Client Sentiment Analysis Tool — Google Workspace Integration

Overview

An internally built AI tool that ingests all company communications via the Google Workspace API and uses a vector database to provide real-time client sentiment scoring and surface critical operational issues. The tool was demoed internally on 2026-02-18 and access is being rolled out to the broader team.

First discussed and demonstrated in ^[1].

How It Works

The tool connects to Google Workspace and pulls every communication artifact associated with a client:

• Email (all company inboxes)
• Slack messages
• Call transcripts (via Fathom recordings)
• Google Drive documents

All of this data is embedded into a vector database, enabling both structured filtering and semantic search across the full communication history. The system then runs analysis to produce:

1. Sentiment scoring — overall tone and trend across the relationship
2. Critical issue flags — operational problems surfaced from within the communication record
3. Activity statistics — volume of touchpoints over time, with date-range filtering
4. Tone trend monitoring — tracks changes in client responsiveness, professionalism, and engagement patterns

"It's basically rooting through all your stuff and saying, here's what's going on." — Mark Hope

Key Capabilities

Sentiment Analysis

• Reads every document, email, and call transcript associated with a client
• Produces an overall sentiment label (e.g., Positive, Stable, Declining)
• Tracks tone trend over a configurable date range (e.g., 30 days, 90 days)
• Monitors response latency — flags if a client starts taking longer to reply

Critical Issue Detection

• Surfaces operational problems mentioned or implied across communications
• Examples from the 2026-02-18 demo:
• Citrus America: 32 inactive trade show geofencing campaigns still enabled and wasting budget; Google Ads remarketing inefficiency; conversion action misconfiguration
• Adavacare: Task delivery delays; 3 of 10 locations missing pricing data; photo asset verification issues

Search & Query Interface

• Structured filters: filter by client, person, content type (email, Drive doc, Slack), and date range
• Semantic search: natural language queries like "tell me about budget issues" or "project status"
• AI Q&A: ask freeform questions (e.g., "When was the last time we changed the budget?") and receive answers grounded in the actual communication record

Activity Dashboard

• Per-client statistics card showing total email, Slack, Drive, and call volume
• Activity timeline to identify periods of high/low engagement
• Useful for spotting relationship drift before it becomes a churn signal

Important Limitations

• Sentiment reflects stated tone only — if a client is being politely positive while privately dissatisfied, the tool will not detect it
• Not a campaign analytics tool — it does not analyze ad performance numbers, CRM data, or billing figures; it only processes communication content
• Complementary to, not a replacement for, X-Ray — X-Ray handles structured performance data; this tool handles unstructured communication data
• Still in active development — the AI Q&A ("Ask") feature is noted as not yet fully reliable; onboarding flow had minor login issues at launch

Access & Rollout

• Built by Mark Hope
• Access being extended to Melissa Cusumano and others as of 2026-02-18
• Login is via invite; password reset flow had minor issues at initial rollout (Mark is aware and monitoring)
• Contact Mark to request access or report issues

Example Outputs

Citrus America (run: ~2026-02-18)

Adavacare (run: ~2026-02-18)

Strategic Value

The tool addresses a specific blind spot: clients can appear healthy in calls and emails while operational problems accumulate underneath. The Adavacare example is illustrative — the client's communication tone was positive and appreciative, yet the tool surfaced four urgent delivery issues that required attention.

This makes it particularly useful for:
- Pre-call preparation (know what's broken before the client brings it up)
- Account health monitoring at scale across the full client roster
- Early churn detection via tone trend and responsiveness monitoring
- Internal accountability (task delays become visible in the communication record)

Related

• ^[1]
• ^[2]
• ^[3]
• ^[4]