When Lena Sokolova first tried to automate customer support for her logistics company in 2019, the chatbot kept asking clients to repeat themselves. Drivers got frustrated. Support tickets piled up. The technology existed, but nobody explained how to make it work for real scenarios.
We started documenting what actually works after eight years of building voice systems for companies across Ukraine. These guides come from projects where we had to figure out why an assistant failed to understand accents, or why users abandoned conversations halfway through.
Intent recognition fails when training data doesn't match how real users phrase requests. We break down how to collect actual user inputs, label them correctly, and build models that recognize variations without constant retraining. Includes sample datasets from Ukrainian retail and banking projects.
Most chatbots lose context after three exchanges. Users repeat information, conversations stall, and frustration builds. This guide walks through state management, context tracking, and designing conversation flows that remember what was already discussed. Based on patterns we tested in appointment booking and technical support systems.
Standard speech recognition models struggle with regional pronunciation variations. We document how to fine-tune models for Ukrainian, Russian, and mixed-language environments, including handling code-switching and industry-specific terminology. Covers acoustic model customization and pronunciation dictionaries.
When users abandon conversations, logs rarely tell the full story. This guide covers setting up proper logging, analyzing conversation breakdowns, and identifying patterns in failed interactions. Includes tools for visualizing conversation flows and spotting where users get stuck.