Genesys vs Toloka

Genesys Cloud Agentic Virtual Agent is an autonomous AI agent that reasons through a customer's goal, plans actions and executes multi-step workflows across systems to resolve requests end to end without human intervention.

Contact-centre platform where AI usage is metered separately from seats: always-on virtual agents, copilots in the agent workspace, and an AI Studio for building and governing agents — with AI Experience tokens granted per organisation and per agent.

Genesys Cloud Knowledge Fabric unifies enterprise knowledge from multiple sources into an adaptive layer that powers AI agents with contextual search and generative-AI answers carrying source citations.

A hybrid AI-plus-human agent that takes a delegated task - research, data analysis, copywriting, design or development - has AI do the first pass, then routes it to one of 10,000+ vetted experts for verification and multi-layer QA, returning results in 2-24 hours.

An independent evaluation platform that ranks frontier LLMs on agentic tool-use tasks using private, non-contaminated benchmarks across industry domains, scored on a pass^5 reliability metric, with the underlying RL Gyms and evaluation datasets available to license.

A self-serve service that lowers per-request inference cost through two tools - fine-tuning LoRA adapters on frozen Qwen3 base models to replace a frontier API on a narrow task, and prompt gisting that compresses long instruction prefixes into learned tokens.

Toloka's catalogue of ready-made training datasets sold outright — three named at the time of writing (Tau-bench Dataset Extension, University-level Math Reasoning, Multimodal Conversations) — as distinct from the custom data work its Platform sells.

Human-in-the-loop data programs -- demonstrations, annotation and evaluation -- for training robotics and physical AI systems.

A self-serve platform where an AI agent turns a described data goal into a full human-annotation pipeline - RLHF and preference data, data collection, instruction tuning, model evaluation, synthetic-data validation and content-moderation QA - with LLM-based quality checks on the output.