First Native LLM Fine-Tuned for the Telecom Industry
Kinetica was founded in 2016 as it developed an innovation initiative for the Army to track and analyze national security threats and deliver real-time analytics.
Today, they are pioneers in GPU-powered spatial and time-series analytics. Kinetica has just unveiled SQL-GPT for Telecom — the industry’s first large language model solution purpose-built for the telco sector. This innovation aims to empower telco professionals by enabling natural language conversations with real-time data to gain actionable insights.
As Chad Meley, CMO of Kinetica, explained, “We’ve put a front end that is natural language to SQL. This enables everyone in the organization to leverage the kinds of time series and spatial joins that a telco organization would do.” Translating plain English questions into performant SQL tailored to common telco data sets and use cases removes the need for specialized data science skills.
For example, a field technician could simply ask the system to “show equipment needing immediate attention” and get an instant prioritized list of issues to resolve.
Overcoming Analytics Bottlenecks for Faster Insights
A key benefit is democratizing access to fast insights that drive decisions. As Meley elaborated, “Scarce resources are a bottleneck for companies to get value from their data. Most companies are just scratching the surface. As such, giving people the ability to ask a straightforward question and get an answer is liberating. Questions that don’t sound necessarily complex can require lengthy code to answer it. We are enabling companies to know the answers to questions that previously would have gone unanswered.”
Phil Darringer, VP of Product for Kinetica, provided a client example showcasing the transformational time-to-insight. For complex geospatial network analysis, their solution delivered results in 30–45 minutes versus months or even years with traditional platforms. This massive acceleration empowers telcos to analyze and act on emerging network issues in near real-time before customers are impacted.
Fine-Tuning for Telco Industry Data and Jargon
So, how does SQL-GPT achieve this? Darringer explains, “There are a couple of layers to this starting with a foundational model. The first step is to instruct it on Kinetica-specific syntax and functions we provide.” This teaches the AI core Kinetica functionality.
“The next is then adding these capabilities around the jargon and the specific knowledge of telco relevant datasets.” Training the model on industry terminology and common telco data schemas builds the literacy necessary to have a natural dialogue tailored to real-world questions telco practitioners need to answer daily.
Purpose-Built for Speed and Scale
Processing performance and scale are crucial when handling the massive data volumes telcos generate from networks, devices, and sensors. As Darringer clarifies, “Because the telcos are very sensitive about their data, the model will be deployed in their environment, whether that’s in their data center, or within their cloud, VPC within their own environment within the public clouds.”
This ensures data privacy while leveraging Kinetica’s GPU-accelerated architecture, as Meley highlights: “We execute on NVIDIA GPUs for inferencing to actually generate the SQL. It will scale linearly with the number of requests and how often they are being used to generate these queries.”
The customized on-premise deployment also mitigates telco concerns around data confidentiality with large public language models. By keeping the fine-tuned model isolated within each telco’s environment, their data remains completely private. As Meley summarized, clients expressed that “they don’t want to take any of their data, even metadata, and expose it to a public API.”
SQL-GPT for Telecom marries natural language simplicity and real-time performance at scale while guaranteeing security for highly sensitive telco data assets. This empowers faster data analysis by more people in the organization and service response to maximize network quality and customer satisfaction.
Kinetica was founded in 2016 as it developed an innovation initiative for the Army to track and analyze national security threats and deliver real-time analytics.
Today, they are pioneers in GPU-powered spatial and time-series analytics. Kinetica has just unveiled SQL-GPT for Telecom — the industry’s first large language model solution purpose-built for the telco sector. This innovation aims to empower telco professionals by enabling natural language conversations with real-time data to gain actionable insights.
As Chad Meley, CMO of Kinetica, explained, “We’ve put a front end that is natural language to SQL. This enables everyone in the organization to leverage the kinds of time series and spatial joins that a telco organization would do.” Translating plain English questions into performant SQL tailored to common telco data sets and use cases removes the need for specialized data science skills.
For example, a field technician could simply ask the system to “show equipment needing immediate attention” and get an instant prioritized list of issues to resolve.
Overcoming Analytics Bottlenecks for Faster Insights
A key benefit is democratizing access to fast insights that drive decisions. As Meley elaborated, “Scarce resources are a bottleneck for companies to get value from their data. Most companies are just scratching the surface. As such, giving people the ability to ask a straightforward question and get an answer is liberating. Questions that don’t sound necessarily complex can require lengthy code to answer it. We are enabling companies to know the answers to questions that previously would have gone unanswered.”
Phil Darringer, VP of Product for Kinetica, provided a client example showcasing the transformational time-to-insight. For complex geospatial network analysis, their solution delivered results in 30–45 minutes versus months or even years with traditional platforms. This massive acceleration empowers telcos to analyze and act on emerging network issues in near real-time before customers are impacted.
Fine-Tuning for Telco Industry Data and Jargon
So, how does SQL-GPT achieve this? Darringer explains, “There are a couple of layers to this starting with a foundational model. The first step is to instruct it on Kinetica-specific syntax and functions we provide.” This teaches the AI core Kinetica functionality.
“The next is then adding these capabilities around the jargon and the specific knowledge of telco relevant datasets.” Training the model on industry terminology and common telco data schemas builds the literacy necessary to have a natural dialogue tailored to real-world questions telco practitioners need to answer daily.
Purpose-Built for Speed and Scale
Processing performance and scale are crucial when handling the massive data volumes telcos generate from networks, devices, and sensors. As Darringer clarifies, “Because the telcos are very sensitive about their data, the model will be deployed in their environment, whether that’s in their data center, or within their cloud, VPC within their own environment within the public clouds.”
This ensures data privacy while leveraging Kinetica’s GPU-accelerated architecture, as Meley highlights: “We execute on NVIDIA GPUs for inferencing to actually generate the SQL. It will scale linearly with the number of requests and how often they are being used to generate these queries.”
The customized on-premise deployment also mitigates telco concerns around data confidentiality with large public language models. By keeping the fine-tuned model isolated within each telco’s environment, their data remains completely private. As Meley summarized, clients expressed that “they don’t want to take any of their data, even metadata, and expose it to a public API.”
SQL-GPT for Telecom marries natural language simplicity and real-time performance at scale while guaranteeing security for highly sensitive telco data assets. This empowers faster data analysis by more people in the organization and service response to maximize network quality and customer satisfaction.
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