Specialized proprietary models that act in real time, trained on your data. They are not LLMs and not wrappers around anyone else's model. Some decisions have a budget of milliseconds and a cost measured in real dollars per mistake; LLMs cannot make those decisions, and these models exist because something has to.
They come from somewhere specific. For ten years, Decide has run custom models that make advertising decisions on live budgets, where every wrong call costs actual dollars and a model has to learn fast because it is learning while the money is being spent. Decision Intelligence is the generalization and extension of that work: the same discipline, trained on your data, aimed at whatever decision your business turns on. More than 20 of these models are deployed in real-time environments today.
The mechanics · why not just use an LLM?
These are regression tasks. An LLM is the wrong instrument for them.
A regression task means predicting a number from facts on record: the probability this customer completes a purchase, the bid worth placing, the price that clears. The reasons a large language model is wrong for that job are mechanical, not a matter of taste.
The answer is a number, not a sentence
These decisions need a calibrated probability, meaning that when the model says 4%, about 4 in 100 such cases really do convert, because bids and prices are arithmetic on that number. An LLM is built to continue text plausibly, and study after study finds that the confidence figures LLMs state do not track how often they are right. In one benchmark, ranges the models were 99% sure of contained the true answer about 65% of the time.
The clock is ten milliseconds
An LLM composes its answer piece by piece, so even a short reply takes hundreds of milliseconds and real compute cost, every call. These decisions arrive thousands of times a second with a budget under ten milliseconds each and a per-decision cost near zero. A compact purpose-built model evaluates in a fraction of a millisecond.
The data is a table, not a document
Each decision reads structured fields: device, time, history, price. On data shaped like that, compact specialized models still beat large general-purpose ones in benchmark after benchmark, including against language models fine-tuned for the task. Language models trade away accuracy on tables for breadth this job does not need.
The model has to keep learning
Outcomes stream back constantly: which choices worked, which did not. A purpose-built model retrains on fresh results continuously, so it tracks a changing world. Steering an LLM's numeric behavior means slow, expensive fine-tuning that cannot keep pace with that feedback.
None of this makes LLMs bad. It makes them the right tool for open-ended work in language, which is exactly where Guardrail MCP earns its keep. For one decision made a million times against structured facts, a model trained to that decision wins on accuracy, calibration, speed, and cost.
In the wild · landing page routing
A/B testing is dead. A single winner is right on average and wrong one pageview at a time.
An A/B test works like this: you build two versions of a page, send half your visitors to each, count which version gets more signups, and from then on show the winner to everyone. Suppose version A is a detailed page and version B is a short one, and B wins, six signups per hundred visitors against five. Everyone now sees B. But B did not win with everyone. Visitors on a laptop, reading carefully, signed up more on the detailed page; visitors on a phone in a checkout line signed up more on the short one. The average buried that split, and every careful reader sent to B from now on is a small loss that repeats forever and never appears in any report, because the test is over.
Decision Intelligence never crowns one winner. On each pageview it reads what the moment shows, the device, the place, the time, how the visitor arrived, and serves the version that people in that situation actually convert on. The careful reader gets the detailed page. The phone visitor gets the short one.
That laptop-versus-phone split is one a person could spot and program by hand. The point of the model is everything past it. It weighs many signals at once, the device, the hour, the day of week, where the visitor came from, what similar visitors just did, and finds winning combinations nobody would think to test: maybe the short page wins on phones, except for phone visitors arriving from an in-depth review, who convert better on the detailed page after all. There are far too many combinations for a person to try, and the winners drift as products, seasons, and audiences change. The model checks them on every pageview and quietly re-learns as the answers move.
A pageview
one visitor, one moment
Decision API
reads the moment: device, place, time, engagement
Variation Anot this visitor
Variation Bnot this visitor
Variation Cbest fit for this visitor. Served.
<10 ms decision
No customer PII required
Client pages stay exactly as they are