Scalable voice data for better AI outcomes.
Every single SIP message and CDR entry correlated for your AI tools.
normalize · correlate · isolate · bring your own LLM
Better data leads to better AI outcomes
AI models are widely available. What makes them truly powerful on a voice network is the quality of data they work from. When your LLMs are backed by a clean, correlated, and queryable picture of your traffic, they can deliver fast, precise, and dependable insights.
Three core properties transform raw voice telemetry into a trustworthy foundation for AI:
| Property | Raw voice data | Voice Intelligence |
|---|---|---|
| Format consistency |  Many formats |
 One normalized record |
| Call completeness | Uncorrelated legs |
Correlated into one call |
| Ownership boundary | Mixed together |
Tenant-isolated |
Voice Intelligence Makes You Data-Ready
AI models analyze your network much like a human engineer does: they need complete, consistent, and accurately assigned records. The same proven pipeline that powers Trace and Nodes organizes your raw telemetry so your AI tools can immediately make sense of it.
It normalizes every data source into scalable databases
It correlates legs into calls with 100% accuracy
It tells AI how to use the data
Reaching the Data With MCP
Once the data is ready, the model you choose reaches it through the Model Context Protocol. TelcoBridges’ MCP server is a way to turn any LLM into a ProSBC expert: query the documentation, add a NAP, push a routing rule, all with your approval.
Voice Intelligence adds the critical data layer. TelcoBridges exposes over 100 role-scoped actions on the ProSBC, allowing your model to go beyond answering questions or updating settings. Voice Intelligence ensures every action taken by AI is driven by clear network evidence rather than guesswork.
Unlock the Full Potential of AI
Realizing the full potential of AI on a voice network requires seamless integration between telemetry and network control. TelcoBridges bridges this gap by providing both the underlying data infrastructure and the AI-enabled session border controller working together.
Proactive monitoring
An AI model watching the correlated stream and surfacing what matters, in plain English, before it becomes an incident, instead of a person watching graphs and hoping to notice.
Proactive maintenance
An AI model that reads capacity and health trends across the fleet and flags the node trending toward its ceiling while there is still time to rebalance, with the SBC action a permissioned step away.
Proactive fraud and anomaly response
An AI model that recognizes a robocall spike, an off-hours surge, or an unusual destination pattern as it forms, explains why it is suspicious against the baseline, and proposes the containing action for a human to approve.
From anomaly to action in one conversation
A structured data foundation turns complex network events into immediate, guided resolution. Voice Intelligence flags the anomaly, your connected AI model explains why it happened against your normal traffic baseline, and you can apply a reversible, auditable fix to the SBC with a single click.
ASR dropped from 91% to 67% on NAP twilio-east in the last 4 minutes. Z-score 4.2.
87% of the failed calls returned SIP 408 (timeout) from twilio-east. The same calls succeed on twilio-west. Looks like Twilio-east peering degradation, not your SBC. Recommendation: lower routing weight on twilio-east temporarily.
Apply weight change via EMS? This is reversible. Stays auditable.
Frequently Asked Questions
Why not just point a model at my existing CDRs?
Because raw CDRs are split across formats and uncorrelated across legs, and often mixed across customers. They are not queryable or searchable in any consistent way, so a model reading them produces confident but unreliable answers. Voice Intelligence normalizes, correlates, and isolates the data first, turning it into one queryable, searchable record, which is the work that makes the model trustworthy.
Do I need a separate MCP for Voice Intelligence?
No. TelcoBridges runs one MCP server, documented on the TelcoBridges MCP page. Voice Intelligence supplies the correlated voice data a connected model reasons over; the MCP is how the model reaches it. You bring the model and its credentials.
Is my data used to train someone else’s model?
No. Your data stays your data, scoped to your tenant. The model you connect reads it to answer your questions; the platform does not pool it into anyone else’s training.
Do I have to use AI at all to get value from this?
No. The same correlated, normalized data drives Trace and Nodes on their own. Readiness for AI is a property the data gains for free once it is done right.
Drive Real Value with AI on Your Voice Network
Start transforming your raw call telemetry into structured, tenant-isolated context today. Fill out the form below to see how Voice Intelligence and MCP bring active, permissioned control to your preferred AI models.