Warehouses store.
Databasin answers.

Connect what you already run. Databasin lands it, keeps it current, and hands the person who knows the business the platform a data team would have built. Ask in plain English, get the answer with the query attached.

›Why did gross margin drop in July?

41.2%↓ 3.8 pts

Freight and duty at the Ohio distribution center account for 2.9 of those points. The rest is product mix.

Sage IntacctSalesforce

The query behind it 
select m.period,
       round(100.0 * (m.revenue - m.cogs) / m.revenue, 1) as margin_pct,
       round(100.0 * m.freight_duty / m.revenue, 1)       as freight_pts
from gold.margin_monthly m
where m.site = 'OH-DC' and m.period in ('2026-06', '2026-07')
order by m.period

›Which customers are at risk of churning?

14accounts

Usage down and support volume up for sixty days running. Together they carry $1.9M of recurring revenue, and nine of them renew before December.

SalesforceHubSpot

The query behind it 
select count(*)                                   as accounts,
       sum(a.arr)                                as arr_at_risk,
       count(*) filter (where a.renewal_date < '2026-12-01') as renewing_soon
from gold.account a
join gold.usage_rollup u on u.account_id = a.account_id
join gold.ticket_rollup t on t.account_id = a.account_id
where u.trend_60d < 0 and t.trend_60d > 0

›What changed in payroll this quarter?

+6.1%headcount flat

Overtime is 78% of the increase, and almost all of it sits in Clinical Operations. Headcount did not move.

Workday

The query behind it 
select p.quarter, d.department,
       sum(p.total_pay)    as cost,
       sum(p.overtime_pay) as overtime,
       sum(p.headcount)    as heads
from gold.payroll_detail p
join gold.department d on d.department_id = p.department_id
where p.quarter in ('2025-Q4', '2026-Q1')
group by p.quarter, d.department order by overtime desc

$50 credit · No card · Sign in with Google or Microsoft

75+ connectors · Pipelines and schedules included · An open lakehouse you own · Or run the whole platform in your own Azure tenant

Who it's for

Built for the person who knows the business.

Not the person who knows the infrastructure. Below is the entire job, source system to scheduled report, done by one person in an afternoon. No engineer, no consultant, no six-month project.

  1. 01

    Connect the accounting system

    Pick Sage Intacct, QuickBooks, or whatever you run. Sign in, choose what you want, set a schedule. Databasin builds the pipeline. There is nothing to deploy and nothing to write.

  2. 02

    Connect the CRM

    The same three clicks. Two systems that have never agreed on a customer list are now sitting in one governed place, refreshing on their own.

  3. 03

    Open the views that came with them

    Around 30 sources arrive already modeled: schemas mapped, relationships declared, business-ready views built. Revenue, customers, margin and AR are there before you write anything. This is the part that normally takes a quarter.

  4. 04

    Ask why the number moved

    Plain English, against approved definitions rather than a guess at column names. The answer comes back with a chart and the SQL underneath it, so you can check it before you send it to the CFO.

  5. 05

    Schedule it for Monday at seven

    It ships itself from then on, to email, Slack, or Teams, as a dashboard, a PDF, or an Excel file. Nobody rebuilds it next month. That is the difference between a report and a ritual.

None of this is a permissions bypass. The person doing it sees exactly what they were already allowed to see.

The problem

Nobody is waiting on a warehouse.
They're waiting on an answer.

The systems are already bought and they already hold the data. What is missing is everything between those systems and the question somebody actually asked.

  1. 01

    The month-end ritual

    Exports from accounting, exports from the CRM, a spreadsheet that reconciles them, and a final number nobody fully trusts. Every month, by hand, by the same person.

  2. 02

    One person is the bottleneck

    Every new question becomes a ticket for the one analyst who knows where things live. The backlog never gets shorter, so people stop asking and go back to Excel. The cost is the questions that never get raised.

  3. 03

    The usual fix costs more than the problem

    Ingestion, warehouse, transformation, orchestration, BI, and an AI layer. Six contracts and somebody to run them. For a company this size that is not a purchase, it is a hiring plan.

Databasin is the other option: the whole chain, already connected, run by the team you have. See what the assembled stack actually costs →

Why that's possible

It works because you're not assembling it.

Connectors, pipelines, an open Apache Iceberg lakehouse, the semantic model, reporting, and agents are one product, on one meter, integrated on day one. Completeness isn't the pitch. It's the reason a single person gets to an answer at all.

Five stacked layers. On top, lit: the outcome — the analyst, agents and dashboards. Beneath it, in descending order: automations, the semantic layer, an open Apache Iceberg lakehouse on Trino and Spark, and 75+ connectors. Arrows point up from the stack to the outcome.

Your data arrives already modeled.

Most AI tools point a language model at a dump of raw tables and hope it guesses right. Ours doesn't have to guess, and that is the difference between an answer you can send to the board and a plausible number you can't check.

  1. 1

    You connect a source

    Salesforce, HubSpot, Workday, Sage Intacct, your SQL Server, or any of the 75+ others, plus a no-code builder for anything without a connector yet.

  2. 2

    The model comes with it

    Around 30 sources arrive zero-config: schemas mapped, relationships declared, business-ready gold views built and ready to query.

  3. 3

    The AI inherits it

    Questions resolve against approved definitions instead of guessing at column names, which is why every answer can ship with SQL you are able to check by hand.

Hosted or in your own tenant, this is the same platform. Bring your own Databricks and it stays a first-class query and semantic target. Land data in Snowflake or Fabric and the governed views are built there too, then federate them through Trino to ask questions across everything.

Proof

Built in production, not in a pitch deck.

Databasin runs today in regulated healthcare, research, finance, and insurance environments. Customers are anonymized because of the sectors they operate in. The outcomes are not.

$2M+
Annual savings across deployed customers
12,500
Staff hours returned to the business each year
$750K
Clinical operations savings at a single health system
18%
Higher grant proposal success rate at a research institution

What used to take weeks or sometimes even months now happens in minutes.

Chief Financial OfficerAcademic medical research institution

We've gone from thinking we know to truly knowing.

Dean of EducationHigher education institution

Where it runs

Two ways to run it. Neither one is a project.

Same platform, same features, same open tables. The only question is whose walls it sits inside.

Databasin Cloud

The fastest start.

Fully hosted and fully managed, HIPAA-ready from day one. Sign up, connect a source, and you are asking questions the same afternoon. Nothing to deploy, nothing to patch, no infrastructure to own.

  • $50 in credit to start. No card, no seat licenses.
  • Metered per minute and tracked in real time, with every rate published.
  • Trino and Spark over the same open Iceberg tables, switchable per query.
Start Free →

Databasin Private on Azure

Your tenant. No meter running.

Install from the Azure Marketplace and the whole platform runs inside your own Azure subscription: your network, your keys, your policies, no data egress. None of the usage meters apply. Databasin is licensed per module, so the line item you approve does not move with how much your team queries.

  • Unlimited seats. No per-user math and no license true-up.
  • Your own compute and your own LLM, billed by Azure directly with no markup.
  • A dedicated support portal, not a shared queue.
Deployment and security → Talk to us about Databasin Private

Already committed to Databricks, Snowflake, or Fabric? Keep it. Databasin layers on top and makes that investment deliver, and the connectors, pipelines, semantic model, agents, and governance all still apply. No migration, no rip-and-replace, no vendor fight. How that works →

Governance and security

The part that gets AI through review.

Most AI-on-your-data projects die in security review, not in the demo. Databasin was built inside a regulated healthcare environment at an academic medical center, so the constraints came first and the capability was built to fit inside them.

Deployment, boundaries, and what we don't claim →

  • Skills run read-only, and it is checked

    Every query an agent skill runs is verified to be a single SELECT before it executes. On the scheduled lane the agent is handed three tools and a skill asking for anything else fails the run rather than degrading quietly.

  • Permissions inherited, not re-invented

    Queries run against governed data under the connection's existing permissions. People see what they were already allowed to see, and nothing about asking in English widens that.

  • Audited where it runs, in your own database

    Every scheduled agent run records its pinned skill version, the tools it was offered, each turn, each query and how it ended. The query log lives in your own metastore, not ours. And every answer ships with the SQL, so any number can be checked by hand.

Bring one system.
See what your team can do with it.

Just your email. We build your workspace and send your sign-in link.

$50 credit · No card · Or talk to a co-founder