Converiqo

Converiqo AI — agentic AI uptime orchestration for EV charging networks (CPO AI) — A closed-loop charger fault-and-repair corpus: continuous OCPP heartbeat and state-change streams with a triage label separating network timeouts from genuine hardware failures; auto-generated repair tickets carrying error code, diagnosis, dispatch, parts consumed, first-time-fix outcome and photo proof; usage- and temperature-driven component-wear predictions (contactors, cables); driver support chat/voice transcripts where the agent issued a remote command and the session resolved; CDR validation exceptions a…

Converiqo offers (Alternative, Sentiment, Reference) — Request further research — no charger, ticket or transcript counts are published and the company record shows no headcount, so deployed volume is unknown. Structurally each managed charger emits continuous heartbeat records plus discrete fault-ticket episodes, so volume scales linearly with chargers under management; that count is the qualification question rather than something estimable from public sources..

Training charger fault-diagnosis models that map error codes to confirmed root cause (raw OCPP logs give the code but never the answer); predictive-maintenance datasets for contactors, cables and connectors with actual-failure ground truth; field-service dispatch and parts-forecasting optimisation; RLHF / agent evaluation corpora from driver-support dialogues where the remote command actually fixed the session — i.e. tool-use traces with verified outcomes rather than preference guesses; firmware regression detection from OTA-before/after uptime; charging-network SLA benchmarking; billing-dispute and CDR-anomaly detection training.

The data is with 2 years of history.

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