The most flexible 100% web, cloud and NoCode decision engine

Because credit policies are designed with a drag-and-drop interface and formulas whose syntax resembles spreadsheets, which lets risk teams create and modify decision flows without writing code or depending on IT.

uFlow runs its API on managed cloud services, which removes the need to maintain your own infrastructure and delivers higher scalability and availability.

Through its REST API, documented with Swagger and secured with JWT token authentication, uFlow connects quickly and safely with any core system, onboarding flow or frontend.

Yes. You can deploy AI models trained in Python or R directly inside your decision flows, without maintaining your own environments.

Yes. Every decision records the input, the rules applied, the outcome and its reasons. That evidence lets you justify a rejection to the customer and reconstruct the decision for a regulator.

Yes. You can backtest each change against historical data in testing environments before moving it to production, so you change policies without surprises and with instant rollback to a previous version.

The engine lets you track your policies' and models' performance with real-time reports and metrics, including drift signals, so you can catch when a model loses accuracy and adjust it in time.

Yes. uFlow integrates through a REST API: your system sends the applicant's data and the engine returns the decision with its detail —rules applied, data queried and outcome— ready to use in your core system, your onboarding or your app. The technical reference is in uFlow's documentation.

Yes. With champion/challenger, uFlow runs two strategies in parallel on live traffic and compares which performs better before replacing the current one. It is how you improve a credit policy with evidence and without risking the whole portfolio.

Yes. Beyond real-time decisions via API, uFlow can process large volumes in batch: for example, re-evaluating an entire portfolio or running a new policy over a file of applications.

Yes. uFlow includes a Python node for advanced logic, transformations or custom calculations within the same decision flow, without leaving the engine or maintaining separate servers.

No. uFlow runs and governs models, it doesn't train them: your data scientists keep training where they already work (their notebook, Python or R, whichever cloud they use) and uFlow puts that model to work deciding in production. It's a design choice: it doesn't force you to migrate your data science, and it keeps model development separate from model execution and control, which is what sound model risk management asks for.

Yes. You take your model from notebook to production without rewriting it and without maintaining your own Python or R environments. Once inside, it is versioned, runs within the policy alongside your rules, is monitored with performance metrics and drift alerts, and every decision it produces is traced and explainable.

Inside: each policy has a dashboard showing transaction volume, approval and rejection rates and the top rejection reasons, without exporting or processing data elsewhere. On top of that: simulating changes against historical data (backtesting), champion/challenger, bulk batch re-scoring, performance monitoring with drift alerts, and the reasoning behind every decision. Outside: custom model training and deep exploratory analytics —ad hoc cuts, cohorts, BI dashboards— which stay in your team’s own tools. uFlow is the decision layer and its monitoring, not a replacement for your data science stack.

Yes. Each policy has its own dashboard with total transactions, how many were approved and how many rejected, with their rates. The panel reflects the current day’s transactions (excluding the last hour), designed to catch a deviation early after a policy change.

The dashboard groups rejections by reason and shows the main ones with their number of occurrences. That tells you whether a single criterion concentrates most rejections —a regulatory validation or a debt rule, for example— so you can decide with data whether the policy is well calibrated or is turning away good customers.

Not for operational monitoring: volume, approval and rejection rates and the main reasons are visible inside the platform, with no exporting or outside processing. For deep exploratory analysis or model training you keep using your analytics team’s own tools.

Yes. It is configured once per policy: you indicate which variable holds the approval status, which value represents an approved case, and which variable stores the rejection reason. Nothing has to be renamed in your flows — the panel adapts to your naming, not the other way around.

Yes, as an orchestration and decision layer. uFlow doesn't verify biometrics or detect deepfakes by itself —your identity and anti-fraud providers do that— but it coordinates them: queries several in parallel, crosses their signals with your rules, geolocation and models, and decides in milliseconds to approve, reject or refer to manual review, with a trace of every decision.

Yes. The decision nodes route each case into three lanes: automatic approval, automatic rejection and manual review. The gray band —neither clearly legitimate nor clearly fraudulent— goes to review with all the evidence ready for the analyst, so your team only looks at the cases that warrant it.