At the 2025 Feishu Project Product Launch, Momenta Senior R&D Director Fan Xiaojun described the company's intelligent-driving R&D workflow. In Momenta's account, the approach was intended to support more vehicle programmes without comparable growth in the R&D team; the actual increase should be checked against verifiable company disclosures.
Momenta says it used Feishu Project to connect vehicle testing, issue, requirement and acceptance management in a traceable workflow. It also co-developed an ASPICE plugin with Feishu Project and Gaoyuan Huaxin, but certification status, scope and suitability for a specific OEM audit should be confirmed with the supplier and auditor. The team describes the approach as moving from “input SPA” toward “output SPA.”
Better AI, Better Life
Founded in 2016, Momenta is an intelligent driving technology company with the mission of "Better AI, Better Life." It strives to create a better life through breakthrough AI technologies, realizing scalable autonomous driving and making transportation safer, more convenient, and efficient.
The company has proposed a "Three-Decade Vision": over successive decades it aims to reduce road casualties, free up more human driving time, and lift logistics and mobility efficiency. Momenta continuously delivers technological solutions for safety assurance, free mobility, and doubled efficiency. Its technologies have been applied in vehicle models from major domestic and international brands such as SAIC, GM, and Mercedes-Benz, making it a preferred partner for many automakers.
Momenta says its “end-to-end flywheel large model” supports its intelligent-driving products. Specific performance, safety and deployment claims require current production and independent test evidence.
A Flywheel with Two Legs Strategy
Momenta implements a "one flywheel, two legs" product strategy. The first leg is intelligent assisted driving, which accumulates massive multi-scenario data through user usage. The second leg is scalable autonomous driving Robotaxi, aiming at pre-installed, mass-produced Robotaxi deployment, which Momenta expects would reduce per-unit costs.
The "one flywheel" refers to the flywheel large model, which operates at high speed supported by massive data, a closed-loop automated toolchain, and data-driven algorithms.
The Flywheel Large Model 5.0, launched in 2023, was described by Momenta as a single-stage end-to-end mass-production large model, introducing a human-like memory mechanism combining "long-term memory + short-term memory." The source article said the R6 model was then expected to enter production in 2024 and used reinforcement learning in simulation. Its current production status, novelty and safety performance should be checked against Momenta's latest disclosures and test evidence.
Since its founding in 2016, Momenta has achieved rapid growth in intelligent driving deployments and partnered vehicle models. The number of mass-production collaboration models has grown rapidly since 2022, with software reaching a widening installed base of vehicles. An OEM or fleet buyer benchmarking ADAS suppliers could ask each vendor for audited deployment and installation figures during due diligence.
Automate Any Task Repeated Two or Three Times
With customer projects and partnered vehicle programmes increasing faster than the R&D team, Momenta attributes its response to changes across software architecture, algorithms, engineering and development processes.
The company advocates the “Input-to-Output SPA” concept: S (Standard) represents standardization, P (Process) stands for process optimization, and A (Automation) means automation. This system ensures accurate requirement input without omissions, high-quality version outputs, and all processes running under a unified framework capable of quickly adapting to customer needs.
Momenta has a rule: any task repeated two or three times must be automated using tools. Although self-developed systems were considered, the team ultimately chose Feishu Project because its platform capabilities closely matched Momenta’s requirements for R&D process tools.
Feishu Project offers two outstanding advantages: first, flexible and efficient workflow configuration, supporting low-code or no-code setup and adjustment of multiple workflows; configuration limits and implementation effort should be tested in a pilot; second, platform openness and secondary development capabilities, enabling seamless integration across all R&D stages via standard interfaces for smooth information flow—with strong adaptability to local scenarios.
According to the source article, Momenta chose to adopt Feishu Project after a two-week trial. This is the vendor case's adoption account and does not establish the same result for other teams.
In addition, Momenta has developed multiple automation plugins to enhance internal R&D efficiency, moving toward highly automated “autonomous driving” in R&D workflows.
Connecting Six Core R&D Processes
1. Traceable Workflow: In Momenta's case, bidirectional links connected customer requirements with delivery records and made the process easier to inspect. Whether a configuration satisfies ASPICE or a particular safety audit must be determined by the relevant auditor from the implemented controls and evidence. Momenta also reports less routine back-and-forth after adoption.
2. Road Test Management System: Built on Feishu Project, this system covers the entire process from test planning and case design to execution. Test tasks are synchronized to in-vehicle displays, allowing safety operators to follow instructions and report issues with one click. The system automatically collects vehicle data, generates issue lists, and produces complete reports. Fewer operators can now supervise multiple vehicles at once. Before adopting a similar setup, a test-operations lead should first measure their own per-operator vehicle coverage as the baseline for comparison.
3. Intelligent Issue Management: In Momenta's account, reported issues can be clustered and an AI pre-analysis can present possible causes in text and image form for human review. Clustering accuracy, error rates, review controls and the model-feedback process should all be tested in a pilot.
4. Efficient Requirement Management: Solves the problem of diverse customer requirement formats and inefficient verification. The system supports one-click batch import of various formats, which Momenta says substantially reduces the time needed to process large requirement batches. Product managers digitally map customer requirements to mainline requirements, allowing R&D teams to focus on core progress with more efficient synchronization and fewer meetings.
5. Multi-Layer Release Gate Mechanism: Establishes strict release gates as safety red lines and the cornerstone of mass production. Every code commit requires code review and unit testing; modules undergo bench validation; multi-module integration is verified through simulation and real-vehicle tests; final mainline versions undergo additional audits before delivery to customer projects, intended to support reliability and compliance in every released version.
6. ASPICE Plugin Certification: Co-created with Feishu Project and Gaoyuan Huaxin, this ASPICE plugin supports common OEM supply-chain process requirements. It supports one-click import of original customer requirements in Reqif or Excel format, establishes document version and baseline repositories covering the entire R&D process, and can generate documentation evidence for review; suitability for a specific audit should be confirmed by the auditor.
Turning Complexity into Simplicity, Ambiguity into Clarity
Momenta summarises the approach as standardisation, process optimisation and automation from input to output. Whether it makes model iteration or R&D work faster, more stable or more accurate should be measured against the team's quality and cycle-time baselines.
Although not perfectly aligned with every expectation (e.g., some metric views require custom plugin extensions), the openness and flexibility of Feishu Project are deepening its collaboration with Momenta, simplifying complex tasks, clarifying ambiguous scenarios, and visualizing invisible work—unlocking infinite possibilities.
Currently, Feishu Project underpins extensive integrations, plugins, and automated API-driven data flows across Momenta's internal systems—scale a CIO can ask to see demonstrated on a live tenant before committing.
Momenta reports internal survey feedback indicating efficiency improvements across most stages. More importantly, the value of digital tools goes beyond mere efficiency. By freeing engineers from repetitive tasks, they can now focus on next-generation intelligent driving model experiments, safer algorithm research, and pushing the boundaries of artificial intelligence. The team aims to redirect time saved from repetitive work toward model experiments and safety research.
This is the true essence of building an “efficiency engine”—freeing human potential to focus on higher-value innovation and thinking.
As CEO Cao Xudong stated, we are now at the cusp of the “Intelligent Driving Moore’s Law,” where rapid large-model iterations are reshaping the industry. Momenta is committed to going beyond Moore’s Law and advancing democratization in intelligent driving.
To achieve industry transformation, it’s not enough to break technological limits—we also need an “equality revolution” driven by ecosystem collaboration, relying on all participants across the industry—from engineers to data labelers, from automakers to suppliers—to jointly improve efficiency and define the pace of technological adoption.
Therefore, we need tools like Feishu Project, as well as collective efforts from the entire industry.
Momenta uses “Better AI, Better Life” to describe its mission and links scalable autonomous driving with safer, more convenient and efficient mobility. Progress toward those goals should be assessed with current production and safety data.
Click here to access highlights from the 2025 Feishu Project Product Launch!