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 require consultants to touch. Reporting that takes weeks. An 18-month implementation timeline for capabilities your 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 designed for organizations that need to move in weeks.
Your ERP, your reporting platform, your integration layer — each one owned by a vendor or consultant who charges for every change. Your team can't self-serve. Your roadmap is someone else's project queue.
Finance has their numbers. Operations has theirs. Sales has theirs. Nobody agrees because nobody's 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 — not building the analytics your leadership is 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 discover the transformation requires a production-grade data foundation they don't have. AI pilots stall because the underlying data isn't clean, governed, or accessible enough to power anything real.
The same platform. The pain is specific to you.
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 which were designed 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 internal production and inventory systems — none of which talk to each other fast enough to inform decisions while they're still relevant.
- 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 across student information systems, financial aid platforms, research grant management, and alumni systems — and struggle to connect any of it in time to act on it. Institutional Research teams spend most of their time extracting and reconciling, not 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. Different entry point.
- One platform replaces your ETL tool, connector layer, data warehouse, and BI stack — one contract, one renewal, one team to call
- Significant cost reduction versus a comparable fragmented stack — documented and defensible at budget time
- Weeks to value, not 18 months — production-grade lake house operational on day one, with your data sources connected in days
- Self-service reporting for finance and operations leaders — no analyst queue, no consultant required for routine questions
- AI querying on your actual data — plain-English questions answered instantly, not escalated to IT
- 200+ pre-built connectors plus an AI-powered API builder for any source that isn't in the library — new integrations in hours, not weeks
- Medallion architecture (bronze for raw data, silver for transformed, gold for governed analytics) provisioned automatically — no custom ETL to write or maintain
- Deploy on your existing Azure tenant or Databricks environment — adds the layer you're missing without displacing what's already running
- Engine-agnostic: Delta Lake, Apache Iceberg, Spark, or native SQL — no storage lock-in at migration time
- LLM-agnostic AI layer — your approved model behind your own security boundary, querying governed gold-layer data
Production-proven. Not a mid-market compromise.
Databasin was co-created at Washington University School of Medicine — one of the most complex, regulated, and high-stakes data environments that exists. What came out of that environment 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.
Your stack,
simplified.
Private enterprise install or hosted. Weeks to value — not months.