Vector Database & Sentiment Analysis Tool Demo (2026-02-10)

Overview

During the ^[1], Mark demoed an internal client intelligence tool built on a vector database. The tool ingests all internal communications across every platform and uses AI to surface sentiment analysis, risk flags, and activity trends — enabling proactive client management without manually reviewing hundreds of documents.

This tool represents a significant operational capability: account managers can get a real-time read on client health across their entire book of business in minutes rather than hours.

How It Works

The system ingests and indexes communications from:

Email (inbound, outbound, and internal)
Slack
ClickUp tasks
Google Drive (Docs, Sheets, Presentations, PDFs — not images)
Meeting transcripts

Documents are chunked and stored in a vector database, which enables semantic search — you can ask natural-language questions like "when did we last discuss X?" or "what did the client say about Y?" and the system retrieves relevant chunks across all sources.

Key Features

Sentiment Analysis & Risk Flags

For each client, the tool analyzes the last 30 days of communications and produces:

Overall sentiment (e.g., positive and stable, negative, mixed)
Risk flags — specific issues surfaced from the data, such as:
Timeline slippage / overdue deliverables
Tone trend changes (e.g., client becoming more terse or critical)
Longstanding blockers (e.g., unresolved technical issues)
Gaps in communication
Key relationship indicators — characterizes the nature of the client relationship based on language patterns (e.g., "collaborative and appreciative," "transactional")

Example from the demo: The tool flagged ^[2] as having critical issues — Google Ads verification stalled for 12+ months, repeated design asset delays, 132 SEO warnings, and sporadic blog execution — despite an overall positive sentiment score. This surfaced actionable blockers that weren't visible from meeting notes alone.

Activity Dashboard

Each client has a communication activity view showing:

Total document count by type (emails, meetings, Slacks, ClickUp tasks, Drive docs)
Inbound vs. outbound vs. internal email breakdown
Activity trend over time (volume of communication by week/month)

Low activity periods are immediately visible — useful for spotting clients who have gone quiet, which can be an early churn signal.

Client Stream View

A chronological feed of all communications for a given client. From this view you can:

Request an AI-generated summary of the last N days of activity
Open any individual document (email, meeting, task) directly
Download or copy summaries in Markdown format for use in reports or Google Docs

Natural Language Search (In Development)

A "search and ask" interface is being built to allow freeform queries across all client data — e.g., "What did Overhead Door say about their competitor last month?" or "When did we last send a report to Aviary?"

Operational Value

Use Case	How the Tool Helps
Weekly client prep	Pull a 7-day summary before each meeting instead of re-reading email threads
Churn risk detection	Sentiment flags surface dissatisfaction before the client raises it directly
Account handoffs	New AM can get up to speed on relationship history quickly
Reporting	Copy Markdown summaries directly into Google Docs for client-facing reports
Accountability	Overdue deliverables and blockers are surfaced automatically from ClickUp + email

Practical Notes

Summaries are output in Markdown and can be pasted directly into Google Docs via Edit → Paste from Markdown
The tool currently covers ~250+ documents per client across all sources
Vector databases work by chunking documents and retrieving semantically relevant chunks based on your query — not keyword matching
The system is built and maintained by Mark; additional features (ask/search interface) are actively in development as of February 2026

^[3]
^[2] — first client flagged by the sentiment tool with critical issues
^[4]
^[5]