We make your customers successful on your platform.
You sold them a great platform. We make sure they actually use it. Databasin is the implementation ally: we rescue stalled cloud deployments, accelerate time-to-value, and drive your consumption metrics faster than anyone else.
HIMSS '23 · '24 · '25
Featured by Microsoft and Databricks at three consecutive annual conferences
75+ connectors
Including schema-aware Epic and Workday, which stall more deployments than anything else
Days, not months
Time to a governed, production lakehouse on your customer's existing platform
The Problem We Solve For You
Don't let the "Plumbing Trap" threaten your renewal.
When your customers buy Microsoft Fabric, Databricks, or Snowflake, they have massive ambitions for AI and analytics. Then reality sets in. Their internal IT teams lack the specialized cloud data engineering skills to operationalize it.
Your platform deployment stalls. Consumption flatlines. The relationship is at risk.
Databasin is the pluggable intelligence platform, co-created at Washington University School of Medicine. For your customers it acts as the implementation layer. It abstracts the complexities of data engineering and turns hard-coded pipelines into simple, repeatable processes.
Accelerated Consumption
We get data flowing into your platform in days, not months. A stalled deployment turns into active, growing consumption.
Protected Relationships
We eliminate the friction of deployment. You become the partner who solved the problem instead of the vendor who sold a stalled investment.
No Competition
We don't compete with your platform. We are the single implementation layer that makes it usable — and we bring you into every win.
The 30-Second Discovery Guide
How to spot a Databasin opportunity in your book.
Keep an ear out for these phrases during customer calls. If you hear them, it's time to bring us in.
We bought [Databricks / Fabric / Snowflake] but we're not really using it yet.
→ Classic stalled activation. This is our entry point.
We've been trying to get Epic data out of Clarity for months.
→ Databasin solves this in days. Schema-aware Epic connector, built in production at WashU Medicine.
Our team doesn't have the skills to manage the platform long-term.
→ The exact skills gap we fill. Databasin abstracts the engineering complexity so their team doesn't have to own it.
We're paying an implementation consultant to do this, but we can't afford them forever.
→ We replace the run-state dependency. A consulting engagement has an end date; one ongoing platform doesn't.
Partner Success Stories
How account teams are putting customers back in motion.
Real deployments where Databasin closed the gap between platform investment and platform value.
Snowflake · Health System Rescue
Saving a stalled Snowflake deployment at a Midwest health system
The Challenge
The customer's internal engineering team spent months unable to get their complex Epic EMR data ingested and reporting-ready. The deployment was completely stalled, putting the Snowflake investment at risk.
The Play
The Snowflake AE introduced Databasin to the Chief Data Science Officer as an integration layer, not a competing platform. It was the piece that made Snowflake actually work.
The Win
Databasin's pre-configured Epic connector resolved the integration complexity. Data began flowing into Snowflake within days. That gave the customer the analytics capabilities they had bought Snowflake for, and it protected the partner's account.
Microsoft · Databricks · Research Acceleration
Accelerating a complex cancer research platform at a major academic medical center
The Challenge
The Chief Research Information Officer needed a modern cloud solution for complex cancer research data, but lacked the internal team to manage the technical demands of an Azure and Databricks architecture.
The Play
Building on Databasin's production deployment at WashU Medicine, the Microsoft AE introduced Databasin as the automated framework to manage the infrastructure, so the research team could focus on the science.
The Win
Databasin provided the implementation layer for ingestion and modeling, accelerating time-to-value and driving immediate consumption within the customer's Azure and Databricks environments.
How It Works
From stalled account to closed deal in four steps.
01
You identify the stall
A customer with an active deployment who can't get Epic, Workday, or their legacy systems into the platform and delivering value.
02
We meet the customer together
A joint call with the Databasin co-founder and your AE. We diagnose the stall and present Databasin as the resolution layer rather than a competing platform.
03
Databasin deploys alongside your platform
BYO mode means Databasin layers on top of your customer's existing environment. Their investment is protected and extended — not replaced.
04
Your deal closes, customer succeeds
The customer gets value from the platform they bought. Your deployment succeeds. We structure co-sell terms that work for both sides.
Platform Fit
Additive with all three platforms. Never a competitor to any of them.
Microsoft
Azure · Fabric · Synapse
Your customer is on Azure but Epic or Workday data isn't flowing. Databasin's connectors land data into their Azure tenant, so PHI never leaves their environment. Medallion architecture (bronze for raw data, silver for transformed, gold for governed analytics) gets them to governed gold data without custom engineering.
Referral signals
Health system that can't get Epic into Fabric or Synapse
Customer asking about HIPAA-compliant data architecture
Azure OpenAI project stalled with no governed data layer underneath
Databricks
Delta Lake · Unity Catalog · MLflow
Your customer bought Databricks for the lakehouse and ML capabilities, but custom-built ingestion is consuming all their engineering capacity. Databasin's modules replace the custom ETL, freeing the team to use Databricks for what it's actually good at.
Referral signals
Engineers maintaining 20+ custom pipelines instead of building analytics
Databricks deployment underutilized due to ingestion backlog
Epic or Workday identified as blockers in QBR
Snowflake
Data Cloud · Cortex AI · Marketplace
Your customer is on Snowflake but the data stack is still fragmented: separate ETL tools, no medallion architecture, no governed semantic layer. Databasin adds the structured ingestion and transformation layer that makes Snowflake deliver on its promise.
Referral signals
Customer paying for Fivetran or MuleSoft on top of Snowflake
Raw data going straight to BI with no medallion architecture
Cortex AI stalled because underlying data isn't governed
Request a Co-Sell Evaluation
We'll review the opportunity and follow up within one business day.