An Autonomous Commercial Agent runs on machine learning pointed at one business objective. Nobody writes the sequence. It reads how your customers behave, decides who to contact and who to leave alone, picks the hour, the channel and the argument, executes. Then it measures itself against your KPI and changes its own approach.
Autonomy is not where an agent starts. It is where an agent arrives. An implementation puts a conversational agent in front of your customers, following flows your team designed. Every conversation it holds becomes signal. When there is enough of it, our Data Science team trains a model on your objective, and the agent stops following the flow and starts deciding it.
Medra builds and operates the conversational agent on WhatsApp, voice and email. It follows the treatment your team defined: who gets contacted, in what order, with which message. It resolves, escalates when it must, and every exchange is logged with its outcome.
A script is written once and executed forever. A model is trained once and corrected forever. The difference is the loop: the agent acts, watches what happened, and retrains on it. Your KPI is the only thing that never moves.
Churn is visible before it happens. Orders thin out, replies slow down, a support ticket stays open. A predictive churn model scores that risk for every customer, every day, and the agent treats the ones it can still recover, before they leave. It decides the hour, the channel and the kind of message. Nobody schedules a campaign.
Customers by churn probability · the agent works the right of the line, and leaves the left alone
A fixed rule performs the same on day one and day three hundred. A model does not. Each retraining cycle folds the last outcomes back into the next decisions: which customers responded, at what hour, to which argument. The curve is the point: not where it starts, but that it keeps moving.
Customers retained for every 100 at-risk customers treated, per retraining cycle. The rule-based line is your starting point: the flows a team writes by hand. The agent line is the same objective, decided by the model.
The engine does not care what the goal is, only that it can be measured. Give it a KPI and a history, and it will learn which decisions move it. These are the objectives we train on most.
The model scores every account for the risk of leaving and the chance of recovery. The agent treats the ones worth treating, before the last order becomes the last one, and leaves alone the ones that only need to be left alone.
It learns which lapsed customers still respond, and to what, and it ignores the ones that stopped answering long ago.
It orders the portfolio by probability of payment, not by amount or age, and picks the offer each debtor is most likely to accept.
It finds the product each customer is most likely to add, and the moment the account is most open to hearing about it.
It scores new leads by likelihood of converting and chooses the contact that gets them across the line, not the one the funnel prescribes.
This is not a switch. It is a project. An autonomous agent is trained on your data, validated against your baseline and released in stages by our Data Science team and yours, together. It takes weeks, not days, and the calendar depends on how much history your data already holds.
We measure what the current flows achieve, and agree the KPI the model will be judged on.
Your history is joined with the conversation logs. Data Science designs the features and trains the first models.
The model decides in parallel while the agent still follows the old flow. We compare, with a control group.
The agent starts deciding inside guardrails. Your team reviews the decisions weekly and can override any of them.
Retraining on every cycle. Monitoring for drift. The guardrails move only when your team moves them.
If your history is thin, the model will be too, and we will say so before training it. If the agent does not beat the baseline in shadow mode, it does not go live. Autonomy is granted by the numbers, not by the calendar.
Bring one objective and the data behind it. We will tell you what the model can learn from it, and what it cannot yet.
