Operators are not short of data — they are short of data they can trust and act on. The SCIKIQ Telecom platform connects BSS, OSS, CDR/xDR and network data once, governs it once, and serves it to every use case and every AI agent.
Churn, fraud, 5G monetisation and cost-to-serve all depend on one question: do billing, network and customer data agree? This is the layer that makes the value chain investable — and AI safe to scale.
Read each card top to bottom: the problem an operator executive owns, the platform capability that removes it, and the SCIKIQ services that put it to work. Most open a working accelerator on demo data.
Usage is checked against billing by sampling, weeks after the bill run.
SIM-swap takeovers, SIM boxes, IRSF and Wangiri surface in separate tools, days apart.
Recharge, usage, network experience and complaints sit in different systems.
Agents cannot see the usage, rating and payment history behind a charge.
“Active subscriber” means something different in billing, CRM and the warehouse.
Network KPIs are planned per site; revenue and experience are reported per customer.
Partner files are settled on trust; IRSF hides inside legitimate traffic.
Lineage and access evidence are rebuilt by hand before every submission.
Models impress in a demo, then fail privacy review or drift on dirty feeds.
The same four steps sit under every capability above, every accelerator and every engagement. Week ranges are indicative for a first domain. See how a services engagement runs on it.
Pre-built connectors for billing, charging, CRM, mediation, OSS and partner files — batch, streaming and change-data capture.
Quality rules, CDR deduplication, identity resolution across MSISDN, IMSI, SIM and account — one golden subscriber record.
Telecom data models, catalogue, lineage and privacy policy, so “subscriber”, “revenue” and “site” mean the same everywhere.
Governed data products for dashboards, APIs, models and supervised AI agents — including natural-language questions for business users.
Agents for anomaly detection, churn, fraud scoring and network optimisation run on the governed fabric — with people supervising every action that changes a customer, a bill or a network element. Meet the digital workforce.
AI-native, zero-code and cloud-agnostic — deployed on Azure, AWS or GCP with infrastructure as code. These six product layers implement the twelve-layer target architecture — the strip under each shows which of the twelve it covers.
Connectors for BSS, OSS, CDR, ERP, cloud and legacy systems; batch and streaming; AI-assisted schema mapping.
RBAC, automated quality checks, sensitivity tagging, raw-to-report lineage and semantic models for business KPIs.
Standardisation, MDM, deduplication and GenAI-driven enrichment, in real time or on schedule.
Curated datasets turned into reusable, governed data products with generated APIs, dashboards and access rules.
Internal and partner discovery of data products, with API access, usage controls and support for monetisation.
GenAI NLQ studio, ML studio, AI agents for supervised workflows and BI connectors (Power BI, Tableau, Looker).
The same six frictions show up in almost every operator we meet. Targets are design goals agreed per engagement, not guarantees.
Data and tooling duplicated across hybrid data-warehouse platforms.
Batch-based integration across many systems leaves data late and unavailable.
No disaster recovery or high availability — high business impact if data is lost.
Duplicated ETL/BI tools, databases, appliances, licences and run teams.
Many technologies needing scarce, expensive skills in non-standard environments.
Quality issues across systems and limited headroom for growing CDR volumes.