Vidurion · Your rules. Your customers. Your model.
We train a super-specialised model on your rules and your customers, prove it against the AI you use today, and run it inside your rules. Six weeks from first call to live traffic. You own the result.
Plus a report card your risk team can sign and the same guardrails enforced live by design.
Past conversations help but aren’t required. No customer data is needed to train.
You can stop at the proof if it doesn’t beat what you use today.
All-in. No token bills. Sized to your volume, not to tokens.
Every practice conversation has a counterpart who holds a fact back: a real budget, a real worry, the actual cause of the problem. The model has a few exchanges to find it and earn a yes without breaking a rule. It plays about twenty thousand of these before you see it.
Who talks, what may be proposed, the rules that can never be broken, and what a good outcome looks like. We write it from documents you already have: policies, scripts, product or service terms.
Simulated customers are built from your segments, not from individuals. Each carries facts the model has to uncover. Thousands of conversations are played before anyone real is involved.
A super-specialised model you own learns from the conversations that earned a yes inside the rules. A breach costs the full reward, so staying inside the rules is learned, not bolted on afterwards.
The model is scored on customers it never practised with, by a judge from a different model family. If the report card passes, it goes live beside your current setup, with every reply checked against the same rules.
A short walk through the lab. The screens are real; the run is a small sample. The parts that make the models good stay in the lab.
Sample run with stand-in models. Numbers on screen are not results.
The same rules the model trained against run on every live response. A breach never leaves the system. The customer gets a fallback answer from your current setup or a hand-off to a person.
Each blocked response is stored with the rule it broke, the customer profile it saw, and what it tried to say. Your compliance or quality team can review any of them, any time.
Shadow, a share of traffic, or all of it, changed from the console. Set the model to zero and your previous setup is back instantly. Nothing about your systems changes.
If real conversations start looking unlike the ones it practised on, or blocks rise, you get an alert and a proposed refresh run. Nothing retrains without your approval.
The policies, scripts and limits your people must follow: what must be said, what can never be said, what needs approval. Each becomes a check the model can’t get past.
The products, plans, remedies or services the model may propose, with the terms a customer needs to hear.
Segments, not individuals: who they are, what they typically want, what they typically hold back. We build simulated customers from this. No customer data is needed to train.
Ten good, ten bad, labelled by your best people. This is how we check our judges agree with your standards before anything trains.
The people who do this best spend about a week with us at the start: judging example conversations, settling what good looks like, reviewing the first practice runs. After that, a short review each week.
If you have them with outcomes, they warm-start the model and sharpen the judges. If you don’t, we start from the rulebook.
The team drafted first responses with a frontier model prompted with its rulebook. It was capable but generic: it rarely asked the question that mattered and answered worries with disclaimers. Here is what the same conversations look like after six weeks.
Your rulebook becomes checks. Your segments become simulated customers.
Our judges matched your super-performers on 18 of 20 examples. Report card against your current model.
Warm start, then about 20,000 practice conversations. One GPU, about a day.
Held-out customers, independent judge, five release gates. Your risk team gets the pack.
Runs beside your current setup first, then 10% of traffic, then more when you say so.
Illustrative scenario built from our proof-of-concept plan. Figures are design targets and will be replaced with measured results from the first founding engagement.
Scored by a judge from a different model family than the one that trained it, with every rule’s breach rate.
Which base model, which teacher, which judges, which rules, which customer segments. Only open-weight models generate training data.
Train and serve in your cloud if you choose. Transcripts are encrypted per tenant. No customer data is needed to train.
Every live answer logged with its rule checks. Traffic share set by you. Off in one click, with your previous setup back instantly.
Tell us the workflow you have in mind. We reply within two working days.