Product · Platform

The model itself, on a private endpoint for your firm.

This is the layer Studio and Code are built on, sold directly. Your engineers point their existing systems at it with one configuration change, and the Excel add-in reaches the same endpoint.

The model

orchid01

A finance-native model, trained by us on financial reasoning we generated and graded ourselves, and served on a private endpoint for each firm. It is not a wrapper around a general model with a longer prompt in front of it.

Shipping

What it was trained to do differently

#BehaviourThe mistake it prevents
01Never retypes a numberA figure copied from memory instead of from the data
02Checks before it claimsA conclusion stated first and verified afterwards, or never
03Says how sure it isOverconfidence, and hedging so heavy the answer is useless
04Holds its positionFolding the moment someone disagrees without new evidence
05Reads the whole documentMissing the fact that only appears when two exhibits are read together
06Gets the mechanics rightMillions read as billions, dropped negative signs, invented line items
07Declines the unknowableAnswering confidently about a period it has no information on

Each of these is measured, not asserted. We will share the full evaluation set with your team under NDA.

For your engineers

One line changes. Nothing else does.

The endpoint speaks the same shapes as the SDKs your team already writes against, including streaming and tool calling, so existing internal tooling and agent loops run unchanged.

Grounding, on request

Ask for it and a second pass checks the answer against the documents supplied, returns a score, and marks anything it could not verify — or rewrites it.

Documents as tables

Financial PDFs are turned into structured tables the model cites from, rather than prose it has to re-read.

Your own audit record

Every request, response and source can be posted, signed, to an endpoint you own — so you keep a complete record even when we retain nothing.

The migration

01# change the base URL, keep everything else
02client = OpenAI(
03 base_url="https://llm.orchid.ac/v1",
04 api_key=os.environ["ORCHID_API_KEY"],
05)
06 
07resp = client.chat.completions.create(
08 model="orchid01",
09 messages=messages,
10)

Keep your key management, your prompts and your existing loops.

On the same endpoint

Orchid for Excel

An add-in in the workbook your team already lives in. It reads the sheet, writes cells and formulas, and traces what depends on what — so the output is a model an associate can audit rather than a paragraph describing one.

Shipping

It talks to your firm's endpoint, under the same access and retention settings as everything else.

Deployment options

Run the whole thing yourself

For firms that want nothing on our side: conversations, files, indexed documents, scheduled work, dashboards and logs all move to a database your firm owns and runs. We hold sign-in and usage counts, and no financial content at all.

Shipping

Integrates into

Drops into the stack you already run.

No migration, no rewrite, no second set of credentials to manage.

Existing SDKs

The same request shapes your team already writes against, streaming and tools included.

Coding agents and editors

Anything that accepts a custom endpoint works by changing the base URL.

Internal tooling

Your own services and notebooks switch over with one configuration change.

Excel

The add-in reaches the same endpoint, under the same rules.

Your identity provider

Access follows the accounts and groups your firm already manages.

Your log and audit store

Signed records of every request delivered to infrastructure you own.

Bring your engineers to the call.

Thirty minutes, and they can interrogate the endpoint, the retention settings and where every byte physically sits.