Enterprise Brand Monitoring & OSINT Platform
Designed and deployed a comprehensive online brand intelligence tool that identifies and catalogs every brand mention across the web in real-time. The platform uses a custom-trained AI model to assess sentiment, threat level, reach, and potential business impact of each mention. Automated alert pipelines notify teams via email and Slack when high-priority threats are detected. The system includes identity attribution capabilities — cross-referencing digital footprints, authorship patterns, and account histories to help identify who is behind coordinated negative campaigns targeting a brand's reputation.
The Challenge
What was breaking
Off-the-shelf social listening tools flood teams with noise and miss the mentions that actually damage the brand. By the time a coordinated attack surfaces in a weekly report, the damage is already done.
Keyword-based listening misses real threats
Legacy tools surface thousands of neutral mentions per day while the handful of genuinely dangerous ones — coordinated attacks, executive targeting, narrative manipulation — get buried.
No attribution layer
Even when threats were found, there was no way to answer the question that actually mattered: who is behind this, and is this organic criticism or a coordinated operation.
Response time measured in days
The loop from mention-posted to security-team-notified was slow enough that hostile narratives compounded across platforms before any human saw them.
Reach and business impact untracked
Existing tools reported volume. They could not distinguish a five-follower complaint from a threat that was about to break into mainstream press.
Our Approach
How we solved it
We built an AI-native monitoring stack from the crawl up. Real-time collection across news, social, forums, and review sites feeds a custom fine-tuned classifier that scores every mention on sentiment, threat severity, reach, and business impact. An attribution engine cross-references behavioral signatures, writing style, posting cadence, and account creation histories to flag coordinated campaigns and connect activity back to operators. Anything above threshold triggers a structured Slack and email alert within minutes — with full context, evidence, and a recommended response path.
Architecture
How the system works
Crawling
Continuous collection from news outlets, X, Reddit, Facebook, Instagram, forums, and review platforms through a mix of APIs and custom pipelines.
Classification
A fine-tuned classifier scores sentiment, threat type, and authenticity. GPT-4 handles semantic evaluation on ambiguous cases.
Threat Scoring
Composite score combining severity, audience reach, velocity, and estimated business impact determines alert priority.
Attribution
Behavioral clustering and linguistic fingerprinting across accounts surface coordinated campaigns and map them back to likely operators.
Alerting
High-priority threats trigger Slack and email alerts with full evidence, attribution context, and recommended response actions.
The Impact
Before vs. after
Outcomes
Beyond the headline numbers
50,000+ mentions ingested and classified per month
94% threat detection accuracy validated against labeled test set
12-minute average alert latency from post to Slack notification
Coordinated campaigns detected and attributed across 6 platforms
Evidence packages formatted to accelerate platform T&S escalations
Security teams shifted from reactive monitoring to proactive response
Takeaways
What transferred
The scarce resource in brand intelligence is not data — it is judgment applied in minutes instead of days. An AI-native stack that classifies, scores, and attributes in a single pipeline turns social listening from a reporting function into a live operational capability. The attribution layer is what converts awareness into action.
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