📋 The Brief

The Problem: Brand Monitoring Done Manually

A mid-sized PR agency managed brand reputation for 14 clients. Their monitoring process was manual: a team member would check each platform every morning, screenshoting relevant mentions, copying them into a spreadsheet and then writing a weekly summary report. Per client, this was taking 3–4 hours a week. Across 14 clients, it consumed more than a full working day — every single week.

Worse, the manual process was inherently backward-looking. By the time a negative sentiment spike was noticed, it was often hours old. A PR crisis that needs managing in the first two hours looks very different from one that's been building for a day. The agency had no early warning system — only hindsight.

The brief: build a platform that monitored all relevant platforms continuously, classified sentiment automatically, surfaced trending topics in real time and generated the weekly reports the account managers were writing manually. The goal was to turn a reactive, labour-intensive process into a proactive, automated one.

❌ Challenge
Manual monitoring 6 platforms per client, per day — 3–4 hours per client per week, entirely non-scalable
✓ Solution
Automated 24/7 multi-platform ingestion — every mention captured, classified and stored within minutes
❌ Challenge
No early warning on sentiment spikes — crises were discovered hours late, when the damage was already done
✓ Solution
Spike detection algorithm alerts account managers by Slack/email within 15 minutes of unusual sentiment patterns
❌ Challenge
Weekly reports took 2–3 hours to compile per client — copy-pasting data, writing summaries, formatting PDFs
✓ Solution
Automated report generation using GPT-4o — branded PDF with charts, commentary and recommendations, every Monday at 8am
🤖 How Sentiment Works

AI That Understands Context, Not Just Keywords

Keyword-based sentiment scoring ("positive word = +1, negative word = -1") is notoriously unreliable for brand monitoring. Sarcasm, nuance and industry-specific language defeat it constantly. We built the sentiment pipeline on fine-tuned GPT-4o that reads the full post in context — understanding that "this is painfully good" is positive, and "great, another outage" is negative.

📥
Ingest
Mention collected from platform API or scraper with full text, author, timestamp and engagement metrics
Multi-platform
🧹
Pre-Process
Deduplication, language detection, spam filtering and relevance scoring — irrelevant brand mentions filtered out
Noise removed
🧠
AI Classify
GPT-4o classifies sentiment (positive/neutral/negative) and extracts topic tags — context-aware, handles sarcasm
94% accuracy
📊
Aggregate
Metrics rolled up per brand, platform, topic and time window — trend detection and spike alerts triggered automatically
Real-time
📦 Platform Features

Everything a PR Team Actually Needs

📡
Multi-Platform Ingestion
Continuous mention collection from X/Twitter, LinkedIn, Reddit, Instagram, YouTube and news/blogs — all flowing into one unified pipeline.
🎭
AI Sentiment Classification
GPT-4o classifies every mention as positive, neutral or negative — with topic tagging and context understanding that keyword scoring can't match.
🚨
Real-Time Spike Alerts
Anomaly detection on mention volume and sentiment ratio. Alerts sent via Slack and email within 15 minutes — before a PR issue becomes a PR crisis.
📊
Brand Dashboard
Per-client dashboards showing sentiment trends, platform breakdown, top mentions by reach, competitor comparison and share-of-voice metrics.
📄
Automated Weekly Reports
GPT-4o generates branded PDF reports every Monday — summary, trend analysis, notable mentions, recommendations. Account managers review and send, not write.
🏆
Competitor Benchmarking
Track competitor brand mentions and sentiment alongside client data — share-of-voice charts, sentiment comparison and competitive intelligence delivered weekly.
⚙️ Tech Stack

Technologies Used

Python 3.11 Scrapy + Playwright GPT-4o (sentiment) Twitter / X API Reddit API (PRAW) YouTube Data API Apache Kafka PostgreSQL + TimescaleDB Redis React + Recharts Slack API AWS EC2 + S3
📅 Timeline

Live Across All Platforms in 9 Weeks

1
Week 1–2
Platform Audit & Data Model
Assessed API availability and rate limits for all 6 platforms. Designed unified mention schema. Agreed list of 14 client brands and their keyword/hashtag tracking rules. Sentiment accuracy baseline established.
2
Week 3–5
Ingestion Pipeline + Sentiment Engine
Kafka-based ingestion pipeline live for X, LinkedIn and Reddit. GPT-4o sentiment classification tuned on 1,000 manually labelled examples from the agency's own archive. 94% accuracy validated before production deployment.
3
Week 6–7
Dashboard + Alert System
React dashboard built with per-client views, platform breakdown, trend charts and top mention feeds. Spike detection algorithm configured and Slack alert integration live — first real crisis alert triggered and caught within 12 minutes.
4
Week 8–9
Instagram, YouTube, News + Automated Reports
Remaining 3 platforms added. Automated report generation live — GPT-4o drafts weekly PDF, account manager reviews in 15 minutes instead of writing for 2 hours. First automated report delivered, approved without edits.
📈 Results

From Reactive to Proactive in 9 Weeks

The shift from manual monitoring to the platform changed the agency's relationship with brand risk. In the first month after launch, the spike detection system flagged three emerging negative sentiment clusters — all caught and addressed before they escalated. Under the previous process, all three would have been discovered the next morning at the earliest.

8hrs
Saved per client per week — across 14 clients, that's 112 hours/month
94%
Sentiment classification accuracy — validated against manual labels
3
PR crises caught and managed in month one — all would have been missed

The automated report generation deserves a specific callout. The account managers were initially sceptical — they expected to spend as much time editing the AI-generated reports as they previously spent writing them. In practice, the first batch of reports was approved with minimal edits by all 14 account managers. The AI-generated narrative matched the quality they were producing manually, at zero time cost.

The agency has since used the platform as a differentiator in pitches — offering a level of brand monitoring granularity and speed that competitors using manual processes simply can't match. Three new client wins in the first quarter were directly attributed to the platform demo.