Raj Desai

Tattva People see clips, posts, and ads that make strong claims. They need help to see facts, context, and bias. A yes/no fact check is not enough. People want to know who says what, what sources support it, and how close their own view is to the evidence. What Tattva does Tattva is a web app and a browser extension. You paste or share any link: a YouTube video, a post on X, an Instagram reel, a podcast clip, or an article. Tattva: Finds the key claims. Pulls evidence and citations from diverse sources. Gives a Tattva Score (0–100) for truthfulness and evidence strength. Lets you add your stance (optional). Shows your Reality Distance from the evidence. Explains why it scored the way it did. Who it is for People who want to check political clips, influencer ads, pop culture takes, or health claims. Communities that want shared context and sources. Platforms that may embed the Tattva Score. Why it is different Focus on context, not only verdicts. Optional stance input to meet the user where they are. Scoring that shows its work with citations and weights. Gentle prompts that help users reflect, not preach. Revenue model Freemium (consumer) Free: extension + web app, Tattva Score, citations, and a basic feed. Track up to 2 topics. Tattva keeps researching those topics and posts updates as Signals (a short digest) with a Pulse-like view of changes over time. Premium: unlock more tracked topics, custom feeds, higher update frequency, deeper dives, saved searches, and custom alerts. Users choose their topics; Tattva keeps them updated with ongoing Signals and a Pulse-style trend view. Rationale: keep public access broad while charging for more topics, control, and depth. APIs (pay-per-use) Endpoints: claim extraction, scoring, citations, topic status, and alert webhooks. Model: pay per analysis call with volume discounts. Optional SLA, audit logs, and crisis-mode topic pages as add-ons. Who pays: platforms, publishers, and tools that want the Tattva Score and citations inside their products. Teams & Enterprise (seats + add-ons) Seats: newsroom, brand, NGO, or enterprise consoles for bulk link scoring, saved topics, alerts, exports, SSO, and RBAC. Add-ons: private topic hubs for incidents, review workflows, and data retention controls. Model: per-seat pricing with optional incident or compliance packages.

It is very difficult to utilize all the public surveillance capabilities of a big city and often time pre symptoms of crime or very big give away factors are missed due to the fact that not everyone can man public cameras or keep an eye on everyone physically. This leads to blind spots for the security bodies to functions and in turn, these blind spots are exploited by anti-social elements of the society. These blind spots can be avoided and there can be warnings given ahead of time of a crime by looking at pre symptoms of a crime (or situations that lead to crime) or there can also be sure sign warnings that prompt for quick action from the Police or any security body. All this can be done by developing a distributed surveillance system that takes signals from two of the smartest beings on earth that are capable of detecting pre symptoms as well as sure sign signals of crime. Humans AI We have tried to do just this by combining user warnings and feedbacks with our trained AI. The whole system if we let it scale can make neighbourhoods super safe and can also lead to a reduction in action to emergency time as this can prompt actions to be taken before an event occurs. To sum it up, We are trying to be your friendly neighbourhood spider man but with a catch that we would be working for the security body and use our spidey sense to inform them about any anti-social activity that may be going on in their neighbourhood. This would make the city safer and the job of the police/security body a little bit easier(or so we hope).