postgres, and MongoDB data sources in one efficient warehouse
- Enabled complex time-series analysis on 150+ columns of IoT data at 10x lower cost
Transformed 8-Hour Financial Data Pipelines into 8-Minute Workflows
- Launched AI-powered financial agents using MotherDuck's performance to enable real-time data exploration
- Migrated from Postgres to achieve 60x faster pipeline execution for complex financial data
Achieved Interactive Browser Visualization of 18 Million Data Points
- Built a fully interactive data exploration app in just one hour
- Delivered sub-second query responses through hybrid local-cloud execution without managing any servers
Unlocked Lightning-Fast Analytics and New Customer Capabilities
- Reduced analytical query times from minutes to seconds while eliminating traditional data warehouse costs
- Enabled new browser-based data experiences impossible with Postgres or Snowflake alternatives