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Diligence a pool before the window closes

Read the files, surface the exceptions, and price against what the documents actually say rather than what the tape claims.

A bid window is short and a pool is large, so diligence gets sampled. Sampling is a reasonable answer to a labour constraint. It is a poorer answer to a data constraint, which is what this usually is.
01

Read the whole pool, not a sample

Document intelligence runs across every file in the pool at once, so coverage becomes a compute decision rather than a staffing decision.

02

Exceptions with the page attached

Every flagged item cites the document and location it came from, so an analyst confirms it in seconds instead of re-opening the file.

03

Your criteria, not a generic ruleset

A Bespoke SLM is trained on your own eligibility and pricing criteria and constrained to cite its sources.

04

API access to everything

Every action available in the interface is available over the API, so the output lands in your own models rather than in another dashboard.

Stack on top of what you already run.

Keep your system of record. Qualr layers on top of it and reads and writes both ways, so nothing has to be ripped out and nothing has to be re-keyed at the seam. These are the systems that matter most for this channel.

Or we build you a custom end to end. If what you are running is the actual problem, we replace it — origination, documents, pricing, and workflow on one record, built to your credit policy rather than to somebody else's product roadmap.

Quick Match

Tell us the scenario. We will route it to a non-agency lending desk that writes this kind of file.