Ving

 

Enabling GenAI-driven Business operations
Ving is our Generative AI Platform that provides low code interface to enable creation and management of AI Agents at work

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Capabilities

Finetuned LLM with a blend of general purpose and business domain specific data.

Continuous learning module for self-enhancement.

Traceability

Detailed transaction management system.

Ability to track query, response, reasoning, and source references.

Data Privacy

Full control over training data.

No use of customer data for training.

AI Governance module for control and enhancement of LLM responses.

Elevate Your Business with Tailored LLMs

Our deep expertise lies in crafting and refining domain-specific LLMs that deliver unparalleled performance. As demonstrated by Ving in the telecom sector, we can tailor these powerful models to any business domain, solving complex challenges and driving innovation.

Industry’s first telecom-specific LLM TSLAM-12B
1
 
 
  Continuous Learning:
learns from user feedback.
2

 

Governance module:

monitors, manages, and approves user feedback
3

 

TSLAM:

7B parameters, 16-bit quantized for local deployment
4

 

Intent-to-API conversion:

converts user intent to network-specific APIs
5

 

Chat Interface

get details on uploaded docs and integrated applications
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No code Interface

easily integrate legacy applications and documents

The Future of Telecom is AI-Driven

NetoAI’s GiP (Generative Intelligent Platform) seamlessly integrates with existing OSS BSS applications, empowering telecom operators to achieve 100% optimization without disrupting their existing infrastructure.

Planes on knowledge graph

Projections Plane
Operations Plane
Network Plane
Contact Plane

Ving

TSLAM

Digital Console- Insights
Intent Manager
No Code Interface
AI Fusion Engine
AI Governance
ving

With GiP and TSLAM, telecom operators can:

 

    • Reduce operational costs: By automating tasks and minimizing downtime.
    • Improve customer satisfaction: By ensuring reliable and efficient network performance.
    • Gain a competitive edge: By leveraging AI-powered insights and optimizing network operations for maximum efficiency.
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Capabilities of Ving

TSLAM (Telecom Specific Large Action Model) is finetuned with a blend of general purpose and as well telecom-specific data (non-copyright protected data such as open-source general datasets, IETF RFCs, and configuration guides). This fine-tuning aims to equip the model with a deep understanding of telecom-specific activities, enabling it to effectively interpret and respond to telecom-specific queries.

Ving provides two interfaces listed below,

A. Document Interface:

    • An interface that facilitates the uploading of any documentation and provides a chat interface for users to query data related to the documents.

B. Application interface:

    • Seamlessly integrate existing applications using a low-code approach.
    • Enables the existing apps to be GenAI capable, providing a chat interface for users to interact with the applications and perform various actions.
    • Enables Ving to take intelligent actions via the apps.
    • No code changes are to be done on the existing applications.

Traceability of Ving

One significant drawback/limitation of all GenAI models is their inability to trace and explain their responses. In the Telecom domain, it’s crucial to understand why a model has made a particular decision, as allowing AI to autonomously manage network and operations (without oversight) is not feasible.

Ving comes with a very detailed transaction management system through which you’ll be able to identify,

  • What’s the query?
  • What’s the response?
  • Why this response?
  • What’s the source referenced for this response?

If dissatisfied with a response, you can provide negative feedback to the model and rectify it via the continuous learning module.
This ensures that Ving considers your input and delivers accurate responses for similar queries in the future.

Data privacy on Ving

The brain of Ving is a lightweight, high-speed 16-bit quantized LLM (TSLAM) boasting 12 billion parameters. This quantization and lightweight design allow for flexible deployment, whether locally within your data center or on your dedicated cloud VM.

  • All your data used for training and inference by Ving will be under your full control.
  • The knowledge graph created by Ving and TSLAM-12B with your data can be deployed either on your private cloud or on-prem. You get to decide who can control and manage the data.
  • After deployment, NetoAI doesn’t require any access to your environment unless there’s a specific need to perform any enhancements.

All data loading, feedback for continuous learning, and integration with application data remain under your control. We strictly adhere to privacy and confidentiality standards and do not utilize or access any of your data for model enhancement purposes.