Not a dashboard — an expert that keeps watching your data and tells you what’s happening and what to do about it. The agent plugs into your CRM, billing, product analytics and ERP, models your customers as states rather than rows, and turns every transition into an explanation, a recommendation and — when you approve — an action.
The agent connects to your operational systems — CRM, billing, product analytics, ERP — and analyses the business continuously through a 5×5 matrix: five dimensions (retention, activation, engagement, payments, cost) crossed with five methods (general analysis, correlations, risk ratio, segmentation, cohorts). Instead of producing flat reports, it builds a customer-state model — activation, engagement, expansion, churn risk, contraction, churn — and tracks transitions between those states in real time.
The shift is subtle but important. A dashboard shows you a metric on a date. The agent shows you which customers are moving in the wrong direction, why, and what tends to stop the movement when caught early. The data lake stays where it is; the difference is in what gets read from it and how often.
When a customer slips into a problem state, the agent doesn’t just notify someone. It explains the cause in business terms, finds similar cases in your own history, proposes a scenario to address it — and on approval runs the scenario through connected tools. It can send a CSM an email, create a task in your tracker, update a status in CRM, all attributed to the agent and reversible from the audit trail.
Underneath sits a hybrid architecture. Deterministic Python computes the metrics where precision matters — aggregations, ratios, statistical tests — and an LLM layer handles interpretation, narrative and adaptation to your specific business. Every conclusion the agent reaches is reproducible, linked back to its source data, and recorded in an audit log. No black-box judgement, no “the model said so”.
The capabilities that turn a stream of business data into a continuously updated picture — and into approved, attributed actions.
Six states tracked in real time — activation, engagement, expansion, churn risk, contraction, churn. Every transition is a signal, not a row in a report.
Five dimensions — retention, activation, engagement, payments, cost — crossed with five methods — general analysis, correlations, risk ratio, segmentation, cohorts.
CRM, billing, product analytics, ERP — whatever already runs the business. The agent reads, watches and acts back through the same connections.
Deterministic Python where precision matters — aggregations, ratios, tests — and an LLM layer for interpretation and narrative. Each side does the work it’s good at.
Explains the cause in business terms, finds similar past cases, proposes a scenario — and on approval emails the CSM, opens the task, updates the CRM. Reversible from the audit trail.
Every conclusion is linked back to source data and recorded in an audit log. No black-box judgement, no “the model said so” — your team can re-run, inspect, override.
Retention analysis in SaaS is the obvious fit, but the same engine works wherever value comes from understanding a customer trajectory rather than a snapshot. Contract-manufacturing health monitoring, activation funnels for low-frequency purchases — visa, legal services, real estate — and B2B contexts where the difference between a renewing and a leaving customer shows up in patterns three months before the renewal date.
The team that owns the agent stops asking “what happened last quarter” and starts asking “what is the agent doing about it right now”. The role of the analyst doesn’t go away — it shifts from running queries to reviewing the agent’s reasoning and approving the actions that matter.
The team will connect the agent to two of your data sources and run it in shadow mode for two weeks. You see what it would have caught — then decide whether to let it act.
