Talk to Data · Product · On-prem and on-cloud
Talk to your data.
An open agent that answers against your data, structured or unstructured, and shows the query or the paragraph behind every answer. Built for companies who want the answers without another per-seat software bill, and who need their data to stay where it is.
In short
- No SQL, no dashboard hunting, no waiting on an analyst
- Reads your data, structured or unstructured
- Runs on-prem or in your own cloud
- PII and PHI never have to leave
- No per-seat licenses
Bring your own model and your own database. Nothing here is locked to a single vendor.
The interface
One box, whatever the question is about.
A no-show rate, a control rate, a 340B eligibility rule, a denials pattern: four systems, four teams, four places a number normally hides. The agent works out which one holds the answer, so the person asking does not have to.
Ask anything about your business.
One interface across your systems. Every answer shows where it came from.
What it does
One question. Your numbers and your documents.
The answer usually lives in two places at once: a table somewhere, and a paragraph in something nobody has read since it was signed.
Ask the way you would ask a colleague
"Which customers slipped last quarter and why?" No dashboard to find, no report to request, no SQL to write. The people who need the answer stop having to route it through the two people who can write the query.
It reads your database
The agent writes the query, runs it against your warehouse or operational database, and explains what it did. You see the SQL behind every answer, so a skeptical analyst can check the working rather than take it on faith.
It reads your documents too
The numbers rarely tell the whole story. Contracts, agreements, policies, invoices, and the PDFs nobody has time to read sit alongside the database, so one question can draw on both.
Every answer shows its source
The row, the query, or the paragraph it came from. An answer you cannot trace is worse than no answer, because someone will act on it. Citations are how this becomes something you can put in front of a board.
Where it runs
Your data does not have to go anywhere.
The reason most organizations stall on this is not the technology. It is that the obvious options mean handing their data to someone else.
On-prem
Entirely inside your own network, with an open weight model on your hardware. Nothing leaves the building, including when the question and the answer both contain regulated data.
In your own cloud
Your AWS, Azure, or GCP account, behind private endpoints. You keep the tenancy, the keys, the logs, and the bill, rather than handing your data to another vendor's multi-tenant service.
PII and PHI safe
Sensitive data does not have to leave your boundary for the agent to work. Redaction, de-identification, and strict deployment patterns are the default rather than an enterprise add-on.
Your access model, respected
The agent runs as the person asking, inheriting the permissions they already have. Someone who cannot see payroll in the database cannot see it through a question either, because that is enforced in the data layer, not in a prompt.
The economics
Answers should not cost per person.
Per-seat pricing forces you to ration access, which quietly means most of the company stops asking questions at all. The people furthest from a license are usually the ones closest to the work.
No per-seat BI licenses
Traditional BI charges by the head, so access gets rationed to the people who can justify a seat and everyone else waits on them. One deployment serves the whole company, which changes who is allowed to ask a question.
No analyst bottleneck
However large the data team, there is a queue in front of the few people who can write the query, and the routine questions are most of it. This clears the routine 80 percent and leaves those people free for the work that deserves them.
Open, not locked in
Bring your own model and your own database. Nothing here depends on a single vendor's cloud, pricing, or roadmap, so the economics stay yours to control rather than being repriced at renewal.
Runs on what you already own
Your existing database, your existing documents, your existing infrastructure. No migration, no rip-and-replace, no data platform program that has to finish before anyone sees value.
Who it is for
Less about your size than your constraints.
- Operations and finance teams waiting days for a number they need in an hour
- Companies paying for BI seats that most of the business never logs into
- Healthcare and regulated firms that cannot send data to a public API
- Teams sitting on a warehouse that only two people can actually query
- Businesses with answers buried in contracts, invoices, and PDFs
- Anyone who has been quoted six figures for enterprise analytics software
FAQ
Common questions.
How is this different from the AI features in our BI tool?
Those answer questions about the data already modeled in that tool, and they charge per seat to do it. This connects directly to your database and your document library, runs wherever you decide, and does not price access by the head. If your BI vendor's roadmap and renewal terms are comfortable, keep them. This is for companies who would rather own the thing.
How do we know the answer is right?
You see the query. Every answer carries the SQL it ran or the passage it drew from, so the answer is checkable by anyone who knows the data. We also build an evaluation set from your real questions before go-live and hold the system to it, rather than shipping and hoping.
Do we need a data warehouse first?
No. It can run against an operational database, a warehouse, or a mix, plus your documents. A tidy warehouse gives better answers, and we will tell you where the model or the naming is working against you, but there is no platform program to finish before you get value.
What stops it from exposing data someone should not see?
The agent runs as the user, not as a service account with broad rights. Row and column level permissions are enforced in the database, so unauthorized data never reaches the answer in the first place. Prompts are not an access control mechanism and we do not treat them as one.
Which models does it use?
Whichever fits your constraints. A frontier model where the data class allows it, or an open weight model on your own GPUs where nothing may leave the building. It is a configuration choice per deployment, not a decision baked into the product.
How do we get started?
A short scoping conversation, then a pilot against one database and one set of documents with a defined list of questions it has to answer well. You see it working on your own data before committing to anything wider.
Let us help you move faster
Need talent, advisory, or a delivery partner? Start a conversation.
Tell us what you are building, hiring for, or still figuring out. We will respond with a clear next step, even if that is a short advisory conversation rather than a project.