Let Business Data Proactively Find the Right People
Operations teams spend much of their time not analyzing data but 'finding data' and 'moving data' — exporting from CRM, processing in Excel, taking screenshots for group chats. FIM Agent delivers data automatically to those who need it.
Data retrieval is daily manual labor
Every morning, operations staff perform fixed routines: log into CRM, filter by region/product line, export Excel, calculate period-over-period changes, create charts, screenshot or copy to documents, share in work groups. When holidays or travel interrupt, data broadcasts stop. This isn't analysis — it's data transport.
Executive ad-hoc queries can't be answered instantly
'What was East China's signing amount last week?' 'How are the top ten clients' contracts progressing this month?' — these questions come up frequently in meetings. Operations need to return to their desk, open systems, pull data, and calculate before responding. By then, the meeting may have ended.
Comprehensive business analysis requires merging multiple data sources
Sales data in CRM, customer complaints in ticketing, marketing spend in ad platforms, financial collections in ERP. Compiling a complete monthly business report means separately exporting from four or five systems, then manually merging and validating in Excel.
Scenario A
Scheduled Data Broadcasts
Daily at 9 AM (configurable), Agent auto-executes: pulls previous day's business data from CRM/database via connectors, aggregates by preset dimensions (region, product line, customer, amount, status), LLM generates analytical summary with key metrics, period comparisons, and anomaly flags, pushes to designated Feishu/WeCom groups as rich cards. Operations staff just review the push and investigate anomalies when needed.
Scenario B
Natural Language Instant Queries
Operations or managers @Agent in group chats: 'What was East China's signing amount last week?' 'Top 10 clients' contract progress this month?' 'Q1 regional collection completion rates?' Agent auto-converts questions to data queries, executes, and returns results. Supports follow-ups and drill-downs: 'Which ones are uncollected? What are the details?'
Scenario C
Multi-Source Data Aggregation
For cross-system comprehensive analysis: Agent simultaneously pulls data from CRM, ticketing system, and financial system, processes in the built-in Python execution environment, and LLM generates comprehensive analysis reports.
Operations staff freed from data transport
Daily and weekly reports auto-generated and pushed. Team's attention shifts from 'how to get data' to 'what does data tell us.'
Data response time drops from hours to seconds
Managers ask questions in group chats using everyday language, Agent responds instantly. No waiting for operations to return to their desk and manually query.
Multi-source data auto-aggregated
No more separate exports from multiple systems and manual merging in Excel. Agent pulls cross-system data, processes and presents it in a unified way.