Enterprise complexity. Without the enterprise budget.
Between $350M and $1B in revenue, most organizations carry a data infrastructure that costs like a Fortune 500 but performs like an afterthought. Legacy systems that need a consultant to touch. Reporting that takes weeks, and an 18-month implementation timeline for capabilities the business needs now.
The modern data stack was priced for companies twice your size. The consultants who implement it were too.
Mid-market organizations didn't make bad decisions. They made reasonable ones: each tool bought for a legitimate reason, each consultant hired to solve a real problem. But the accumulated cost of a fragmented stack now exceeds what any single capability is worth. And the 18-month enterprise implementation model was never meant for organizations that need to move in weeks.
Your ERP, your reporting platform, and your integration layer are each owned by a vendor or a consultant who charges for every change. Your team can't self-serve, so your roadmap lives in someone else's project queue.
Finance has their numbers. Operations and sales have their own. Nobody agrees, because nobody is pulling from the same place. Reconciliation happens in Excel, after hours, by the same two people every quarter.
Snowflake, Databricks, Tableau, and their connector ecosystems were designed and priced for organizations with dedicated platform teams and eight-figure data budgets. Mid-market organizations pay enterprise rates for a fraction of the capability they need.
The enterprise data implementation playbook — discovery phase, architecture design, phased rollout, change management — was built for organizations with a year to wait. Most mid-market decisions can't survive that timeline.
Your data team spends 70% of their time ingesting data, fixing pipelines, and maintaining the plumbing. None of that is the analytics your leadership keeps asking for. The 70/30 plumbing trap hits mid-market organizations hardest, because there's no slack to absorb it.
Mid-market organizations are told AI will transform their business. Then they discover the transformation requires a production-grade data foundation they don't have. Pilots stall because the underlying data isn't clean, governed, or accessible enough to run anything real.
One platform, but the pain is specific to your industry.
Regional banks, credit unions, insurance carriers, and wealth management firms spend more of their data budget on compliance reporting than on the analytics that actually grow the business. The data exists. Getting it into a form that satisfies regulators and executives at the same time is the problem.
- Regulatory report assembly pulling from five separate systems, reconciled manually
- No unified customer view across lending, deposits, and investment products
- Audit requests that take weeks because data lineage doesn't exist
- Core system data locked behind vendors who charge for every extract
Construction and real estate organizations run projects through platforms like Procore, Sage, or Viewpoint. None of those were built to give executives a consolidated view of portfolio performance, cost variance, or labor utilization across active jobs.
- Job cost actuals vs. estimates scattered across project management, accounting, and field tools
- No real-time labor and equipment utilization across the portfolio
- Subcontractor and supplier data siloed from internal cost tracking
- Forecasting built on spreadsheets that break when project managers leave
Mid-market CPG organizations sit between retailer data, distributor data, syndicated point-of-sale feeds, and their own production and inventory systems. Those systems don't talk to each other fast enough to inform a decision while it still matters.
- Retailer sell-through data arriving days after the decision window has closed
- No unified view of inventory across DCs, co-manufacturers, and 3PLs
- Trade promotion ROI impossible to calculate because sales and spend data live in different systems
- Demand planning running on manually assembled spreadsheets two weeks behind actuals
Colleges and universities generate enormous volumes of data: student information systems, financial aid platforms, research grant management, alumni systems. Connecting any of it in time to act on it is the hard part. Institutional Research teams spend most of their time extracting and reconciling rather than analyzing.
- Enrollment funnel visibility fragmented across CRM, SIS, and financial aid platforms
- Retention risk identification weeks behind when intervention is still possible
- Research grant compliance reporting assembled manually from disparate award management systems
- Alumni and advancement data siloed from student outcome records, blocking longitudinal analysis
Whether you're the buyer or the builder, we have your conversation.
Same platform, two ways in.
- One platform replaces your ETL tool, connector layer, data warehouse, and BI stack, which leaves one contract, one renewal, and one team to call.
- The cost reduction versus a comparable fragmented stack is significant, and it's documented well enough to defend at budget time.
- Weeks to value instead of 18 months, because a production-grade lake house is operational on day one and your data sources connect in days.
- Finance and operations leaders report for themselves, so routine questions skip the analyst queue and the consultant.
- AI querying on your actual data: ask in plain English, get the answer instantly rather than escalating to IT.
- 75+ pre-built connectors, plus an AI-powered API builder for any source that isn't in the library. New integrations take hours, not weeks.
- Medallion architecture (bronze for raw data, silver for transformed, gold for governed analytics) is provisioned automatically, with no custom ETL to write or maintain.
- Deploying on your existing Azure tenant or Databricks environment adds the layer you're missing without displacing what's already running.
- Engine-agnostic across Delta Lake, Apache Iceberg, Spark, and native SQL, so nothing locks in your storage at migration time.
- An LLM-agnostic AI layer runs your approved model behind your own security boundary, against governed gold-layer data.
Production-proven. Not a mid-market compromise.
Databasin was co-created at Washington University School of Medicine, which is one of the most complex, regulated, and high-stakes data environments that exists. What came out of it is an enterprise-grade platform available to every organization at a fraction of enterprise pricing. Mid-market organizations get the same architecture, the same connector library, the same AI layer. Not a scaled-down version.
One platform
where your stack
used to be.
$50 credit · No card · First answer in 5 minutes · Private enterprise install or hosted