Warehouses store.
Databasin answers.
Ask it
Connect what you already run. Land it in an open lakehouse you own. Then let anyone in the business ask in plain English and get an answer with the query attached.
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Just your email. We build your workspace and send your sign-in link.
The problem
Buying the platform was the easy part.
Almost every company has now bought a modern data platform: Databricks, Snowflake, Fabric, BigQuery. Very few have gotten the business outcomes they were sold. The platform isn't the bottleneck. Everything you have to build around it is.
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01
The stack is six products, not one
Ingestion, warehouse, transformation, orchestration, BI, and an AI layer. Six contracts, six renewal dates, six vendors pointing at each other when something breaks.
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02
It consumes engineering hours all year
Standing the stack up is the small part. Keeping it running is pipelines that break, schemas that drift, and connectors that need patching. That is capacity spent on plumbing instead of on the work the business is waiting for.
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03
Business users are still waiting
Every question becomes a ticket. The report backlog never clears. People give up and go back to exporting to Excel.
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04
The AI mandate has nowhere to land
Leadership wants agents and AI assistants on company data. Security won't allow it without governance, lineage, and an audit trail. Nobody built those.
What we hear
Five sentences we hear in almost every first call.
If any of these is yours, there is a conversation worth having. Most organizations have three or more at the same time.
“We've got data everywhere and nothing talks to each other. We keep saying we'll modernize.”
A connected ecosystem, without the integration project
75+ connectors across EHR, ERP, CRM, HRIS, databases, files, and SaaS, plus a no-code API builder for anything without one. Around 30 are zero-config: schemas mapped, schedules set, business-ready views built. They're AI-ready the moment they're switched on.
“Leadership wants a ChatGPT for our own data. Security keeps killing it.”
A governed chat layer on your own platform
Plain-English questions become governed SQL against cleaned data, with every claim cited and the query shown. Bring your own model, whether that's Claude, GPT, or an approved internal LLM, and run it in your own tenant so nothing leaves the perimeter.
“We want to scale agents on top of our platform, but we can't staff it or govern it.”
Agents out of the box, in plain English
A no-code skill builder: write the rule in plain language, set the bounds, and the agent runs on a schedule, speaking up only when something's real, with receipts. Read-only by construction, with hard query and row budgets and a full audit trail on every run.
“Our reporting backlog is months deep and it never gets shorter.”
The backlog stops being a queue
Reports are built, filtered, and edited in natural language, then ship themselves: charts generated from the answer, live dashboards for the team, and executive PDFs and Excel delivered to email, Slack, or Teams on a cadence. The report is agent output, not a template someone maintains.
“We have thousands of contracts and PDFs and no way to get anything out of them.”
Unstructured data, queryable, without the engineering
Contracts, PDFs, and other documents are embedded in-platform and queried alongside structured data, with page-level citations you click straight through to the highlighted source. Nothing leaves your environment.
What Databasin is
One platform, assembled before you log in.
An open, governed, agentic data platform. Connectors, pipelines, an Apache Iceberg lakehouse, a semantic layer, and an AI layer, integrated on day one and on one meter. It is not a warehouse you have to staff. It is the working data platform you were trying to build.
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.
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1
You connect a source
Pick Salesforce, NetSuite, Sage Intacct, your SQL Server, or any of the 75+ others. Sign in, choose what you want, set a schedule. A few minutes, no engineer.
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2
The model comes with it
Around 30 sources arrive zero-config: schemas mapped, relationships declared, business-ready gold views built. Not a six-month modeling project you run first.
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3
The AI inherits it
Questions resolve against approved definitions instead of guessing at column names, which is why the answer arrives with SQL you can check rather than a plausible number you can't.
This works the same whether Databasin runs hosted, in your own tenant, or on the Databricks, Snowflake, or Fabric platform you already own.
How it works
Connect. Land. Ask. Automate.
Four steps, and the first three are mostly clicking. You're configuring it to your business, not building it.
Step 01
Connect
Click a connector, authenticate, and the pipeline builds itself. Native sources arrive with schemas mapped, schedules set, and gold views already in place.
Step 02
Land
Data lands in an open Apache Iceberg lakehouse in your storage, or straight onto the Databricks, Snowflake, or Fabric platform you already own.
Step 03
Ask
Business users ask in plain English and get an answer, a chart, and the SQL behind it. One click turns any answer into a governed dashboard the whole team can use.
Step 04
Automate
Turn the questions people keep asking into agents. Executive summaries, data-quality sweeps, anomaly detection and root cause, running on a schedule and delivering to inboxes and channels.
Where it runs
Our lakehouse. Or yours.
Everything above works in all three. The difference is what you get underneath.
Databasin Cloud · hosted
The fastest start.
Fully hosted and fully managed, HIPAA-ready from day one. Sign up, connect a source, and you are querying the same afternoon.
- Trino and Spark over the same Iceberg tables, switchable per query, with no copies and no second warehouse. Doris and DuckDB are in the toolbox.
- Open Iceberg on Parquet, in storage you own. No proprietary format holding your data hostage.
- Nothing to deploy, nothing to patch. $50 in credit, no card.
Your Azure tenant · self-install
Same lakehouse. Your walls.
Install from the Azure Marketplace and the whole platform runs inside your own subscription. Your network, your keys, your policies, no data egress.
- The same engine choice and the same open tables as hosted.
- Unlimited seats for the whole organization.
- Your compute and your LLM, billed by Azure with no markup.
Bring your own · Databricks, Snowflake, Fabric
Already committed? Keep it.
Databasin layers onto the platform you already bought and makes that investment deliver. No rip-and-replace, no migration project, no vendor fight.
- Connectors, pipelines, the semantic model, agents, and governance all still apply.
- You keep the platform, the contract, and the political capital already spent on it.
- The engine choice above is the one thing that stays with our lakehouse.
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.
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Read-only by construction
An agent gets three tools: read the schema, run a SELECT, fetch the result. A skill can narrow that list. Nothing can widen it.
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Bounded every run
Turn limits, query budgets, and hard row caps. No runaway loops, no surprise spend, no agent quietly grinding through the warehouse overnight.
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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.
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Fully auditable
Skill version, tools offered, every turn, every query, completion status. And every answer ships with the SQL, so any number can be checked.
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.
This project was a game-changer. Our campus and clinical operations are now more efficient.
We've gone from thinking we know to truly knowing.
You already bought the platform.
Now get the outcomes.
Just your email. We build your workspace and send your sign-in link.
$50 credit · No card · Or talk to a co-founder