Own the source code. Deploy autonomous agents. Run on your infrastructure — with zero vendor dependency and full FCC-compliance readiness.
Telecom operators face a unique convergence of pressures: real-time network demands, massive customer bases, strict regulatory oversight, and a workforce that must constantly upskill as infrastructure evolves. ibl.ai delivers a production-grade AI platform — not a pilot, not a SaaS subscription — that you own outright and deploy on your own infrastructure.
With 1.6M+ users across 400+ organizations and proven deployments at global scale including NVIDIA's learn.nvidia.com, ibl.ai is built for the complexity telecom demands. Our autonomous AI agents don't just answer questions — they monitor networks, execute remediation workflows, coordinate field teams, and analyze compliance data without human intervention at every step.
From network operations centers to customer experience teams to technician training programs, ibl.ai gives telecom providers a fully owned AI stack. You receive the complete codebase, deploy it inside your perimeter, and operate it indefinitely — with no telemetry, no external calls, and no dependency on ibl.ai to keep running.
ibl.ai is not a prototype or a proof of concept. It powers AI operations for 400+ organizations globally, including NVIDIA's worldwide AI training platform — built for the scale and reliability telecom demands.
You receive the complete, unobfuscated codebase. No black boxes, no SaaS dependency. Your engineering team can audit, extend, and modify every component to meet telecom-specific operational and regulatory requirements.
Run on your own data centers, private cloud, or air-gapped network infrastructure. Zero external dependencies means the platform operates fully within your security perimeter, critical for core network environments.
Use Claude, GPT-4, Gemini, Llama, Mistral, or your own fine-tuned models. Swap or combine models per use case without re-architecting. No lock-in to any single AI provider or model generation.
If you never call ibl.ai again after delivery, the platform keeps running. You own the IP, the infrastructure, and the roadmap. This is a permanent asset, not a recurring dependency.
Every capability is accessible via RESTful APIs. Model Context Protocol (MCP) integration connects agents to OSS/BSS systems, network databases, ticketing platforms, and external data sources natively.
AI agents continuously monitor network telemetry streams, detect anomalies in real time, correlate fault signatures across nodes, and autonomously execute predefined remediation playbooks — escalating only when human judgment is required. Agents query OSS databases, open trouble tickets, and notify NOC teams without manual triage.
Autonomous agents handle complex customer service workflows end-to-end: verifying account status, diagnosing service issues via API calls to network systems, processing billing adjustments, and coordinating field dispatch — all without agent handoffs. Escalation to human agents occurs only for defined exception conditions.
MentorAI agents deliver personalized upskilling programs for field technicians and NOC staff — adapting content to individual skill gaps, tracking certification progress, and generating compliance-ready training records. Agents proactively assign modules based on role changes, new equipment deployments, or regulatory updates.
Agents autonomously audit network configurations, data handling practices, and operational logs against FCC, CPNI, and CALEA requirements. They generate compliance reports, flag violations, and trigger remediation workflows — maintaining a continuous compliance posture rather than point-in-time audits.
Autonomous agents analyze service demand forecasts, technician availability, skill certifications, and geographic routing to optimize field dispatch schedules. Agents coordinate with ticketing systems, send technician briefings, and update customers in real time — executing the full coordination loop without dispatcher intervention.
Agents analyze behavioral signals, usage patterns, billing history, and support interactions to identify at-risk customers. They autonomously trigger personalized retention offers, schedule outreach, and log all actions to CRM systems — executing the full retention workflow, not just surfacing a risk score.
Traditional chatbots answer questions. Autonomous AI agents take action, reason over context, and deliver measurable outcomes.
ibl.ai deploys autonomous AI agents that go beyond simple Q&A. Our agents reason, plan, and execute multi-step workflows while you retain full code ownership and infrastructure control.
The entire platform runs within your network perimeter with zero external dependencies. No calls to ibl.ai servers, no cloud APIs required, no internet connectivity needed. Critical for core network environments and classified infrastructure where data sovereignty is non-negotiable.
ibl.ai collects no usage data, no model inputs, no operational telemetry. Customer data, network configurations, and agent interactions remain entirely within your infrastructure. This is an architectural guarantee, not a policy commitment.
Every agent decision, API call, database query, and workflow execution is logged with full context, timestamps, and reasoning traces. Audit logs are stored within your infrastructure and are fully queryable — supporting FCC, CALEA, and internal governance requirements.
Built-in multi-tenancy supports strict isolation between business units, network domains, and user roles. Granular RBAC ensures that NOC teams, field technicians, compliance officers, and executives access only the data and agent capabilities appropriate to their role.
Because you own the complete codebase, your security team can audit every line — no black boxes, no obfuscated dependencies. Penetration testing, vulnerability assessments, and security certifications can be conducted against the actual code running in your environment.
Run LLM inference entirely on your own hardware using open-weight models like Llama or Mistral. No prompts, no customer data, and no network information is ever sent to external model providers — eliminating a critical data exfiltration vector.
Full source code ownership means the platform is a capital asset on your balance sheet, not an operational expense that disappears if you stop paying. The system runs indefinitely without any ongoing relationship with ibl.ai.
Your security, legal, and compliance teams can review the complete codebase before deployment. This is essential for FCC-regulated environments where third-party software must meet strict security standards and where black-box systems create unacceptable regulatory risk.
Integrate directly with your OSS/BSS stack, proprietary network management systems, or legacy infrastructure. Your engineering team can extend agent capabilities, add custom workflows, and build telecom-specific features without waiting for a vendor roadmap.
Run on-premises in your data centers, on private cloud, across hybrid environments, or in air-gapped network segments. Source code ownership means you control the deployment architecture — not the vendor.
You control the upgrade cycle. No forced migrations, no deprecated APIs, no surprise breaking changes from a vendor update. Your production environment stays stable on your schedule — critical for telecom operators where unplanned changes can impact network reliability.
ibl.ai delivers the complete source code and works with your engineering and infrastructure teams to deploy the platform within your environment. This includes configuration for your network architecture, integration with existing OSS/BSS systems, and setup of your chosen LLM models — whether cloud-based or on-premises inference.
ibl.ai engineers collaborate with your teams to build and configure autonomous agents tailored to your operational priorities — network fault management, customer service orchestration, technician training, or compliance monitoring. We configure MCP integrations, define agent reasoning workflows, and establish audit logging aligned to your governance requirements.
Your engineering team takes complete ownership of the codebase and production environment. ibl.ai provides documentation, knowledge transfer, and optional ongoing support — but the platform runs entirely under your control. No dependency on ibl.ai for uptime, updates, or operations.
Autonomous agents handling fault detection, triage, and first-level remediation reduce NOC staffing requirements and shift human attention to complex, high-value escalations — delivering significant operational cost savings at scale.
AI agents executing end-to-end service workflows — from diagnosis to resolution — reduce average handle time and increase first-contact resolution, directly lowering cost-per-interaction across millions of customer touchpoints.
Personalized AI-driven training programs accelerate technician certification and reduce time-to-productivity for new hires and upskilling programs — critical as 5G, fiber, and edge deployments demand continuous workforce development.
Continuous automated compliance monitoring and report generation eliminates the manual effort of periodic audit preparation, reducing both the cost and the risk of regulatory non-compliance across FCC, CPNI, and CALEA frameworks.
Proactive agent-driven retention workflows — triggered by behavioral signals and executed autonomously — intercept at-risk customers before churn occurs, protecting revenue without proportional increases in retention team headcount.
Telecom operators are subject to extensive FCC oversight covering network reliability, outage reporting, consumer protection, and data handling. AI systems that interact with network operations or customer data must support accurate, auditable records for regulatory reporting.
ibl.ai's complete audit trail logs every agent action with full context. Air-gapped deployment ensures no regulated data transits external systems. Source code ownership allows your legal team to verify compliance posture before deployment.
FCC rules strictly govern how telecom providers collect, use, and protect CPNI. AI systems that access or process customer usage data, call records, or service information must operate within defined CPNI boundaries and maintain demonstrable data protection controls.
Zero telemetry architecture ensures CPNI never leaves your infrastructure. Role-based access controls restrict agent access to CPNI data to authorized workflows only. Full audit logs provide the documentation trail required for CPNI compliance certification.
CALEA requires telecom providers to maintain lawful intercept capabilities and ensure that AI-driven network management systems do not interfere with or obscure lawful access requirements. AI platforms operating in network infrastructure must be architecturally transparent.
Full source code delivery allows your compliance and legal teams to verify that the platform does not interfere with CALEA obligations. Air-gapped deployment and complete audit trails support the documentation requirements associated with lawful intercept compliance.
Telecommunications is designated critical infrastructure under federal guidelines. AI platforms deployed in network operations environments should align with NIST CSF controls covering identify, protect, detect, respond, and recover functions.
Air-gapped deployment, zero external dependencies, full source code auditability, and comprehensive audit logging directly support NIST CSF control families. On-premises model inference eliminates third-party AI provider risk from your critical infrastructure threat model.
See how ibl.ai deploys autonomous AI agents you own and control — on your infrastructure, integrated with your systems.