Reads the real estate market on YouTube, automatically
We automated real estate market intelligence from YouTube, 97% accuracy, live in a month
97%
Sentiment Lassification Accuracy
0
Manual Steps in the Live Pipeline
1 month
Kickoff to Production
AI & Automation
About The Company
Their product is only as good as the quality and speed of the insights behind it.
The Challenge
A high-value signal, not yet captured
Hundreds of YouTube channels publishing real estate commentary every week: analysts, agents, and investors discussing market shifts in real time. The client could see the volume. They had no infrastructure to turn it into structured data.
Volume ready to be unlocked
Manual tracking was already happening, at limited scale and slow turnaround. The opportunity wasn't fixing a broken process, it was replacing a human bottleneck with a system that runs continuously and doesn't miss a channel.
Specialist language, purpose-built classification
Generic sentiment tools aren't calibrated for real estate vocabulary: market cycles, inventory language, regional pricing signals. Broad positive/negative buckets weren't going to produce insight accurate enough to act on.
Infrastructure to build a data edge on
The goal wasn't analyst hours saved. It was a structured data product - sentiment at a volume and consistency that becomes a competitive advantage over firms still reading the market manually.
The Approach
Collection & Scope
Mapped target YouTube channels, defined what "useful sentiment signal" looked like for the client's use case, and scoped the pipeline architecture.
- Channel list and data access confirmed
- Sentiment categories defined with the client
- Pipeline architecture scoped
Build
Built the scraping and classification pipeline: YouTubeTranscriptApi and YouTubeDL pulling subtitles, OpenAI API classifying sentiment tuned for real estate language, results landing in RDS Postgres.
- Subtitle scraping layer built and tested
- Sentiment classification model configured and tuned
- AWS Lambda scheduling and CloudWatch monitoring set up
Validation & Delivery
Pipeline tested against manual benchmarks. Accuracy confirmed at 97% before handoff. Full documentation delivered so the client owns and can extend the system.
- Validated against manual classification sample
- 97% accuracy confirmed
- Handed over with full documentation
The Results
97%
Sentiment classification confirmed against manual benchmarks before go-live.
1 month
From scoping to validated delivery: one dedicated team, start to finish.
1 pipeline
Single automated system replacing manual channel monitoring across the entire market.
0 manual steps
Every classification now runs automatically - no analyst time spent on collection or tagging.
Ready to work together?
Have data you're not getting full value from yet?
Case Studies
Results that Compound
Machine Learning For Customer Predictions
Company builds health & fitness apps used by millions. We built the ML infrastructure that made their Google Ads spend chase value.
