Butler Shen Agent Workers

Solutions for the Internet Industry

Automate information processing and data analysis so your team can focus on creative, high-value work

Solutions for the Internet Industry

Industries：Internet, e-commerce, consulting, SaaS/software, and more

Industry Pain Points：Gathering information, organizing data, and drafting content consume huge amounts of time and crowd out creativity

Efficiency

Information gathering efficiency improved by about 70%

Autonomy

Cross-system data queries shift from "file a request and wait" to "just ask out loud"

Growth

Onboarding time for new hires cut by 50%, helping them ramp up and work independently faster

Real-World Use Cases

Product Manager — Requirement Management

Saves 6.5 hours per week, so product managers can focus on validating requirements and driving innovation

Product Manager

Background：Product managers at a SaaS company spend roughly 8 hours every week collecting user suggestions from the support ticketing system, the user feedback portal, internal collaboration platforms, and other internal channels — and then manually categorizing, deduplicating, and prioritizing them.

  1. 01

    Full coverage, nothing missed

    The AI pulls raw data from internal systems such as the support ticketing platform and user feedback database on a schedule

  2. 02

    Less time spent on manual sorting

    It performs semantic analysis and clustering, automatically classifying feedback into categories like "feature requests," "bug reports," and "UX improvements"

  3. 03

    PMs simply review and adjust

    Cross-referencing the existing backlog and roadmap, the AI proposes an initial priority list (based on frequency, user tier, etc.)

  4. 04

    Total time cut from 8 hours to 1.5 hours

    It outputs a weekly Requirements Analysis Report and archives it in the internal knowledge base for the team

Operations Specialist — Automated Daily Reports and Data Monitoring

Daily report generation cut from 2 hours to 5 minutes; anomalies are now caught the same day instead of the next

Operations Specialist

Background：Operations teams at an e-commerce platform spend more than 2 hours per person every day exporting sales, traffic, and inventory data from the corporate database, organizing it in Excel, and writing the daily report.

  1. 01

    Data pulled automatically

    The AI connects to the enterprise data warehouse on a schedule and extracts the previous day's key operations metrics (GMV, conversion rate, average order value, etc.)

  2. 02

    Generated in under 1 minute

    Using preset templates, it produces a richly formatted Daily Operations Report with trend charts and YoY/MoM analysis

  3. 03

    Operations can read it directly

    The report is pushed to the internal workspace (or email list), with abnormal metrics flagged (e.g., conversion rate dropping more than 10%)

  4. 04

    Helps pinpoint issues

    Ops can follow up with "Why did the conversion rate drop?" The AI cross-references support complaints and product review data to provide an initial analysis

Sales Manager — Self-Service Data Queries

80% of routine data pulls are now self-served by sales; analysts focus on deep analysis, and decision-making efficiency improves by 60%

Sales Manager

Background：Analyst teams at an internet company are flooded with ad-hoc data requests every day. Business users wait 2-3 days for their turn in the queue, delaying decisions.

  1. 01

    Configure once, use forever

    Connect to the company's internal databases (e.g., MySQL) in Butler Shen and configure table mappings and permissions

  2. 02

    AI auto-generates and runs SQL

    A business user asks in natural language: "Last Wednesday to Friday, what was the registration completion rate for new iOS users?"

  3. 03

    Sub-second response

    The system returns the table and automatically generates a line chart, with download support

  4. 04

    Live chart updates — no need to refile a request

    Users can drill in further: "Now break it down by city"