Totogi
AI · Telecom · BSS PlatformTwo AI products that turned a telecom platform into an AI-native category leader
Plan AI predicts churn and personalizes offers across 8 million subscribers. BSS Magic generates entire telecom billing systems with AI, cutting 8-month integrations to 14 days. Together, they redefined what a modern telco platform looks like.
Telecom BSS is a graveyard of legacy software. Thousands of products, incompatible data schemas, and integration projects that cost millions and take the better part of a year. Totogi had the cloud-native platform and $100M in backing from TelcoDR. What they needed was AI that could turn that platform into something the industry had never seen — a BSS that builds itself and a monetization engine that thinks for itself.
Product One — Plan AI
Predicting churn before customers leave
Traditional telecom retention is reactive — a customer calls to cancel, and someone offers a discount. Plan AI flips this entirely. Machine learning models segment 8 million subscribers by behavior, usage patterns, and risk signals, identifying churn likelihood before the customer even considers leaving.
The system doesn't just flag at-risk customers. It generates personalized retention offers calibrated to each subscriber's value, usage profile, and price sensitivity. The right offer, to the right customer, at the right moment — automatically.
Revenue unlocked through personalization
Beyond retention, Plan AI identifies revenue opportunities hiding in the subscriber base. Customers on plans that don't match their usage. Segments that would respond to targeted upsell campaigns. High-value subscribers who've never been offered premium services.
LLMs generate the campaign copy, segment the audiences, and personalize the offer parameters. What used to require a marketing team and weeks of analysis now happens continuously, at scale, across millions of subscribers.
An 8-month integration in 14 days. 500,000 lines of production code generated in a single day. This isn't incremental improvement — it's a new physics for telecom.
Product Two — BSS Magic
A BSS that builds itself
Telecom BSS integration is one of the most expensive, painful processes in enterprise software. Over 2,000 products, each with unique data schemas, incompatible APIs, and years of accumulated cruft. A typical integration project runs 8 months and costs millions in professional services.
BSS Magic uses AI to standardize data structures across any BSS system, automating the mapping and integration that used to require armies of consultants. It doesn't just speed up integration — it eliminates the need for professional support teams entirely. What took 8 months now takes 14 days.
The Totogi Ontology
At the core of BSS Magic is the Totogi Ontology — a unified semantic layer that gives AI a complete understanding of telecom data across any vendor or system. It encodes decision logic, maps relationships, and provides the guardrails for AI to reason across fragmented BSS stacks.
The ontology is what makes the magic possible. Without it, AI is just pattern matching on incompatible schemas. With it, AI can harmonize, orchestrate, and generate production-ready integrations that actually work in the real world — proven live on stage at the AI-Native Telco Forum, where Totogi generated 500,000 lines of production BSS code in a single day.
The Impact
From cloud telco to AI telco
Plan AI and BSS Magic didn't just add features to Totogi's platform. They changed what the company is. Totogi went from positioning as a cloud-native BSS to branding as the world's first AI-native telecom platform — a category of one.
Guinea Mobile selected Totogi as their BSS platform. 2degrees redefined their wholesale growth strategy around it. Tier 1 operators globally are running BSS Magic in production, clearing quote backlogs worth hundreds of thousands and cutting order processing times by 80%.
The products we built became the reason Totogi could stand on stage at MWC Barcelona and claim the AI-native telco category. Not a pitch — a live demonstration, with 500,000 lines of code to prove it.
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