At nine on Monday, a reply tells a customer to pay the postage on a faulty kettle, and your support lead corrects it: faulty goods come back free. At noon, refunds over £75 start needing a manager’s approval. By the afternoon, the morning’s questions are back in new words, and something arrives that nobody has seen before. On Friday, a newer model comes out.
With a model on its own, the week ends where it began. By Friday evening your AI knows exactly what it knew at nine on Monday, and your team has spent five days teaching it things it couldn’t learn.
ACI, Adaptive Continual Intelligence, turns each of those moments into a lesson. It learns from the results of your work while the work happens and acts on what it learns, so by Friday your AI is better at your work than it was on Monday.
Monday, nine o’clock: your team corrects a reply, and the next one like it has the fix.
A correction becomes the next answer.
When your support lead corrects the kettle reply, ACI learns from the correction, and the very next customer who asks gets the fix, however they phrase it. Nobody edits a prompt, updates a document or waits for a training run.
Today, a correction lives in the conversation where it was made, and ends with it. Teams that want it to last write it into a prompt, which grows with every exception until nobody is sure which lines still apply.
With ACI, the people who know the work teach your AI by doing it. You can see who corrected what, and when, and each correction stands until someone on your team changes it.
Over a stream of 3,000 decisions in which the rules changed five times, the model alone was right 59.8% of the time. Put Duplex, which is built on ACI, beside the same model, and the two together were right 94.6% of the time.
The work changes, and your AI keeps up.
At noon, the new rule on refunds over £75 arrives. ACI follows it from the next request, and everything else it learned stays as it was: the kettle correction still applies, and so does every decision your team settled last month.
A rule change usually starts a hunt. The old rule is written into prompts, documents and cached answers, and every copy someone misses goes on giving the old answer, confidently, until a customer notices. With ACI, a rule change is a decision your team makes once, rather than a clean-up that takes all week.
The rule changes in those 3,000 decisions are where the approaches parted. On the decisions they affected, Duplex was right 92.5% of the time. A cache managed 42.6%, and the model alone 2.3%.
The more it learns, the less each request costs.
By three, the same questions are coming back again and again. ACI answers each one it has learned in milliseconds, with no model call, and your model takes what is new, where it does its best work.
Left to itself, a model works every request out from scratch, so the thousandth refund question costs as much as the first, and takes as long. The bill grows with use, and so does the wait.
With ACI, cost runs the other way: the more of your work it learns, the fewer calls your model gets. Over the same 3,000 decisions, Duplex cut model calls by 90.3%, cost by 84.8% and the time each answer took by 81.9%, while accuracy went up. An AI that learns gets cheaper as it gets better.
It knows what it knows.
At half past four, a request arrives that looks like the morning’s refunds and isn’t: an order split in two, one half delivered and the other lost. ACI recognises that it hasn’t learned this case, and hands it to your model, briefed with what ACI knows about it.
A cache would see a refund like the others and return the answer it already has, right or wrong. The model by itself answers anything, with the same confidence whether it knows your rules or not. Either way, the mistakes arrive looking exactly like the right answers.
Knowing where its knowledge ends is what lets you trust ACI with more of the work. It answers what it has learned and brings in your model for what is new, rather than guessing.
The tests used misleading look-alikes: requests that look like familiar work but are new. Across ten runs, Duplex was wrong on 7.5% of them, against 45.3% for a cache.
What it learns goes where you go.
On Friday, your team switches to the newer model. Everything ACI learned this week comes along: the kettle correction, the approval rule and every decision your team settled, so the new model starts its first morning with your work already beside it.
Teaching that lives inside a model leaves with it. A fine-tuned adapter belongs to the model it was trained on, so a new model means another training run, and the week’s lessons have to be taught again. In the tests, what Duplex learned moved to another model family with no retraining, while not one of six fine-tuned adapters could make the move.
It goes beyond models, too. Any assistant or agent you allow can use what ACI learned, so a correction made in your support assistant also helps the agent that drafts your refund emails.
That was one week. A model on its own begins every Monday as it was released, no wiser about your work than on its first day. With ACI, each week starts where the last one ended, and the weeks add up.