Root cause you can prove.
Not just a plausible answer.
ABot AI works out what happened with deterministic telecom engines first, then uses an LLM to explain it. Every finding points to a packet, a log line or a timestamp. Same input, same answer, every time.
ABot AI is telecom-certified by Dell Technologies
Dell Technologies has validated ABot AI on Dell PowerEdge servers. The full certification record is published on Dell InfoHub.
- Solution version
- ABot AI 2.38
- Category
- AI / ML
- Certified on
- 17 Sep 2025
- Platform
- Dell PowerEdge R470 on bare metal
Evidence first. Language second.
Deterministic processors do the correlation. The LLM only reasons over evidence that has already been checked, behind guardrails.
Evidence backed
Every claim is tied to correlated evidence. That removes hallucination from the RCA.
Knowledge, not just RAG
Not a document search. It identifies the 3GPP procedure and spots drift from a healthy one.
Data sovereignty
Runs in your cloud with your enterprise LLM. Customer data is added securely.
Where generic LLM analysis stops short
An LLM on its own tends to stop at the error code. ABot AI follows the whole causal chain, from failure to detection to recovery.
| Capability | ABot AI | LLM-only |
|---|---|---|
| Root cause and fix | ✓Full causal chain from failure to recovery, with specific fix steps. Same answer every run | ✕Stops at the symptom with generic advice. Varies run to run |
| Evidence | ✓Every claim tied to a timestamped packet or log line, in exact event order | ✕Pattern matching, no trail |
| Multi-source correlation | ✓PCAPs, logs and counters together, including hidden config issues | ✕Struggles across sources, misses config issues |
| Call flow and impact | ✓Protocol, state and sequence validated. Impact graded by severity | ✕Generic description |
| Training | ✓None. Built on encoded telco knowledge | ✕Ongoing training and large vendor datasets |
Where teams use it
Each card is a use case in its own right. In a live network some data is easier to get than others, so start with what you have and add sources as access allows.
RAN, Core and IMS
End-to-end RCA across monolithic and disaggregated RAN (gNB, DU/CU), Core and IMS, including RAN and core interop.
Lab, pre-prod and fieldUE devices
Chipset, OS and OEM telemetry correlated with the network to find device and RAN interop issues.
From test devices and OEM toolsPCAP analysis
Rebuilds sessions across NGAP, NAS, GTP, PFCP, SBI and Diameter, and spots drift from a healthy 3GPP procedure.
Readily available in live networksLog analysis
Logs parsed and correlated with anomalies, including OpenShift, Dell APEX and AWS CloudWatch.
Needs OEM cooperationApplication and cloud KPIs
App stats plus K8s, OpenShift and cloud KPIs, with anomaly detection and outlier ranking.
Via a data lake such as NWDAFPredictive models need to learn from experiments, not just documents
Standards teach an AI model what a network is supposed to do. Only operational experience shows what it actually does under load, faults and change. That data is scarce, because operators rarely share failure data. ABot generates it.
Trained on documents
- Standards and specifications
- Protocol definitions
- Synthetic Q&A
- Answers that look right
- Knowledge benchmarks
Trained on ABot experiments
- Observed behaviour of real network functions
- Protocol behaviour under load and faults
- Scenarios with a known, injected cause
- Evidence-backed diagnosis and outcome
- Outcome benchmarks: did the network recover?
How the Scenario Factory produces labelled data
Every run produces a labelled record: the known cause, the full evidence and the outcome.
What the labelled data feeds
Prediction models
Congestion, anomaly and failure prediction trained on known causes.
LLM and agent training
Fine-tuning and tool-use examples grounded in real outcomes.
Benchmarks
Held-out scenarios that test generalisation, not memorisation.
Safety validation
Try a proposed fix on the twin before the live network.
Abstention training
Ambiguous evidence teaches a model when not to act.
Starter scenario library
Raw operator data stays private. Only sanitised or abstracted records are shared.
Building AI for RCA or Autonomous Networks? Prove it works before it goes live.
An AI that diagnoses or acts on a network needs an independent referee. ABot creates faults with a known cause, lets your AI respond, and checks the answer against deterministic evidence.
What gets measured
AI RCA vendors
Benchmark your engine on held-out, known-cause scenarios and show customers the scorecard.
Autonomous network teams
Test closed-loop actions on a twin before they touch live traffic, in line with the ITU-T Y.3181 ML sandbox idea.
Operators adopting AI
Get an independent, repeatable second opinion before you trust a vendor's AI with your network.
Simple to adopt, hard to fool
Intent-based analysis
Minimal model training
Reduced LLM dependency
Rapid onboarding
Simple to use
Pairs with ABot Test. ABot AI finds the issue in your data, and the ABot Core Digital Twin reproduces it and proves the fix. The Scenario Factory writes the test cases.
Go deeper
Test drive ABot AI today
Explore RAN, Core, IMS and infra data in the Experience Centre on AWS Marketplace.
