Slack Communication Diagnostic for B2B SaaS
Case Study Summary
Project Type: Internal Research Prototype
Domain: B2B SaaS (Insurance Sector)
Objective: Detect and visualize team-level communication bottlenecks
Duration: 4 weeks
Team Size: 1 (solo project)
Simulated Metrics:
- 32% faster average handoff completion time
- 18% reduction in message latency between teams
- 42% increase in early detection of “stalled” discussions
- Weekly dashboard adoption by 5 internal team leads (pilot phase)
Challenge
In distributed SaaS teams, misalignment often hides inside communication tools.
Threads go unresolved, handoffs lag across channels, and managers don’t see execution drift until it affects delivery timelines.
The goal of this prototype was to explore how lightweight retrieval and analytics systems could surface those hidden dynamics — without requiring major workflow changes or invasive monitoring.
Approach
I built a Slack-integrated diagnostic bot that:
- Aggregated structured and unstructured message data from multiple workspaces
- Measured reply latency, message depth, and handoff frequency across teams
- Applied clustering to identify emerging silos or recurring “stall points” in conversations
- Generated a weekly summary report with trend visualizations and suggested focus areas
The intent wasn’t to score productivity — it was to expose weak communication edges where alignment was degrading.
Results & Insights
- Faster visibility: Detected potential execution drift an average of 5–7 days earlier than team leads typically noticed manually.
- High interpretability: Managers reported the metrics were “easy to trust” because the system surfaced why a signal appeared — not just that it did.
- Sustained engagement: Dashboard usage remained above 80% during the pilot.
- Behavioral pattern finding: Discovered recurring lag during cross-functional ticket handoffs, often tied to missing context links or unclear ownership.
Solution Overview
Below is a simplified architecture view of how the diagnostic loop worked — from Slack data ingestion to insight generation.
A minimal retrieval and analytics loop connecting Slack APIs, vectorized communication features, and a lightweight trend dashboard.
Tech Stack
- Slack API for data ingestion
- PostgreSQL + Timescale for temporal metrics
- OpenAI Embeddings for context similarity across threads
- FastAPI backend service for data access
- Streamlit for dashboard visualization
- Docker for deployment and reproducibility
- GitHub Actions for lightweight automation
Additional Context
- Focus Areas: Execution drift, team dynamics, alignment metrics
- Constraints: Non-invasive data collection; no message content stored beyond metadata
- Outcome: Prototype validated that simple retrieval and summarization over communication metadata can surface team misalignment patterns early
- Future Exploration: Expand to include Jira and Notion signals for richer multi-channel drift detection
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Learn More
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