Migrate & Consolidate
Transformation done in weeks
Turn migrations into a repeatable capability by representing both the legacy and target environments in a single, executable semantic layer that validate state transitions during coexistence. Enable AI to do the heavy lifting – mapping, validation, and reconciliation. Transformations move in weeks, not years.
A capability instead of a program
BSS, OSS, and core migrations drag on because legacy and target stacks don’t share the same semantics – and too much critical business logic lives in spreadsheets, rule engines, and tribal knowledge. The Totogi Ontology becomes the shared language across both worlds, giving AI the context to generate mappings, validate behavior, reconcile exceptions, and maintain end-to-end traceability – so consolidation becomes faster, safer, and repeatable.
Shorter migration timelines
AI-driven mapping, validation, and reconciliation shrink migrations from months/years to weeks – and reduce “unknowns” fast.
Lower migration cost and risk
Less manual analysis and rework, fewer contractors, and earlier detection of semantic gaps before they become production fires.
One experience across dual stacks
Keep journeys consistent across estates – same offers, entitlements, invoices, and support answers – so customers never feel the coexistence.
Faster stack consolidation
Unify products, pricing, and policies across brands and systems so “one company” becomes real in production, not just on billboards.
Cleaner coexistence during phased change
Keep integrations stable and traceable during parallel run, multi-brand transitions, and hybrid environments – without brittle point fixes.
Migrations fail when semantics diverge.
Fix the semantics – and migrations become a repeatable capability.
An AI truth layer for migration, coexistence, and consolidation
Totogi creates a consistent, machine-readable model of your telco across legacy and target systems – capturing entities, business rules, and valid state transitions – so change can be validated as real behavior, not just data movement. It keeps semantics consistent across parallel stacks, enabling safer migrations and faster consolidation.
- Ontology-driven mapping of entities and relationships across legacy, target, and coexisting stacks
- Logic capture and validation so both worlds behave correctly during coexistence and after cutover
- Action-level validation to confirm critical operations work across stacks
- Automated reconciliation to identify, explain, and resolve exceptions at scale across brands and systems
- Traceability and governance from requirements → mappings → validations → cutover and consolidation decisions
Start with one high-stakes
business problem
You don’t need to start with a massive transformation.
Start with one high-impact question: deal execution, order fallout, revenue leakage,
or even cross-system reporting disputes.
We’ll show you how the Totogi Ontology solves this.
Discover more telco insights
Turn migrations into a repeatable capability
See executable semantics in action
It means migration and consolidation of telco BSS, OSS, and core systems stop being a long, brittle, one-time projects and becomes a repeatable capability done in weeks instead of months and years of workshops, design, bespoke API and schema translations, and endless exception handling. Totogi Ontology provides a shared, executable semantic layer (the Telco Ontology) that aligns legacy and target systems on meaning—not just field names. That lets you automate mapping, validate business logic and state transitions, and reconcile outcomes at scale while both stacks coexist. The “weeks” claim isn’t about reckless cutovers; it’s about removing the core time sink: humans manually interpreting mismatched definitions across domains (customer, product, entitlement, billing, etc.) and then reworking everything when reality disagrees.
Because migrations usually fail at the point nobody models properly: semantics and behavior. Your data isn’t just rows; it represents contracts, entitlements, pricing rules, lifecycle states, and cross-system dependencies. Legacy stacks often encode those rules in a mix of code, configs, “special cases,” and tribal knowledge. Then the target stack encodes them differently—so the same customer can mean different things depending on where you ask. That’s why “we moved the data” doesn’t equal “the business still works.” Projects stall in reconciliation hell: edge cases, mismatched statuses, phantom products, rating differences, and order fallout that only shows up when real traffic hits.
Think of it as a shared language your systems can execute. Totogi Ontology models key telco entities (customers, accounts, services, products, resources, charges, events, etc.) and, more importantly, how they relate and change over time. During migration, that becomes the bridge between “what legacy means” and “what target expects.” Instead of translating thousands of data fields blindly, you translate concepts and validate that the transitions still make sense (e.g., when a service moves states, what entitlements should exist, what charges should apply, what downstream systems must reflect). That’s how you migrate between systems or consolidate desperate domains without losing the plot.
It upgrades mapping from “field-to-field translation” to concept-to-concept alignment across domains. In real telco stacks, entities rarely line up cleanly: one system’s “subscriber” is another’s “service instance,” and product structures vary wildly across catalog, CRM, provisioning, and billing. Ontology-driven mapping identifies what each thing is, how it relates to other things, and what rules govern it—then maps that meaning across stacks. That reduces ambiguity, prevents accidental logic loss, and helps you keep consistency during coexistence. It’s especially valuable in post-merger scenarios, where you inherit multiple definitions of customer, product, and entitlement and nobody can agree which one is “the truth.”
By validating the migration at the level where risk actually lives: operations and outcomes. Instead of waiting until cutover to discover that some customers can’t provision, some invoices don’t balance, or some entitlements silently vanish, you run parallel validation while both stacks operate. You test state transitions, order scenarios, rating results, and downstream reflections across systems—then use automated reconciliation to isolate and resolve exceptions. This is faster than traditional methods because you’re not relying on manual interpretation or spreadsheet-driven sign-offs. You’re using a shared semantic model to automate what humans usually do slowly: interpret meaning, compare behavior, explain differences, and decide whether the gap is acceptable or a real defect.
It means customers don’t get punished for your internal transformation. During coexistence, you may run two billing stacks, multiple catalogs, or split CRM/provisioning flows. Without semantic consistency, customers feel it immediately: wrong invoices, missing add-ons, inconsistent self-service views, and support agents who can’t see the same reality across systems. “One experience” means offers behave the same, entitlements are consistent, orders complete reliably, and care teams have a unified understanding—even while back-end systems differ. The Telco Ontology provides the shared context to keep definitions aligned across stacks. So you can modernize without creating a “before and after” customer base where half your subscribers are on one planet and half are on another.
Yes—because post-merger consolidation is rarely a technology problem first. It’s a meaning problem. Two operators merge and suddenly you have different customer hierarchies, different product structures, different billing semantics, and different lifecycle states. Integrating “as-is” stacks creates a spaghetti of translations that becomes impossible to unwind. Totogi’s approach lets you consolidate semantics first: model both environments in a single ontology-driven layer, reconcile differences with traceability, and then choose a rational path to decommissioning or convergence. That helps you move faster, reduce risk, and avoid the most common trap: spending years building integration glue that makes the combined estate more complex than the two companies were separately.
No. This is built for the reality most CSPs live in: hybrid, multi-vendor, and coexistence-heavy. You can keep your current systems running while you map, validate, and migrate in controlled waves. The Telco Ontology sits as a semantic layer that helps you normalize meaning and verify outcomes across legacy and target environments. That’s important because “rip and replace” isn’t just risky—it’s usually impossible given regulatory constraints, contract obligations, customer impact, and operational dependencies. Instead, you modernize without betting the company on a single cutover weekend. You get a governed path: parallel run, evidence-based validation, automated reconciliation, and phased decommissioning when you’re confident—not when a project plan says you should be.
Any migration where cross-domain consistency matters—which is basically all of them in telco. Billing migrations are obvious because financial correctness is non-negotiable. But consolidation also hits product and offer semantics (catalog), entitlement rules (charging/provisioning), quote-to-order flows (CPQ/order management), customer identity (CRM), and downstream reporting definitions (BI/finance). It’s also highly relevant for OSS and network-adjacent transformations where service and resource models diverge across systems. The common factor is not the system type; it’s the requirement that multiple systems must agree on “what a thing is” and “what state it’s in.” The ontology provides that shared understanding so the migration doesn’t fracture your operational truth.
Start where pain is highest and ambiguity is greatest: a cross-domain area that currently causes fallout, leakage, or repeated escalations. That might be an order flow that breaks across systems, a billing discrepancy class that keeps reappearing, or a merger-induced mismatch in product semantics. The point is to establish the ontology-driven pattern: define meaning, map concepts, validate actions, reconcile exceptions, and build traceability. Once that pattern exists, scaling to the rest of the estate gets easier because you’re not reinventing the method for every migration wave. You’re extending a governed semantic foundation. Big-bang programs fail because they try to solve everything at once. This approach wins by making progress in controlled increments that compound.