Evolving AI Drivers with AI: UISEE Unlocks End-to-End Autonomous Driving Business Management with AI-Native Approach

2026-07-14

Many enterprises talk about AI-driven efficiency

gains, but the real challenge is often not whether an AI agent can be built,

but whether it can truly integrate into business workflows. In scenarios as

complex, collaborative, and traceability-intensive as autonomous driving, this

challenge becomes even more pronounced.

As a leading enterprise in the commercialization of

full-scenario L4 autonomous driving, UISEE has made defect management within

its R&D system a critical foundation as it scales up autonomous driving

deployment: behind every defect ticket

lies a multi-role workflow, massive data analysis, and an extensive processing

and knowledge accumulation pipeline.

UISEE has long been committed to building an

AI-Native organization where AI serves as "augmented workforce."

Focusing on the highly complex business process of defect management, UISEE has

partnered with Feishu to embed AI directly into business workflows across real

work scenarios, transforming AI from an external tool into an integral part of

daily productivity.

When Large-Scale

Commercial Autonomous Driving Becomes the Norm

In many R&D teams, handling bugs is routine

work: issues are reported from the field, triaged by the central platform, then

analyzed by developers. Teams align back and forth across logs, screenshots,

and comment threads, before finally documenting conclusions back into the

system.

But at UISEE, so-called "defects" are

directly linked to risks in real vehicles, real roads, and real operational

scenarios. Especially as the business continues toward large-scale

commercialization, every anomaly in perception, localization, planning and

control, or system response cannot be handled slowly like ordinary issues—they

must be identified, triaged, and located as quickly as possible.

The difficulty of this pipeline stems not just from

the volume of issues, but from their severity. A single defect ticket often

contains more than just a text description—it comes with images, logs, and

multimodal operational data, with individual issues potentially reaching

several gigabytes of data. Once an issue enters the workflow, the central

platform must first determine ownership, and developers must then download

data, replay scenarios, perform analysis, and verify findings. What gets

consumed continuously is not just time, but also the team's attention.

When issue handling involves frontline intake,

cross-team handoff, in-depth analysis, and result write-back, what teams

typically need is no longer a point solution for efficiency, but a unified

workspace that connects key processes together.

Unified Capture:

Leaving No Defect Unnoticed

In the past, issues from real vehicles, testing,

and delivery sites would first be aggregated into the system. But these issues

would not arrive with clear labels: is it a perception issue, localization

issue, planning and control issue, or system-level anomaly? Which team or

specialist should handle it? This often relied on central platform team members

making judgments based on experience.

At UISEE, "removing safety drivers and

achieving truly unmanned operations" is the core prerequisite for the

business. For this very reason, every anomaly cannot simply be "noted

down"—it must enter a standardized processing pipeline as quickly as

possible.

Based

on Feishu Projects, combined with AI Agent and MCP capabilities, UISEE has

built an intelligent analysis pipeline for autonomous driving defect handling. Clotho, as the engineering execution entity, does

not remain outside the workflow—it directly enters the defect handling process

itself: once issues enter the system, they are uniformly received,

intelligently classified and analyzed, and ultimately consolidated into

structured analysis reports with traceable closed-loop records.

As a result, the central platform no longer needs

to "fish for issues" from scratch. Instead, they receive an initial

structured assessment and then decide whether human calibration is needed.

Frontline actions that previously relied on individual experience are now being

systematized.

Intelligent Triage:

Surfacing Risks First

If classification addresses the question of

"who to assign to first," the more difficult step is understanding

"why the issue actually occurred."

Analyzing autonomous driving defects is rarely

something that can be concluded from a description alone. Developers typically

need to switch back and forth between screenshots, logs, runtime records,

replay tools, and source code information to piece together what happened in

the field.

Within Feishu Projects, issues do not stop at the

recording level once they enter the system. Leveraging MCP's unified access to data and context, the Clotho agent

continues the analysis along the existing workflow, automatically retrieving

relevant data and attachments, supplementing context with multimodal materials

including images and logs, before proceeding to automated processing.

When encountering more complex long-tail risks, the

agent further deploys log analysis and

system-level diagnostic capabilities, cross-referencing across massive runtime

logs and visualized data to generate defect analysis reports faster, improving

defect triage efficiency by 70%. As a result, developers no longer spend

significant time on data extraction, repeated verification, and basic

troubleshooting—they can focus their energy on root cause judgment and

resolution.

In

this pipeline, AI first takes over repetitive, time-consuming, and highly

standardized work.Judgments closer to the root cause remain with the engineers.

Deep Retrospective:

Enabling Continuous Knowledge Evolution

Many teams stop at "solving it this time"

when handling issues. But what UISEE values more is whether this analysis path

can be reused, and whether this processing experience can remain beyond just

being stored in someone's head.

Therefore, after defect analysis is completed, results are written back to defect comments

or processing records in Feishu Projects via MCP, forming closed-loop

information with full context, process, and conclusions. Subsequent

collaborators no longer see just a "processed" status—they can see

how the issue was identified, analyzed, and confirmed. When similar issues

arise again, reference paths can be found more quickly without starting from

scratch every time, with key defect reproduction speed improved by 3x.

The bigger change is that actions that were once highly dependent on individual experience are

now being consolidated into a unified pipeline supported by Feishu Projects: frontline issues enter the system, triage and

analysis are completed within the workflow, and subsequent result write-back

and knowledge accumulation stay within the same collaboration context,

achieving 100% defect traceability coverage. What has been accumulated is not

just efficiency gains, but process certainty.

Through the collaboration of Feishu Projects +

Clotho, UISEE brings AI into real business scenarios, gradually consolidating the defect processing pipeline—once scattered

across multiple people and stages—into a single unified system, building a

quantifiable, traceable, and self-learning quality defense system. The

central platform no longer relies solely on senior expertise to handle the

first round of triage, developers no longer always need to piece together

issues from the most fragmented materials, and analysis conclusions remain in

the system for continuous reuse.

From tool to competitive moat, AI does not replace

people—it maximizes the capabilities of every individual. This is the meaning

and value of building an AI-Native organization.