How Baidu’s AI Code Assistant Boosted R&D Efficiency by Over 11% in Marketing Platforms

In the rapidly evolving commercial landscape, effective marketing platforms must deliver high‑quality services and precise ad targeting, which puts increasing pressure on underlying R&D processes. The Baidu Marketing Service team responded by rebuilding its product with the Wenxin large model and adopting the Baidu Comate intelligent code assistant to accelerate AI‑native development.

Comate integrates large‑language‑model capabilities into the coding workflow, supporting over 100 programming languages and more than 10 major IDEs. It provides real‑time code recommendations, generation from natural‑language comments, and a full‑lifecycle assistance platform. By September of the previous year, more than 95% of the team’s engineers were using Comate, with AI‑generated code accounting for 29.42% of total output.

Performance measurements show that Comate delivers code‑completion suggestions within 300 ms. Engineers writing a single line of code without assistance average 16.54 seconds, while adopting a recommendation reduces this to 1.74 seconds. Assuming a 40% adoption rate, the calculated efficiency gain per engineer is:

Adoption efficiency = (1 – 1.74 / 14.8) × 40% ≈ 35%

Non‑adoption loss = 1.74 / 14.8 × (1 – 40%) ≈ 7%

Net daily productivity increase per engineer = (35% – 7%) × 40% ≈ 11.2%

Beyond line‑by‑line coding, Comate enables rapid generation of scripts. For example, engineers can describe a desired Shell script in natural language, and Comate produces a functional script within seconds, eliminating the need to search existing repositories.

The Comate Open Platform extends these capabilities through customizable integrations. Teams can connect third‑party tools, internal knowledge bases, and Retrieval‑Augmented Generation (RAG) services, turning Comate into an intelligent search and code‑generation engine across the organization’s ecosystem.

One concrete use case involved streamlining a multi‑platform deployment workflow that previously required manual steps across eight systems (IDE, Git, Maven, project management, CI/CD, and various deployment platforms). By encapsulating the process in Comate, a single command such as “deploy dev” now completes the entire update in under five minutes, saving more than ten minutes per operation. At an estimated 2,800 monthly deployments, this translates to roughly 467 saved work hours, equivalent to 2.5 full‑time engineers.

The AI‑native marketing product “Qingge” (Light Boat) leverages generative AI to translate natural‑language marketing intents into audience targeting, creative generation, and ad optimization. Early internal testing showed a 23.3% increase in ad conversion and a 22.7% boost in ROI for a major IT education group.

Overall, the Baidu Marketing Service team’s adoption of large‑model technology and the Baidu Comate assistant demonstrates a measurable improvement in R&D efficiency, product innovation, and marketing effectiveness, illustrating a successful transition to AI‑native development practices.