A leading North American wireless telecom provider partnered with us to transform how complex legal contracts are processed. They wanted to eliminate manual, error-prone processing of complex legal contracts to accelerate network expansion decisions and unlock value from previously untapped contract repositories.

Tredence deployed its ATOM.AI Document Intelligence platform on Databricks to automate extraction, insight generation, and report creation across hundreds of thousands of legal documents at scale. Built as a reusable, enterprise-grade architecture, the solution enables faster, more confident decision-making across the network rollout lifecycle and positions the telco to extend GenAI-driven document intelligence beyond this use case. Achieving an 80% reduction in decision cycle time is the kind of measurable business outcome that leading telecom AI consulting services providers, such as Tredence, commit to delivering before an engagement begins.

A leading American media and entertainment company sought to reimagine its advertising sales operations through a unified, AI-powered planning platform spanning linear TV and connected TV.

Tredence, a leading media analytics solutions provider in the US, built a single intelligence layer that equipped sales teams with real-time inventory visibility, scenario planning capabilities, and cross-channel campaign management tools. The solution enabled faster deal closures, more effective upselling of premium ad inventory, and a scalable foundation for converged linear and digital advertising.

A leading American telecommunications provider wanted to reimagine its network planning and operational efficiency through a repeatable, intelligence-driven platform.

Tredence built a Databricks-led solution spanning AI Query, Databricks ML, Lakebase, and Unity Catalog to unify network data into a single, high-visibility intelligence layer—enabling faster planning turnarounds, smarter decommissioning of non-performing assets, and a scalable foundation for ongoing operational optimization.

A leading U.S. telco sought to quantify the impact of network improvements on its existing customer base, new customer acquisition and identify network sites or area anomalies to identify network upgrades and precision deployments.

Tredence developed a data foundation on DBX, Azure to store and analyze network topology, telemetry data, customer usage, customer sentiment in the region, competitive landscape from a network standpoint, pricing, and promotion data. Tredence deployed a causal model based on this framework for individual markets to calculate the impact of network improvement on net activation and define the RoI for capex on network. Running causal models at the individual market level delivers the precision and actionable insight that organizations increasingly expect from leading telecom analytics consulting companies like Tredence.

A top US telco leader wanted to understand key drives of customer disconnect to reduce attrition rates, which cost it millions in lost revenues and profitability. The telco sought to launch targeted interventions, offering high-risk customers relevant products and offers to keep these accounts.

Tredence built a model on ADF and Snowflake to identify at-risk customers and their disconnect drivers. The propensity models assign customers a disconnect score, align key drivers with recommended actions, and provide trend analytics. It integrates with existing enterprise resource planning (ERP) and customer relationship management (CRM) systems to enable account-level actions and provides a playbook that categorizes accounts and actions to reduce churn.

The wireless company sought to leverage journey analytics to uncover behavioral shifts and optimize marketing strategies.

Tredence built and deployed a customer journey analytics and insights tool. The new tool enables cross-functional teams to track and analyze shifts in customer behavior to identify key trends and drivers and monitor and visualize customer journeys to pinpoint and address issues. The result is a scalable, AI-driven retention framework that empowers teams to act with greater precision, consistency, and speed. Delivering this level of predictive retention intelligence is increasingly a defining capability of leading telecom AI services companies like Tredence.

A SaaS company sought to understand the impact of marketing spend on converting leads into sales prospects through the marketing funnel and across paid marketing channels. The company’s leaders wanted to create insights and integrate marketing and sales systems to reduce wasted spend and convert more leads into customers.

Tredence developed extract-transform-load (ETL) data routines and an API to harmonize spend and impression data from partners. We implemented a self-serve insights platform to generate attribution, channel, and campaign insights, spanning 10 spend channels and three global regions. A what-if scenario builder helps teams optimize budgets across channels to reach their target goals.

A US publishing firm lacked insights and analytical applications to track audience engagement and conversion. Its leaders wanted to build a recommendation engine for the company’s media sites that would ultimately scale across 400 brands, 725 websites, and 42 different verticals. Marketing measurement datasets exceeded 800 million rows.

Tredence enabled a common AWS data platform account with cloud best practices. We then built and deployed a recommendation engine powered by AWS Personalize and Python for 100+ media sites. Delivering personalization at scale across 100+ media properties showcases the expertise that organizations expect from media analytics consulting companies like Tredence.

A leading gaming company grappled with fragmented on-premise systems, limited data accessibility, and delayed insights—hindering agility and growth. They needed a modern, scalable foundation to unlock real-time visibility, streamline reporting, and improve compliance.

Tredence’s partnership with this interactive gaming company began with a Google Cloud Platform (GCP) data modernization effort but evolved into a transformative multi-cloud initiative. Tredence solidified GCP’s role as a growth driver by enabling them to maximize its data potential while increasing business flexibility. This transformation highlights how media analytics consulting companies like Tredence enable enterprises to turn modern data platforms into strategic business assets..