Grow revenue from the customers you already have

Obviable learns how each of your customers really uses your product, then predicts which accounts are at risk of leaving and which are ready to buy more, weeks before it shows up in your revenue. For every alert you get a plain-language reason and a recommended next step, delivered right inside the tools your team already uses. Think of it as an early-warning and growth-radar system for your customer base.

It is built for subscription software (Business-to-Business SaaS) companies that want to keep more customers and grow the ones they have, typically serving Heads of Customer Success, Revenue Operations / Chief Revenue Officers, and CEOs. It fits best when you have enough usage history to learn from and more accounts than your team can watch closely by hand. If you bill by usage (pay-as-you-go) rather than fixed seats, it still works, the signals just come from how much customers actually use the product.

Every run you get four things: a health score for every account (low / medium / high), an artificial-intelligence (AI) memo on each alert explaining what is happening and what to do, a metrics table covering all accounts, and a weekly digest. None of it lives in a new dashboard you have to learn; the scores, reasons and next steps land directly in Slack, Salesforce, HubSpot or wherever your team already works. So it adds clarity to your existing workflow rather than another tool to log into.

You always get the why and the what, never just a score. A statistical model flags the risk early, and an AI layer writes a short memo in plain language: the main driver, the context, and a recommended action your team can take before the call. That is the difference between an alert that creates work and one that saves it.

Generic Customer Success and CRM platforms run the same template on every company and still need you to configure and maintain them; our edge is going deep on your specific usage data to build signals tuned to how churn and growth actually work for your product. Building it in-house means hiring data scientists and pulling focus from your roadmap, and a tool like ChatGPT can summarize text but cannot reliably predict churn on your historical data. We do the whole thing for you, end to end, and prove the value before you pay for the run.

Before any deployment, we run the engine on your own past data and measure the return: how many at-risk accounts it would have caught early and what that is worth to you. You see that proof first, then decide whether to pay for the ongoing run. We would rather show the value on your numbers than ask you to take headline metrics on faith.

It is done-for-you: we take your raw usage, billing, support and CRM data and handle the cleaning, preparation and integration ourselves, so there is no integration project sitting on your engineers. Your involvement is light, mainly granting access and a few alignment touchpoints, and you do not need pristine data to start because checking and improving data quality is part of what we do. The whole point is that this does not become another stalled internal project.

Yes. Each client gets one fully isolated instance, so your data is never pooled or mixed with anyone else's, and we are based in Europe with General Data Protection Regulation (GDPR) practices in mind, including signing a Data Processing Agreement. We only need the data required to make predictions and can work with pseudonymized identifiers where personal details are not necessary.

Pricing is a one-time setup of around 9,000 EUR plus a yearly run of around 10,000 EUR for weekly deliveries, with the scope included up front and no surprise add-ons. You are billed by your number of customers, not per seat, so the cost scales with your business in a predictable way.

You are typically live in under about 12 to 14 weeks, with a concrete output at every phase rather than a long wait for a big reveal. And it is not only about churn: the same engine flags quiet accounts ready to expand or upgrade, so it supports your net retention, not just damage control.

Obviable is built by a France-based team that combines machine-learning engineering with hands-on customer retention experience, co-founded by Sonia Bouanane and Simon Grah. The approach reflects years of turning messy behavioral data into reliable, automated retention workflows.