Pinewood.AI Unveils Groundbreaking Autonomous AI Agent for Automotive Retail
Pinewood.AI, a leading cloud-based technology provider for automotive retailers and original equipment manufacturers (OEMs), has announced the debut of its innovative solution code-named Project Intelligence (Pi). This autonomous AI agent represents a significant advancement in automotive retail technology, designed to execute operational tasks across dealership and OEM systems without requiring traditional integrations.
A New Category of Automotive Intelligence
Debuting at the NADA 2026 conference in Las Vegas, Project Intelligence moves beyond conventional chatbots and vehicle recommendation systems to automatically perform complex digital tasks on behalf of dealership teams. Unlike traditional AI tools that rely on application programming interfaces (APIs), integrations, or structured data pipelines, Pi operates natively within the Pinewood Automotive Intelligence Platform and works directly through existing browser-based systems.
The revolutionary aspect of Pi lies in its ability to function at the browser level, completing any task that a human could accomplish through a web interface. This includes logging into portals, navigating workflows, completing forms, extracting data, and making decisions across disconnected platforms—all performed autonomously, continuously, and without manual errors.
How Project Intelligence Transforms Dealership Operations
Developed by Seez, Pinewood.AI's automotive AI division, Pi operates through two primary workflows. First, it functions as a browser agent that can interact with web interfaces just like a human operator. Second, it leverages Pinewood's Model Context Protocol (MCP) servers to execute actions deep within the Pinewood system architecture.
This dual approach enables dealerships to streamline everyday operations while freeing employees to focus on higher-value strategic priorities such as vehicle sales, customer support, and revenue growth initiatives.
Addressing Operational Friction in Automotive Retail
"Dealership teams lose countless hours each day to repetitive, system-to-system work that adds no value for customers," explained Bill Berman, CEO of Pinewood.AI. "Pi changes that dynamic by acting as a digital worker inside the systems dealers already use, handling operational friction so teams can focus on selling vehicles, supporting customers, and growing revenue."
The autonomous capabilities of Project Intelligence include:
- Task execution across dealership and OEM systems, including logins, navigation, form completion, and multi-step workflows
- Cross-system coordination without APIs or custom integrations, operating directly through existing browser interfaces
- Real-time decision-making that adapts to changing screens, prompts, and workflows as tasks progress
- Reduced operational friction by eliminating manual data entry and repetitive back-and-forth between disconnected platforms
Advanced Architecture and Implementation
Built with a multi-agent architecture and powered by large language model (LLM) reasoning capabilities, Pi continuously perceives on-screen context, determines the next best action, and executes tasks in real time until objectives are complete. For added control and oversight, the solution incorporates human-in-the-loop functionality, allowing staff to monitor progress or take over instantly when needed.
Perhaps most importantly for dealership implementation, deployment of Project Intelligence does not require changes to existing dealer infrastructure or workflows. The system works directly within the Pinewood.AI Platform, making adoption seamless for independent dealers, dealer groups, and OEMs alike.
Following its debut at NADA 2026, Project Intelligence will soon become available to customers, addressing some of the most time-consuming operational bottlenecks in automotive retail. This represents a significant step forward in how artificial intelligence can transform traditional business operations through intelligent automation that works within existing systems rather than requiring extensive modifications.
