Gets better with use
What is System of Learning? The part of Kinnow that gets better with use. Agents adjust from the approvals and corrections your people make, agreed definitions of your numbers act as the reference, and each role gets screens that adapt to it.
In plain words
It means Kinnow adjusts to how your company works, using what your people approve, reject and correct. It does not mean an agent quietly rewrites its own rules. Every draft still waits for a person.
Example
In a month an agent drafts 40 bank matches. Your accountant approves 34 and rejects 6. Each answer is compared with how sure the agent said it was. Once there are enough examples (by default 30, looked at over the last 90 days), Kinnow checks whether drafts marked "90% sure" were right about 90% of the time. If they were not, Kinnow shows that agent's drafts less prominently until it has earned more trust on your books. The numbers here are made up.
Why it matters
- Cost. Fewer repeat corrections of the same kind, and less time on drafts that are routinely right.
- Risk. An agent that sounds sure and is often wrong is worse than one that says it is unsure.
- Control. Trust is measured on your own approvals, not claimed in a brochure.
How Kinnow handles it
Approvals and rejections are recorded against each agent's stated confidence; see AI confidence that means something. Figures you have confirmed become one agreed set of definitions for your numbers (semantic layer). Each role gets screens that fit its work.
The limits: learning here means feedback and measured confidence. Safeguards such as testing a change against your own known-good answers before it goes live are rolling out (Kinnow learns your business, safely). Agents are set up and checked with each client.
Ask us to show the approval history of an agent on a sample set of bank lines.
In Kinnow
Want to see how your corrections would feed back? Ask for a demo with your own examples.
Questions
Does Kinnow train an AI model on my data?
Learning here is feedback on your own approvals and corrections. Your data is not used to train outside AI models.
How long before I see a difference?
There is no fixed time. Confidence is checked once there are enough examples (30 by default within the last 90 days), so an agent you use daily gets there sooner than one you use once a month.
Can an agent learn to approve things by itself?
No. Nothing is filed, paid or posted without a person. That stays true however well an agent has done so far.