Collect & normalize
Modular adapters ingest permitted public posts, comments, interactions, timestamps and available metadata into one common schema.
UPDATESReal Time. Verified.
Useful intelligence starts
with honest uncertainty.
UPDATES turns noisy public conversation into evidence-backed summaries while showing what the system knows, how it knows it, and where inference remains limited.
Explore the demo analysisOur intelligence pipeline
From public signals to explainable insight
We do not send raw social media directly into a sentiment model. Every stage has a defined responsibility and confidence boundary.
Qualify the signal (CSQE)
Relevance, duplicates, spam, emoji-only replies and contextless content are scored before analytics. Low-signal data is retained but excluded or down-weighted.
Understand in context
English, Hindi and Hinglish analysis combines the topic, parent discussion and nearby replies where available. Model providers remain replaceable.
Explain change & spread
Timestamped trends and aggregate interaction graphs highlight acceleration, communities and observed amplification—without claiming unsupported causation.
Privacy by design
- Public data and permitted platform access only
- Aggregate audience intelligence over individual profiling
- No inference of income, exact age, address or private identity
- Representative voices anonymized where appropriate
Read confidence correctly
- Geography may be observed or probabilistically inferred from public signals.
- Age is a broad, low/medium-confidence bracket—not a verified identity fact.
- Network movement is observed correlation, not proof of causation.
- Model predictions can be wrong and require human interpretation.
The reservation-protest experience uses seeded aggregate statistics and representative records so the SIH prototype runs without paid APIs. Production collection depends on configured credentials, platform policies and completed analysis runs. Instagram and Facebook access is not assumed.