# AI for messaging infrastructure.

- Canonical URL: https://www.broadnet.me/broadnet-ai
- Language: en
- Last updated: 2026-09-29
- Publisher: BroadNet (https://www.broadnet.me)
- Markdown version of the page above, generated from the rendered page.

[Where this fits AI product map · See all four AI products and who each one is for](https://www.broadnet.me/ai#ai-map)

Carrier AI · inside operator networks

Machine-learning fraud detection, routing optimisation, intent classification, and sentiment analysis for messaging at scale. The same models that run inside the BroadNet network — now available as composable services for the operators and enterprises that already trust the carrier.

[Talk to AI engineering](https://www.broadnet.me/contact) [All AI products](https://www.broadnet.me/ai#ai-map)

Training corpus

300M+

Messages per day of real BroadNet network telemetry — across 200+ countries, 1,000+ direct operator connections, and 23 years of interconnect history.

**Inference p99**

<50ms

**Languages**

16

**Production models**

6

**Operating since**

2003

No PII ever leaves the customer tenant. Models train on hashed, aggregated network features only.

![Carrier-grade AI inside the operator network](https://www.broadnet.me/marketing/generated/viz-ai.png)

AI for carriers

## Carrier-grade ML, inside the network.

Line-rate fraud detection, routing optimisation, and sender-ID anomaly scoring deployed inside operator networks — trained on telemetry from 300M+ messages a day.

200+

countries

1,000+

operator connections

99.99%

SLA

[Talk to AI engineering](https://www.broadnet.me/contact)

Inside the agent console

## Watch a model resolve a ticket — in the customer's language.

Intent detection, a live tool call, a RAG lookup, and a resolved answer — in Arabic and English, from one conversational model. Below is a redacted, illustrative transcript.

- Customer
- Tool / RAG call
- AI agent

broadnet-ai · agent-console

Example · redacted Session

session: cx_7f21••• model: bn-convo-v4 · ar+en

detected: ar intent: order_status

مرحبًا، أين طلبي رقم ‎#12345؟

→ nlp.detect_lang() → "ar" · classify_intent() → order_status (0.98)

→ rag.retrieve(kb="orders", ref="#12345")

→ order_status(#12345)

← 200 OK { status:"out_for_delivery", eta:"18:30", courier:"•••••" }

Resolved

طلبك ‎#12345 قيد التوصيل وسيصل اليوم قبل الساعة 6:30 مساءً.

detected: en intent: notify_subscribe

Thanks! Can you text me when the driver is close?

→ notify.subscribe(order="#12345", channel="sms", trigger="eta<10m")

← 202 Accepted { subscription:'sub_9f2c•••' }

Resolved

Done — I'll text you when the driver is about 10 minutes away.

Illustrative example — a redacted sample transcript, not a live customer session.

Built on real BroadNet telemetry

## The data only a carrier can see.

Carrier AI is trained on the same network signals BroadNet has been collecting since 2003 — DLRs, route latencies, sender-ID histories, and two-way conversational flows. It's not a wrapper on a public LLM. It's models with ground truth.

Training corpus

300M+

Msg / day

Routing coverage

200+

Countries

Direct DLR feedback

1,000+

Operator connections

Ground-truth history

23 yrs

Of telemetry

Capabilities

## Six models. All in production today.

Every model below is running live inside the BroadNet network right now — catching fraud, picking routes, and tagging intents on the 300M+ messages we process every day. The same models are available as services to operators and enterprises through the Carrier AI API.

Used in SMS Firewall

### ML fraud scoring

Sub-second fraud probability for every inbound A2P message. Catches SMS phishing, AIT, grey-route artefacts, and trust-score anomalies before they hit the subscriber.

Used in SMSC Gateway

### Smart routing

Reinforcement-learning router that picks the best operator path per destination, time of day, and content type. Optimises for delivery, cost, and DLR truthfulness simultaneously.

**Routes/sec per node**

30,000

Used in Sender ID Registry

### Sender ID anomaly detection

Behavioural fingerprinting on alphanumeric senders. Flags hijacked IDs, spoofed brands, and look-alike registrations before they damage your customer trust score.

**Coverage**

200+ ctry

Used in Two-way messaging

### Intent classification

Multilingual intent tagging for inbound replies — STOP, HELP, purchase, support, escalation. English + Arabic native, plus 14 additional languages tuned for MENA business contexts.

**Languages**

16

**Intents**

240+

Used in Campaign analytics

### Sentiment analysis

Aspect-level sentiment scoring across two-way conversations. Surfaces the segments of a campaign that drove negative reactions so you can fix the message, not the audience.

**Aspects**

32

**Polarity**

5-class

**Latency**

<50ms

Used in SMSC + voice firewall

### SIM-box & flash-call detection

Carrier-grade detection of grey-route revenue leakage — SIM-box origination, flash-call abuse, and bypass-OTT patterns scored at line rate. Feeds the same A2P monetization loop the firewall enforces.

**Latency**

<20ms

Network integration

## The models sit inside the network, not next to it.

Model decisions are written back into the routing plane in real time — no batch jobs, no nightly retrains. The firewall, SMSC, WhatsApp, RCS, and voice connectors all consume inference from the same regional clusters with end-to-end p99 latency under 50ms.

1. Layer 01 Edge Inbound A2P / Two-way / Voice Every message, call event, and webhook is enriched in-flight with model features — sender reputation, content embedding, route history.
2. Layer 02 Models Carrier AI inference plane Six production models — fraud, routing, anomaly, intent, sentiment, conversational — served from regional inference clusters with <50ms p99 latency.
3. Layer 03 Network SMS Firewall · SMSC · WhatsApp · RCS · Voice Model decisions are written back into the routing layer in real time — no out-of-band batch jobs, no human-in-the-loop bottleneck.

POST · api.broadnet.me · fraud scoring

live

```
{
  "msg_id":     "01HGN…7QF",
  "sender":     "MyBank",
  "destination":"+971 50 ••• 4567",
  "features": {
    "sender_age_d":     412,
    "route_dlr_truth":  0.998,
    "content_emb":      "<f32x768>"
  },
  "decision": { "label":"clean", "score":0.0021, "model":"fraud-v4.2" }
}
```

Data governance

## Carrier-grade AI means carrier-grade data handling.

Carrier AI is built inside a regulated carrier — not a model shop. The same compliance posture that lets us operate as a licensed messaging provider in 200+ countries governs every byte the models touch.

### Data stays in customer infrastructure

No PII ever leaves your tenant. Models train on hashed, aggregated network features — never message bodies, never subscriber identifiers.

### Full model audit logs

Every inference is signed and persisted with the feature vector, model version, and decision. Exportable for regulators, internal audit, and dispute resolution.

### GDPR · CCPA · HIPAA · ISO 27001

Carrier AI inherits the parent network's certifications: ISO 27001:2022, ISO 9001:2015, ISO 22301 BCMS, SOC 2, plus NIST AI RMF validation and OWASP LLM Top 10 testing.

- ISO 27001
- ISO 9001
- ISO 22301
- SOC 2
- GDPR
- NIST AI RMF

Hashed feature pipeline Signed inference logs · 7-year retention On-prem / regional residency available

Carrier AI · engineering intake

## Talk to AI engineering.

Tell us where you want a model — in your firewall, on your two-way traffic, or fronting your support queue. An AI engineer will scope the integration, share a benchmark on a slice of your real traffic, and stand up a pilot within two weeks.

[Talk to AI engineering](https://www.broadnet.me/contact) [All AI products](https://www.broadnet.me/ai#ai-map)

$ ai@broadnet.me · pilot scoped within two weeks