Smarter Networks, Faster Ops, Happier Customers — With Evidence
AI Telecom applies ML/LLMs to build, run, and secure carrier & enterprise networks—RAN/core/transport, SD-WAN/SASE, OSS/BSS, and CX—while keeping latency low, tickets down, fraud out, and auditors satisfied.
SolveForce delivers AI for telecom as a system: governed data → low-latency edge/cores → ML services (forecast/anomaly/optimization) → guarded RAG over runbooks & standards → automation via SOAR/OSS—backed by audit-grade evidence.
Connective tissue:
🧠 AI platform → /solveforce-ai • 🖧 Fabric → /networks-and-data-centers • 🌐 Access → /connectivity
📶 Field/RAN → /private-5g • /cbrs • /mobile-connectivity
🔀 Control → /sd-wan • 🛡️ Edge → /sase • /ztna • /nac
🧱 Data → /data-warehouse • /etl-elt • /data-governance • /vector-databases
🛡️ Security → /cybersecurity • /waf • /ddos • 📊 Evidence → /siem-soar
☁️/🧠 Infra → /cloud • /edge-data-centers • /bare-metal-gpu
🎯 Outcomes (Why SolveForce AI for Telecom)
- Ops efficiency — auto-triage & remediate incidents; reduce MTTR and ticket volumes.
- Network quality — better RAN/transport utilization, fewer drops & congestion minutes.
- Forecasts that matter — capacity & traffic predictions that guide capex and power savings.
- Fraud & abuse down — SIM swap/A2P abuse/robo-traffic detection; STIR/SHAKEN verifications.
- CX uplift — NLP copilots for NOC, field techs, and contact centers—grounded with citations.
🧭 Scope (What We Build & Operate)
- Data fabric — streaming telemetry (RAN KPIs, xDR/NetFlow, logs), CM/PM/AM data, OSS/BSS feeds → lake/warehouse with contracts & lineage. → /etl-elt • /data-warehouse
- ML services — anomaly detection, traffic/capacity forecast, fault localization, intent translation, energy optimization.
- LLM copilots — guarded RAG over runbooks/MEF/3GPP/TM-Forum docs; change-explain & procedure generation with cite-or-refuse. → /vector-databases
- Edge inference — low-latency models (scheduling/steering) at cell/POP/edge DC; promotion to regional cores for heavy jobs. → /edge-data-centers
- Automation — closed-loop actions via SOAR/OSS (policy change, path pin, RAN parameter tweak, cache purge, ticket update). → /siem-soar
- Security & fraud — signaling & SIP abuse, bot/carding on portals (WAF/Bot), SIM swap patterns; DDoS anomaly controls. → /waf • /ddos
🧱 Building Blocks (Spelled Out)
- Governed data → safe models
- Contracts/lineage; PII minimization/tokenization; quality tests; feature registry; model cards. → /data-governance
- Low-latency rails
- EVPN/VXLAN cores; Anycast inference; GPU edge nodes where needed; message buses for real-time features. → /networks-and-data-centers • /bare-metal-gpu
- Guarded RAG
- Label/ACL pre-filters before ANN; telecom ontology (alarms/KPIs/acronyms); answers must cite sources or refuse. → /vector-databases
- Policy & safety
- Read-only service roles; staged rings; change approval on auto-remediation; rollback via SOAR.
- Zero-Trust
- ZTNA for consoles/APIs; device posture; NAC at sites; per-session approvals for privileged ops. → /ztna • /nac • /pam
🧰 Reference Architectures (Choose Your Fit)
A) NOC Copilot (Grounded LLM)
- RAG over runbooks/TMF/3GPP + ticket history; query KPIs/topology; propose actions with citations; open SOAR playbooks (rate-limit, reroute, config push) under approval.
B) RAN Optimization & Energy Saving
- Predict load; steer bands/carriers/slices; recommend tilt/power profiles; schedule eNB/gNB sleep windows while respecting coverage SLAs; edge inference <20 ms.
C) Transport & SD-WAN Assurance
- Telemetry → anomaly/forecast; intent → vendor-specific policies; closed-loop reroute/pin; brownout detection & packet-dup policy for voice. → /sd-wan
D) Fraud & Abuse Defense
- Patterns for SIM swap, CLI spoof, SIP scanning, A2P gray routes; WAF/Bot on portals; STIR/SHAKEN attestation checks; SOAR to block/revoke/notify.
E) Contact Center AI (CCaaS)
- NL routing, real-time assist, post-call summaries; PII redaction; PCI redaction on recordings; knowledge answers with citations.
📐 SLO Guardrails (You Can Measure)
| Domain | KPI / SLO | Target (Recommended) |
|---|---|---|
| Edge inference | p95 latency (policy action) | ≤ 10–20 ms at POP/edge |
| Forecast quality | MAPE (24–168h traffic) | ≤ 5–12% by domain |
| Anomaly recall@fixed FP | Recall @ 1% FP | ≥ 85–95% |
| Ticket automation | Auto-triaged incidents | ≥ 50–70% eligible |
| MTTR delta | Reduction vs baseline | ≥ 25–50% |
| Fraud detection | Block rate (known patterns) | ≥ 95–99% |
| RAN energy | Off-peak energy saving | ≥ 10–25% (site class) |
| Evidence completeness | Events/actions/citations | = 100% |
SLO breaches open tickets and trigger SOAR fallbacks (disable auto-action, human-in-the-loop, route to safe default). → /siem-soar
🔒 Compliance & Privacy
- CPNI (telecom privacy), STIR/SHAKEN attestation/verification logs, TCPA considerations for outreach.
- PII minimization & DLP, lawful intercept compliance posture (process documentation—no circumvention). → /dlp
- SOC 2 / ISO 27001 evidence packs; model cards & data lineage for audit.
📊 Observability & Evidence
- Model: versions, features, training data lineage, drift metrics.
- RAG: queries, retrieved docs, citations, refusal ledger.
- Automation: proposed → approved → executed → rollback chain; who/what/when/why.
- Cost: $/inference, GPU hours, data scan $/GB; FinOps dashboards. → /finops
All streams feed SIEM; SOAR playbooks enforce approvals, throttles, and rollbacks. → /siem-soar
🛠️ Implementation Blueprint (No-Surprise Rollout)
1) Use-cases & SLOs — NOC copilot, RAN energy, transport assurance, fraud, CCaaS; define targets.
2) Data & governance — contracts, PII minimization, lineage, quality tests; feature store. → /data-governance
3) Platform — edge/region GPUs, vector DB, model registry, prompt/feature stores; CI/CD for models. → /bare-metal-gpu
4) Guardrails — cite-or-refuse, pre-filters, allow-listed actions, human-in-the-loop for risky intents.
5) Integrations — OSS/NMS/EMS/SD-WAN APIs, ticketing, SOAR, WAF/Bot, STIR/SHAKEN feeds.
6) Pilot & rings — shadow mode → advisory → supervised auto → partial auto → full auto; rollback criteria.
7) Operate — SLO dashboards; drift/quality reviews; cost & safety evaluations; quarterly playbook expansion.
✅ Pre-Engagement Checklist
- 📡 Domains (RAN, transport, SD-WAN, OSS/BSS, CCaaS) & SLOs.
- 🗂️ Data sources (PM/CM/AM, xDR/PCDR, NetFlow, logs), privacy labels, retention.
- 🔐 Identity/ZTNA posture; PAM approvals; API access to OSS/NMS.
- 🧠 Models/LLMs (in-house vs hosted), vector DB choice, guardrail policies.
- 🧰 Automation scope (SOAR/OSS actions), approval matrix, rollback.
- 💸 Budget guardrails & GPU plan; $/inference targets.
- 📊 SIEM destinations; audit/report cadence; regulator obligations.
🔄 Where AI Telecom Fits (Recursive View)
1) Grammar — telemetry & control ride /connectivity and /networks-and-data-centers.
2) Syntax — built on /cloud + /edge-data-centers with secure on-ramps.
3) Semantics — /cybersecurity preserves truth; /siem-soar proves it.
4) Pragmatics — /solveforce-ai plans actions safely with approvals and citations.