Measuring what
your chatbot
actually does
Jusnador started from a straightforward observation: businesses were deploying chatbots and then guessing whether they worked. We built the infrastructure to stop guessing — real data, accessible from anywhere in the country, for teams of any size.
A support team in Kharkiv had deployed a chatbot to handle incoming product questions. Three months in, their team lead, Oksana Verhun, noticed something odd: ticket volume had not dropped. The chatbot was running, sessions were being logged, but no one could tell which conversations ended well and which ones quietly failed. The data existed somewhere — it just wasn't visible to anyone who needed to act on it.
That situation turned out to be common. Across industries — retail, banking, logistics — teams were building conversational interfaces and then operating them blind. Performance reviews happened quarterly at best, based on rough session counts rather than anything that revealed where conversations broke down or why users dropped off at specific points.
Jusnador was built to close that gap. The goal was not to replace the chatbot platforms teams were already using — it was to sit alongside them and make the invisible visible. Intent accuracy, fallback frequency, conversation depth, resolution rate: these numbers were always there. We built the tools to surface them clearly, and to make them just as accessible to a team in Uzhhorod as to one in Kyiv.
What we track and why it matters
Every number below represents a real decision point — the kind that gets missed when monitoring is an afterthought.
The platform runs identically whether your team is in Lviv or Mariupol. No regional pricing tiers, no latency gaps, no features gated by location.
Jusnador connects to the chatbot infrastructure you already have. There is no migration, no rebuild — just a monitoring layer that reads what is already there.
Thresholds are set per-metric, not globally. A spike in fallback rate on one intent triggers its own alert — separate from everything else happening in the same session log.
Reports are structured so that a product manager and a developer can read the same document and each find what they need — without a translation layer between them.
The people behind the platform
Small teams tend to build more opinionated tools. Everyone here has spent time on the data side of a chatbot project — which means the product reflects what actually frustrates analysts, not what sounds good in a feature list.
Bohdan Yarema
Head of Analytics EngineeringBohdan spent several years building data pipelines for e-commerce chatbots before joining Jusnador. He is the reason the platform handles high-volume session logs without the query times that used to make live monitoring impractical.
Taras Holovatyi
Product LeadTaras came from a background in enterprise NLP tooling. His focus is on keeping the interface honest — making sure that what the dashboard shows matches what is actually happening in the conversation logs, with no flattering smoothing applied.
Iryna Savchuk
Client Integration SpecialistIryna manages the onboarding process for new clients, which in practice means translating between what an organisation's chatbot infrastructure looks like and what Jusnador needs to connect to it reliably. She has handled integrations across banking, retail, and municipal services.
The full team is distributed across Ukraine — which is deliberate. Understanding how chatbots perform in different regional contexts requires people who actually work in those contexts. Remote-first since the beginning, not as a policy, but as a practical decision that turned out to shape how we think about equal access to the platform itself.
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