Avidia delivers instant AI-driven insights from call tracking, transcriptions, bookings, and marketing data, no dashboards, no manual reports.

SAN FRANCISCO, CA, UNITED STATES, December 30, 2025 /EINPresswire.com/ -- AvidTrak announced the launch of Avidia, an agentic AI data analyst designed to answer business and marketing performance questions directly from a company’s call tracking and connected data sources.

Avidia is built to reduce the manual work traditionally performed by data analysts, such as pulling reports, applying filters, and building dashboards. Instead of navigating reporting interfaces, users can ask Avidia questions in natural language and receive responses generated directly from their business data.

Avidia operates as an agentic AI, meaning it performs analytical tasks on behalf of the user. Rather than presenting static dashboards, Avidia retrieves, processes, and summarizes data dynamically in response to user queries. Common questions include how many completed calls exceeded a specific duration, how many appointments were booked during a given period, which marketing campaigns generated the most phone calls, or which customer service representatives completed the highest number of bookings.

The system accesses data from AvidTrak’s call tracking platform, including call logs, AI-powered call transcription, and conversation outcome extraction. When integrated with customer relationship management systems and advertising platforms such as Google Ads, Avidia can also help users evaluate which marketing tactics and website pages are contributing to phone-based conversions and downstream revenue.

Avidia supports a set of pre-configured questions for common business use cases, such as appointment tracking and campaign performance, while also allowing users to ask custom questions. The platform learns from user prompts over time, improving its responses as it becomes more familiar with the structure and context of a company’s data.

Powered by large language models, Avidia allows users to select the AI engine they prefer. Supported models include LLMs from OpenAI, Google Gemini, Grok, and DeepSeek. Users can change models based on internal preferences while maintaining the same underlying data source, which remains the organization’s own business data.

One of the primary benefits reported by early prospects is the elimination of dashboard-building workflows. In many organizations, dashboards require coordination between business teams and IT resources. Avidia removes that dependency by delivering answers on demand, without tickets, custom queries, or predefined reports.

“Avidia allows teams to interact with their data directly, without relying on dashboards or manual analysis,” said a spokesperson for AvidTrak. “It functions as a dedicated data analyst that is always available to answer questions using the company’s own data.”

Avidia is designed for businesses and agencies that rely on phone call leads and require fast access to operational and marketing performance data without manual reporting overhead.

About AvidTrak:
AvidTrak is a call tracking and marketing attribution platform that helps businesses and agencies measure, route, and analyze inbound phone calls across digital and offline marketing channels. The platform supports keyword-level call attribution, dynamic number insertion, advanced call routing, AI-powered transcription, and conversation analytics. AvidTrak integrates with major advertising platforms, analytics tools, and CRMs to provide clear visibility into phone-driven conversions. The company is known for transparent pricing, responsive product support, and more than a decade of experience solving complex call tracking and attribution challenges.

Amin Haq
AvidTrak
+1 800-818-1591
[email protected]
Visit us on social media:
LinkedIn
Facebook
X

Legal Disclaimer:

EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.

Information contained on this page is provided by an independent third-party content provider. Frankly and this Site make no warranties or representations in connection therewith. If you are affiliated with this page and would like it removed please contact [email protected]