About Us
Made with love in Brooklyn
StrawberryData is in active MVP: we ship fast, learn fast, and keep both products improving. Free tiers lean on straightforward insights. Cleaner & more sophisticated AI/ML-powered insights are available when you need them - reach out to tailor the stack.
Our Background
We bring experience from leading tech companies
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WayfairCG
CarGurusH
HubSpotData Platform
StrawberryData specializes in podcast analytics, media intelligence, and more. We transform unstructured datasets from thousands of sources into structured, actionable data that powers better decisions across PR, investor relations, competitive intelligence, and risk management.
General Platform Methodology
How we turn sources into actionable intelligence
Step 1
Data Ingestion
We continuously aggregate sources from thousands of vetted and trusted sources in the news, media, & podcast landscape in near real-time. Source-specific adapters normalize formats, validate freshness, and keep the inputs clean and consistent across products.
Step 2
Cleaning & Prepping
Our pipeline identifies and groups duplicates using similarity scoring, content hashing, and timing. We also have custom cleaning procedures to standardize across sources.
Step 3
Processing & Sentiment
We use a variety of methods that may include Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to turn raw news and podcast text into structured, searchable signals - topics, key entities, tone, and sentiment - so teams can compare coverage, spot shifts, and act faster. The free tier uses a simple method, while more sophisticated processing is exclusive to our Pro solutions - contact us to learn more.
Step 4
Aggregation & Delivery
Multi-layer analytics rolled up results by topic, source, and time to power dashboards across the Podcast Analytics Platform and Media Intelligence Platform.
Transparency & Limitations
We believe in being upfront about what our data can and cannot do
- Sentiment analysis is DIRECTIONAL, not absolute truth. Models can miss sarcasm, nuance, and cultural context. We use a simpler model in the free platforms that you see.
- Coverage depends on source availability. Paywalled or closed ecosystems may be underrepresented.
- Historical data is limited to our collection start date. We cannot retroactively analyze older articles.
- Entity detection may occasionally misclassify articles. We continuously improve our models.
- 'If you're not embarrassed by the first version of your product, you've launched too late.' - Reid Hoffman. We don't believe in allowing perfection be enemy of the good.
Have questions about our methodology?Reach out to us
Ready to explore the data?
Try our free views or reach out for Pro & Custom solutions.