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Small language models trained on your credit policy

Not a chatbot wrapper. Purpose-built small language models that run against your guidelines, your documents, and your data — with the answer traced back to the source.

Bespoke SLMs in detail

Grounded in your guidelines

Answers come from your credit policy, your matrices, and your documents — not from a general model recalling somebody else's overlay.

Every answer cites its source

Responses link back to the guideline section they came from, so a reviewer can confirm the answer instead of trusting it.

Human review on high-risk calls

Credit, compliance, and borrower-impacting decisions escalate to a person by default. The model drafts; it does not approve.

Your data stays yours

Models are deployed for your organisation. Your documents are not used to train a shared model.

Auditable by construction

Every prompt, retrieval, and response is logged, so you can reconstruct why the system said what it said.

Embedded where the work happens

Available inside LendLAB, LiteAPP, and your own tools through the API — not as another window to check.

What Bespoke SLMs talks to

Bespoke SLMs reads from and writes to the systems you already run. Each integration below has its own page describing exactly what moves in each direction.

Bespoke SLMs questions

If the answer you need is not here, ask us — we will give you a straight one.

Why a small model instead of a frontier model?

Because the task is narrow and the accuracy bar is specific. A model scoped to your guidelines is cheaper to run, faster to respond, easier to evaluate, and simpler to keep inside your data boundary. Where a larger model is genuinely better for a step, Qualr routes that step to one.

Can it make a credit decision?

No, and it is not designed to. It surfaces what your guidelines say and drafts the supporting work. Credit, compliance, and borrower-impacting decisions route to a human reviewer.

Is our data used to train anything shared?

No. Models are deployed per organisation and your documents stay within your deployment.

Quick Match

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