Reasoning + Verifier

Ledgr

Ledgr is the owned model core: a domain-tuned reasoning model that resolves AP/AR/close exceptions and a separate verifier model that gates every action before it touches the ledger.

5 lettersReasoning + VerifierOwned model · GPU-accelerated

Part of the Closora close

LedgrDoculaVocoraTwinnReconnLedgrDoculaVocoraTwinnReconn
Overview

What Ledgr does

Ledgr is the owned model core: a domain-tuned reasoning model that resolves AP/AR/close exceptions and a separate verifier model that gates every action before it touches the ledger.

Every product in the suite shares memory, audit trail, and the verifier — composing one autonomous close function from owned models.

At a glance

  • 3-way match exception resolution
  • Pre-write verification gate
  • Accrual and coding judgment
Key features

Why teams deploy Ledgr

How it works

Inside the workflow

Ingest

Ledgr receives its inputs from your ERP, banks, or document stores in real time.

Reason

Owned, fine-tuned models make a structured, tool-callable decision with rationale.

Verify

An independent verifier gates the action against policy before anything is written.

Record

The outcome and its provenance land in the immutable audit timeline.

Specifications

Built on the NVIDIA stack

Ledgr specifications
TrainingNVIDIA NeMo on DGX
ServingTensorRT-LLM + Triton
Verifier latency<100ms per action
MoatProprietary close-cycle corpus
Inside the product

A look at the surface

Live view
Exception detail
Audit trail
Use cases

Where it earns its keep

Growth
$2k–$10k /mo

Ledgr included with the Growth platform.

  • Unlimited agents
  • Shared audit trail
  • SSO + audit log
  • Shadow onboarding
Book a demo
Related products

Pairs well with

FAQ

Ledgr questions

Yes — packaged as a NIM microservice for VPC or air-gapped deployment.

Yes — each product is self-contained and can start on a single workflow without the rest of the suite.

Against a labeled benchmark with verifier catch-rate reported transparently.
Ready when you are

See Ledgr on your data.

We'll run it in shadow mode against a recent period so you can compare.