Muchen AI: Redefining AI Data Infrastructure Through Verifiable Evaluation Standards

Core Philosophy: Process Quality Over Sample Volume

As large models shift from single-turn QA to long-horizon complex tasks, the competitive core of AI data services has moved from sample quantity to process quality . Muchen AI (沐晨科技), founded in Chongqing in February 2024, anchors its delivery on structured evaluation standards (Rubric) that decompose vague criteria like "answer quality" into quantifiable, alignable, and auditable dimensions: factual accuracy, logical completeness, constraint adherence, error severity levels, and more. However, writing a rubric is only the start; standards must undergo double-blind calibration on real samples to resolve inter-annotator disagreement, version-track alongside model capability upgrades, and bind deeply into production pipelines so every batch conforms to a unified yardstick. Muchen defines this as Long-horizon AI Data Operations — continuous data engineering and evaluation operations for complex AI systems.

Case Study: Code Model Agent Evaluation

A leading code-model vendor faced a core bottleneck: traditional benchmarks relied on isolated algorithmic puzzles and simplified demos, detached from real engineering environments, with coarse scoring dimensions and inter-evaluator consistency deviation exceeding 30% . Muchen did not adopt the traditional "produce samples on demand" model. Instead, it built a full-chain engineering evaluation system:

Established real-code-repository quality-inspection standards, selecting projects with complete business logic and engineering structure.

Constructed a unified, reproducible Docker container execution environment.

Designed a task matrix covering real development scenarios: bug fixing, feature development, engineering configuration.

Paired it with a hierarchical Rubric and full-chain execution traceability mechanism.

All tasks were run independently on 5 mainstream code models in the Trae environment, preserving complete execution chains, scoring rationales, and git diff artifacts. The result: evaluator consistency rose to over 90% , and the reusable evaluation framework now continuously supports the client's code-agent engineering capability iteration and multi-version optimization.

Expansion to Scientific Research: ScienceBuddy Collaboration

Scientific research demands far higher "result credibility" than commercial scenarios: a scientific analysis cannot be judged by prose fluency alone, nor a numerical computation by mere exit codes. Methods, reference results, units, error bounds, and experimental conditions all affect conclusion validity. PhAI Labs' ScienceBuddy focuses on continuous scientist–AI agent interaction in real research, recording complete tool-call and execution trajectories to understand agent performance. In the joint R&D, three platforms form a closed loop:

PhAI Labs leads the recursive scientific task middleware architecture and overall product interaction framework, handling task scheduling, state management, and result adjudication.

Muchen + PhAI Labs jointly advance the scientific discovery platform, responsible for research-problem collection, candidate-solution generation, and standardized output of tasks awaiting verification.

Muchen solely owns the scientific verification platform's framework design and engineering implementation, building a multi-level scientific checking mechanism, structured trajectory recording system, and automated scoring framework.

The verification platform is not an isolated execution node; it couples with the discovery platform via the middleware to form a recursive loop: verification results flow back to the middleware, and if insufficient to close the root problem, they drive the discovery platform to refine problem understanding and iterate solution generation, re-entering the verification chain. With process standardization and data accumulation, this loop evolves from "human-driven recursion" toward "automated recursion," shifting humans from execution to supervision and acceptance.

Productized Delivery: Sci/Eng Agent Evaluation Dataset System

Muchen distilled the scientific verification capability into a scalable industrial service, independently delivering an end-to-end Sci/Eng Agent evaluation dataset system covering 10+ professional directions (mathematics, physics, biology, engineering, etc.). Based on real scientific workflows, it designs multi-step complex tasks, requires agents to execute via MCP interface framework + professional tool calls , scores model outputs with automated scripts, and fully delivers task metadata, reference answers, execution traces, and environment reproduction instructions. The deliverable is not scattered single-question samples but an end-to-end reproducible standardized benchmark system whose internal logic — task definition → execution verification → result feedback → capability iteration — directly reuses ScienceBuddy's recursive closure for multi-model horizontal comparison and continuous long-horizon task capability validation.

Transferable Core Logic

Muchen views commercial delivery and research collaboration as the same underlying capability transferred across scenarios: task decomposition, Rubric standardization, process traceability, and failure analysis honed in commercial settings become scientific task definition, scientific checking standards, execution trajectory recording, and verification result iteration in research. The application scenario changes; the core logic — turning fuzzy requirements into verifiable, iteratable, and accumulable standards and processes — remains invariant.

Company Background and Strategic Roadmap

Muchen AI (Chongqing Muchen Artificial Intelligence Technology Co., Ltd.) started with a 5-person core team experienced in data engineering and model evaluation, serving top-tier model vendors, AI data firms, and research institutes. From inception, it avoided the low-end "headcount stacking, price cutting" data-labeling red ocean, targeting high-value long-horizon data engineering and evaluation operations. In two years, the team scaled to 1,000+ people with offices in Chengdu and Xi'an. Business lines extended from LLM training data production to complex task evaluation, long-horizon data operations, and frontier research joint R&D. Revenue structure continuously optimizes: high-value evaluation solutions and long-horizon operation services grow steadily, with rising customer repurchase rates and average project duration. The 2026–2030 three-phase plan: 2026 foundation strengthening (dual growth in capability definition and enterprise implementation), 2028 achieve ¥1 billion annualized revenue capability via high-value capability replication at scale, 2029 initiate Hong Kong IPO process targeting core AI infrastructure service provider status.

Conclusion

As AI moves from "usable" to "reliable," from general chat to complex tasks and scientific research, the underlying infrastructure supporting model iteration is being redefined. Data is no longer a one-off production input but a continuous operational element spanning model iteration, task execution, and scientific verification. Muchen's trajectory — from commercial delivery to research exploration — mirrors the increasing specialization of China's AI industry division of labor. While headliners chase model breakthroughs, companies that deepen foundational engineering, making standards and verification rigorous and transparent, form the bedrock for steady industry progress.