Telecom. Six workloads, one endpoint.
Six telecom AI workloads — network assurance and root-cause analysis, experience-driven churn prediction, revenue assurance and the field-operations copilot among them — run on one OriginChainDB deployment across mobile operators, ISPs and tower companies. Each needs more than one data model at once, which is exactly why they stall on a stitched stack.
Mobile network operators, ISPs, MVNOs and tower companies with 5M+ subscribers, running 5G and fibre build-outs against compressing margins.
The forcing function.
Operators face sustained margin compression, making AI an operational lever rather than a strategic aspiration. Customers are up to five times more likely to churn after a poor network moment — which means the churn model and the network telemetry have to live in the same query.
The network-and-revenue graph — root cause walks the dependency chain; SIM-box and dealer fraud walk the same edges.
is the lever AI moves first — but only when telemetry and the operational record sit in the same query
the two lines AI is bought to move in telecom — both need network truth joined to customer truth
more likely to churn after a poor network moment — McKinsey
Six workloads, five query shapes.
Five of these six workloads need SQL, five need vector search and five need graph traversal; three need /ask and one needs full-text — which is why single-model databases deliver them only with a second system and a sync problem.
Alarms, KPIs, topology and change history in one store — graph traversal walks the dependency chain while vector similarity matches the incident to prior resolved cases.
Faster fault detection and resolution; fewer customer-visible incidents per site.
Join live network-experience signals to billing, care and behavioural data so retention fires on the actual degradation event, not a month-end score.
Retention offers triggered inside the window where they still work.
SIM-box, IRSF, subscription and dealer fraud modelled as graphs over CDRs, devices, KYC records and dealer chains.
Recovered revenue leakage — historically one of the fastest AI paybacks in telecom.
Semantic retrieval across method statements, vendor manuals, ticket history and site records, answerable from a field device in plain language.
Higher first-time-fix rate and shorter mean time to repair.
Micro-segment on usage embeddings and social/referral graph position, joined live with entitlement and inventory to construct the offer.
ARPU uplift per campaign at lower discount intensity.
Contract terms, SLA history, circuits and escalation paths modelled together — answer enterprise account questions without a war room.
SLA credits reduced; enterprise renewal risk visible early.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in Telecom.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
Network truth and customer truth in the same query — the only configuration in which experience-driven retention actually fires in time.