A closed-loop charger fault-and-repair corpus

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 and roaming reconciliation/refund records via OCPI (Hubject, Gireve); and OTA firmware update logs bracketed by before/after uptime.

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Sample

Brickroad customers

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Coverage

Universe, instruments and categories.

Industry: Application Software
Instruments: equities
Categories: AlternativeSentimentReference

Dataset card

Type: not stated
Format: Multimodal — JSON time-series (heartbeat/state stream, error codes, telemetry), ticket and work-order text records, chat and voice transcripts, and repair-proof photographs
Volume: 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.
Users: N/A — B2B software sold to charge-point operators, not a consumer product. Relevant population is CPO tenants and the chargers they connect; est. a small number of tenant networks given a Dubai-registered company with no recorded headcount.
History: 2 years
Update frequency: not stated
Growth: Active but early-stage — a full commercial surface is live (automations, industries, business directory, pricing, demo booking, bilingual English/Arabic) with a Gulf-market go-to-market, though no customer count or funding is disclosed.
Launched: ~2025-2026 — the company record was created in late 2025 and the agentic-AI framing sits in the 2025-2026 wave of LLM agent products
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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