Only the schema is read
dblm learns tables, columns, keys and relationships; your rows stay where they are.
Ask a question the way you'd ask a colleague. dblm writes the query, runs it on your own databases, and hands you the rows, along with the exact query and a sentence on what it found.
Which 10 branches took in the most deposits in 2025, and how much did each bring in?
Top 10 Branches by Deposits
total_deposits by branch_name · USD · chosen by dblm from the shape of the result
| branch_id | branch_name | total_deposits |
|---|---|---|
| 7 | Seattle Downtown | 13,874,215.33 |
| 3 | Portland Pearl | 10,612,508.35 |
| 11 | Denver LoDo | 9,468,331.53 |
| 1 | Chicago Business | 9,027,963.97 |
| 9 | Austin Congress | 8,351,228.00 |
| 5 | Dallas Business | 6,188,745.39 |
| 12 | Phoenix Central | 5,536,902.63 |
| 2 | San Diego Bay | 5,241,389.07 |
| 14 | Boise Main St | 4,907,516.85 |
| 6 | Sacramento East | 4,715,044.34 |
SELECT b.branch_id, b.name AS branch_name,
ROUND(SUM(t.amount), 2) AS total_deposits
FROM branches b
JOIN accounts a ON b.branch_id = a.branch_id
JOIN transactions t ON a.account_id = t.account_id
WHERE t.txn_type = 'deposit'
AND t.posted_on >= '2025-01-01'
AND t.posted_on < '2026-01-01'
GROUP BY b.branch_id, b.name
ORDER BY total_deposits DESC
LIMIT 10
Seattle Downtown leads at $13.87M, $3.26M ahead of Portland Pearl. They are the only two branches past $10M.
Key observations
Hover or tap an observation to see it on the chart.
One engine for every question. Close to your data, never inside it.
The exchange above: three tables joined, the query written, the rows returned, charted and summarised.
A bar chart, a pie, the raw rows, and the query behind them, shown verbatim so any number can be checked.
dblm reads the schema: tables, columns, keys, relationships. Your rows stay exactly where they are.
Point it at a connection, and the schema is indexed and annotated. One question can span several sources at once.
Why askDBLM
The answer was already in your database. It just had to become a ticket first: filed, queued behind eleven others, answered by whoever can write the query, and returned as a CSV that answers yesterday's version of the question.
Every follow-up is a new ticket, and every ticket waits its turn.
STATUS: QUEUED · 11 AHEADQuick question: churn by cohort?
filed Tue 09:14 · queued behind 11 others
Same thing, but last month
follow-up · waits its turn again
Actually, split by region too
the question that would have been a reply
Ask, read the answer, ask the follow-up. It remembers what you were just talking about.
STATUS: ANSWEREDask again any time. there is no queue.
A new interface
For decades, people learned software: where the menu is, which filter to set, how to write the query. Now the software can do the learning. The interface to data is becoming a sentence.
You learn the app. Every question is a path through forms and menus someone designed in advance.
You learn the data model. The answer is in there, if you know which filters, fields and charts to combine.
The app learns you. Say what you want to know in your own words and get the answer itself, not a report to dig through.
Your users shouldn't need to know your schema, or that you have one.
semantic · conversational · grounded in your real data
In your app
Put dblm inside the product you already ship, whether that's a web app or an iOS or Android app. Your users ask in their own words, and the answer arrives as a chart, a table or a sentence, right where they are.
iOS app
Your top three in December:
Web app · React
Deposits dipped 12.6% in 2022 and have climbed every year since. 2025 is 11% above 2021.
Android app
The two business centres lead for 2025:
| Chicago Business | $1.40M |
| Dallas Business | $1.08M |
| Seattle Downtown | $678K |
| Phoenix Central | $384K |
Organizational knowledge
Your definitions, your rules and the answers you already trust. Every question is answered with all of it in mind.
Dashboards
Every answer can be pinned as a report. Drag them onto your board, arrange and resize them, pick the chart that tells the story, and each one keeps itself up to date.
Ask in plain English; when the answer is worth keeping, pin it. The query comes with it.
Drag, resize and re-chart each report (bar, line, pie or table) until the board reads right.
Each tile re-runs its stored query on its own schedule. No model in the loop, so the numbers are exact.
Security
dblm cannot write to your data. Every query it writes, whether SQL, a MongoDB pipeline or a REST call, passes a read-only guard for its own language before it runs. Anything the guard doesn't recognise as a read is refused. There is no flag that turns this off.
INSERT · UPDATE · DELETE · DROP · ALTER · TRUNCATE ·
$out · $merge · POST · PUT · PATCH …
writes of every kind are refused before they ever reach your data.
dblm learns tables, columns, keys and relationships; your rows stay where they are.
Mark one private and it never reaches a prompt, a query or a page.
Every organization gets a dedicated instance of its own, not a shared pool.
Every question, query and result is logged per user, so you can retrace any number.
Each deployment has its own spend ceiling; cost cannot run away quietly.
Prefer to keep everything in-house? Run dblm entirely inside your own network.
Tailored to your team
Everything your team needs to get answers and keep them.
Get rows, a chart, and the query it wrote, every time.
Say "just last month" or "split by region" and it knows what you were talking about.
Postgres here, MongoDB there, a REST API in between.
Save a good question with blanks in it; anyone on the team can run it safely.
Pin an answer and it refreshes on a schedule. There's no model in the loop, so it stays cheap and exact.
The same answer as a dashboard, an image, or a plain CSV for the spreadsheet.
A hidden column disappears from prompts, results and charts alike.
Find how someone got a number six weeks ago.
Each with their own audit trail, never a shared login.
Plans
Start free on a hosted instance, bring your whole team with Enterprise, or run it inside your own network, also for free.
for getting answers from day one
No card needed
for teams that run on their data
Everything in Free, for your whole team
for data that can't leave your network
On your own infrastructure
FAQ
Anything else? Sign up and ask your own database. It's free.
No, that's the point. You ask in plain English. If you do know one, every answer shows the query it ran (SQL, a MongoDB pipeline or an API call), so you can check the working.
PostgreSQL, MySQL, SQLite, MongoDB and ClickHouse today, plus REST APIs described by an OpenAPI or Swagger spec, which surface as queryable tables too. One question can span several of them at once.
No. dblm is read-only by construction: every generated query is checked by a read-only guard for its own language (SQL, MongoDB or a REST API) before it runs, and there is no way to run a write through it. See the limits.
Your rows never leave your database. dblm reads schema metadata (tables, columns, keys, relationships) and sends only that, plus your question, to the language model. You can mark columns private so they are hidden even from the model.
About a minute. Pick an organization name, create your password, and you'll be dropped into a fresh instance. Add a connection and index it, and you're asking questions.
100 queries every month on a dedicated instance of your own, with no card and no time limit. When your whole team is asking, Enterprise is $10 per user per month.
Yes, and self-hosting is free. dblm can run entirely inside your own network, with the same read-only guarantees, so your data and your questions never leave your infrastructure.
Get started
Free for up to 100 queries a month, no card. A dedicated instance, and every answer shows its working.
Your instance is created, dedicated to your organization.
You're in the panel at your-org.askdblm.com.
Add a connection; the schema is indexed and annotated.
First question answered, with the query shown.
Already have an instance? Sign in to your portal →
Free plan · 100 queries a month · ready in about a minute
Sign in at the portal with the password you just chose, and open your instance from there. Its address is . It signs you in with that same account, so there's no second password.