Frequently Asked Questions

A KPI system — a KPI management system, a KPI tracking system, or simply KPI governance — is an environment where metrics are calculated on one methodology, update automatically from company data, and are available to every level of management as reports and dashboards.

The platform takes on the whole training cycle: assigning courses by position or unit, deadline reminders, assessment, recording results and issuing certificates. Instead of a paper register and spreadsheets, the institution gets a single source of truth for who completed what, when and with what result.

Reporting is automated separately: analytics is produced by unit, grade of position and period, so preparing a report for management stops being a job that takes days.

Calculating metrics by hand consumes analyst time and introduces the risk of error with every new report. KPI automation frees the team from that routine and guarantees that everyone is looking at the same numbers.

Yes. The structure of the institution is carried into the platform as a hierarchy of course categories and user groups: central office, regional bodies, directorates and departments. Each unit gets its own set of courses and its own training officer.

For organisations with a regional network this is the main scenario: one LMS serves both the central office and the regional units. A local manager sees only their own team, while the central office sees the consolidated picture.

A dashboard refreshes automatically and removes the discrepancies between files kept by different departments. Instead of hunting for the current version of a spreadsheet, a manager opens one dashboard with trustworthy data.

Yes. Professional development for civil servants is built as a programme of several courses with their own deadlines, a pass mark and a final assessment. The platform records attempts, scores and the completion date, and issues a certificate.

Assessment is configured separately from the learning materials: a question bank, random selection, limits on time and attempts. A learning history is kept for every employee and is available to the responsible officer.

Practically any metric that has a data source behind it: financial, operational, sales, production or customer service indicators. We define the list together with you during discovery.

Yes, and this is one of the reasons Moodle is chosen for the public sector. The standard roles — administrator, instructional designer, manager, learner — are extended with custom ones that match real positions and areas of responsibility in the institution.

Rights are granted not “to the system” but to a specific context: a course category, a single course or a group. A training officer in a regional unit therefore works only with their own users.

Connections are made through ready-made connectors to common ERP and CRM systems. Data reaches the KPI system automatically, and if the company already runs Power BI, the dashboards integrate without losing existing work.

Yes. Integration is done through the platform's REST API, webhooks or a middleware service. The most common scenarios are single sign-on through a corporate account (Active Directory, LDAP, SSO) and synchronising users and the structure of units from the HR system.

SCORM, xAPI and LTI cover external learning content. The exact list of integrations is defined during requirements analysis, depending on the systems the institution already runs.

KPIs are measurable indicators of current performance, while OKRs are a method for setting ambitious goals with key results. The two are not mutually exclusive: OKRs often rest on the same data as a KPI system.

Yes. Courses, learning materials, the user base and completion history are all migrated. Where materials currently exist as documents, presentations and video, they are assembled into structured courses; where they sit in another LMS, export in standard formats is used.

Migration is run as a managed project: first a test transfer on a copy, then a data reconciliation, and only after that the launch in production. A rollback plan is prepared in advance.

Out of the box you get reports on course progress and completion, assessment results, user activity, the gradebook and action logs. They can be filtered by unit, group, course and period, and exported to a file.

Where the institution needs a report format the core does not provide, we develop a custom one — for example a summary of mandatory programme completion broken down by regional unit.

Yes. Moodle support runs under an SLA and covers core and plugin updates including security patches, performance monitoring, incident response and consultations for the institution's administrators.

Platform development is handled separately: new courses and units, additional integrations and reports. The corporate training system we modernised for a leading Ukrainian bank, for example, stayed under our support after launch.

An AI solution for business is a system that learns from a company's data and takes on a decision or an action that previously required a person: forecasting demand, scoring the risk of customer churn, classifying requests, choosing a recommendation, finding an answer inside corporate documents.

Artificial intelligence for business differs from ordinary automation in that it works from a pattern derived from historical data rather than from a rule written out in advance.

AI automation pays off fastest on repetitive operations that do not reduce to a simple rule: processing and checking documents, classifying and routing requests, preparing and normalising data, drafting routine texts.

Where a process is described by clear conditions, classic business process automation is cheaper and more reliable. We separate the two layers: AI takes on exactly the step that requires "understanding" a text, a document or a behaviour.

AI data analytics applies where the volume of information exceeds what can be reviewed by hand: finding patterns in transactions, behavioural clustering of the customer base, anomaly detection, testing business hypotheses against historical data.

On the Planeta Kino project, ML algorithms broke the base into behavioural clusters using more than a year of transactions, and the results were validated together with the marketing team through in-depth interviews.

Predictive analytics forecasts future events from historical data: demand, sales, capacity, stock, the probability of customer churn. Unlike ordinary reporting, which describes the past, forecasting analytics buys you time to act.

A practical example: for New Products Group an ensemble ML model forecasts production volumes with over 80% accuracy. The company moved from reacting to shortages or surpluses after the fact to planning volumes in advance.

Yes, and this is what makes a solution work in practice. AI integration runs through REST API, message queues, webhooks or a direct connection to the data warehouse — whichever suits your infrastructure.

An example from our work: at Planeta Kino the churn risk score is passed to Salesforce Marketing Cloud, and the customer receives a message matched to their risk level. Forecasts can reach an ERP for production planning or BI reporting in the same way.

Yes — developing AI solutions around a company's specific processes is our main format of work. Custom AI solutions are needed when an off-the-shelf service does not account for the specifics of the business.

A telling case is goodwine: the client planned to use a ready-made recommendation service, but testing showed the model did not take warehouse stock into account. Our own model, combining behavioural and logistics factors, delivered +5% average order value and +10% stock rotation.