AI inside the operator's perimeter, audit-ready by design.
Carrier-grade AI inside your network. Subscriber data stays sovereign.
Customer-relationship and network-operations workflows under your national data-protection regime, traffic-and-location confidentiality rules, and your retention directives. Lawful-interception data is handled by your dedicated LI system under judicial warrant — out of scope for deeplinq.
Why telecom needs a different AI posture
A telecom operator runs against a layered envelope no generic AI platform was built to fit. Your national data-protection regime scopes how subscriber data is processed — GDPR in the EU, PDPL in the UAE and Saudi Arabia, Loi 09-08 in Morocco, the nLPD in Switzerland, and equivalent frameworks elsewhere. Traffic-and-location data sits under a parallel regime — the e-Privacy Directive in the EU, with regional equivalents elsewhere. Retention directives, the national telecom regulator, and customer expectations stack on top.
Most AI platforms were built for a different customer — one accepting hyperscaler tenancy, vendor-hosted models, and a data path the regulator never reviewed. A subscriber summary without a citation back to billing, CRM, or NMS is not an answer a DPO can defend. Customer-data residency cannot move to a vendor cloud — not by policy, by architecture.
Two categories of telecom data sit in different worlds. Lawful-interception data — handled by your dedicated LI system, under judicial warrant, by your LI compliance team. Customer-relationship and network-operations data — billing, CRM, support, NMS, change-control. deeplinq operates exclusively in the second category. It does not touch the first.
Customer-care agents, grounded in subscriber state
A level-one or level-two agent juggles five to eight systems on every call — CRM, billing, network status, knowledge base, ticketing, contract repository. Average handle time runs long, first-call resolution stays low, and a generic AI assistant hallucinates the moment it runs out of subscriber context.
The platform plugs into the operator's BSS and OSS stack — CRM, billing, NMS, ticketing, knowledge base, contract repository — through one Connector Hub. Before : the level-one agent toggles five to eight systems on every call while average handle time runs long. After : plain-language queries — 'summarise the last three interactions', 'list active services and recent network events affecting this subscriber' — return cited answers inside the existing CRM screen, every retrieval RBAC-enforced.
Frameworks the workflow respects: your national data-protection regime; your traffic-and-location confidentiality rules; ISO 27001. The boundary stays explicit — deeplinq does not auto-resolve, does not auto-credit, does not modify the contract. Augmentation, not replacement.
Network operations, context surfaced during the incident
During a P1 or P2 incident, the level-two analyst spends an estimated 20-40 minutes reconstructing context — opening the NMS, pulling change-control history, searching ticketing for similar past incidents. MTTR drags while the analyst rebuilds context the operator already holds.
The platform reaches NMS, change-control, ticketing, and configuration repositories through one Connector Hub. Before : the analyst opens four tools in sequence and rebuilds context the operator already holds, while MTTR drags. After : the analyst asks once — "show changes on the affected node in the last 72 hours", "correlate the current alarm pattern with recent config events" — and receives a sourced answer cited back to NMS records, change tickets, and config entries.
Frameworks the workflow respects : ISO 27001 logging posture ; your internal change-management policy. The boundary is read-mostly — agents surface context, suggest correlations, log every retrieval. The analyst decides. Boundary details on /scope/.
Regulatory and DPO response, drafted from a unified subscriber view
A subject-access request lands under your national data-protection regime. The DPO has thirty days to produce a complete view of one subscriber's data, queried manually across six to ten systems. A regulator inquiry follows the same path.
The platform harmonises the subscriber view across BSS, OSS, support, and retention systems through one Connector Hub. Before : the DPO queries six to ten systems by hand against the thirty-day clock. After : structured response packages — subject-access bundle, regulator inquiry response, retention-compliance attestation, lawful-basis documentation — assemble with full source attribution. 'Produce the subject-access bundle for subscriber X' returns a cited draft.
Frameworks the workflow respects: subject-access and rectification rights under your national data-protection regime; your retention directives. The DPO reviews and signs off every package. deeplinq drafts; humans validate; signature is a human act.
Fraud-pattern enrichment for the analyst
A telecom fraud team works through hundreds of alerts daily — SIM-swap, international revenue share fraud, subscription fraud, account-takeover. Pattern enrichment across CDR, customer history, device data, and complaint records is manual, and the same pattern often surfaces twice across analysts who never compared notes.
The platform reaches CDR, customer history, complaint archive, and device data through one Connector Hub. Before : the fraud team works through hundreds of alerts daily — SIM-swap, IRSF, subscription fraud, account-takeover — with manual pattern enrichment that surfaces twice across analysts. After : agents correlate anomalous patterns — clusters of accounts sharing device signatures, unusual call-destination profiles — and surface them in the analyst's queue with sourced citations. The platform enriches ; the analyst decides.
Frameworks the workflow respects: lawful-basis rules under your national data-protection regime — typically legitimate interest for fraud prevention; your retention rules on CDR. The boundary is pattern-surfacing only. Your fraud-detection system remains authoritative; the analyst remains the signer.
What the regulator and the DPO receive
A regulator inquiry or a DPO audit is not passed on claims. It is passed on evidence. deeplinq treats the evidence layer as a first-class platform concern. Every prompt and response is archived with full context. Every retrieval — record, field, document, recorded-call reference — is attributed to its source. Every model call is logged with identifier, parameters, timestamp, and outcome. Every agent action carries the decision trace, the RBAC evaluation, and the retention parameters that applied.
Model-version pinning is the structural backbone — the model that produced an output during one audit cycle reconstructs the reasoning during the next, and silent substitution is eliminated by architecture. Reporting templates, retention parameters, and lawful-basis annotations stay versioned alongside the interactions they cover. When the operator's compliance function exports an evidence bundle, the export is structured — not a reconstruction project.
deeplinq does not certify telecom regulator compliance — no platform can. Compliance is a property of the operator and of the operator's existing certifications. What deeplinq produces is the evidence trail those functions expect to see, with the integrity posture they expect to preserve.
Deployment inside the operator's perimeter
Telecom residency is not one question. It is several the data itself asks. Customer-relationship data shaped by your national data-protection regime. CDR sitting under specific retention rules. Recorded-call archives under your traffic-and-location confidentiality regime. The deployment topology has to fit each, not the reverse.
deeplinq supports four deployment modes — on-premise inside the operator's data centre; customer-tenanted private cloud (VPC) on AWS, Azure, or Google Cloud; a regional sovereign cloud aligned with the operator's residency obligations; deeplinq-managed cloud where the operator's compliance posture allows it. Telecom-specific drivers shape the choice: CDR residency, customer-data sensitivity, retention boundaries, and the operational separation from the lawful-access framework that sits outside this scope.
Model choice is held behind an interface the operator controls. Cloud APIs for non-sensitive operational queries; open-weights for customer-data and CDR workloads. The full model-agnosticism posture — supported providers, residency contracts, version pinning — is detailed on /banking-regulated and applies identically here.
deeplinq is built on a four-layer architecture designed for sovereignty, auditability, and integration.
Start a conversation. Not a sales process.
A working session with our team on your regulatory envelope, your deployment constraints, and the customer-care, network-operations, regulatory-DPO, or fraud-pattern workflow where the evidence posture matters most. Bring your DPO. We'll bring the architecture.