Revrag.ai offers Emma, an AI-powered sales agent that automates and scales outbound sales processes through intelligent prospecting, personalized communications, and meeting scheduling.
Marketing & Salesrevrag.aiTracked since 2026-08-211 discovery source
377Backlinks
128Referring domains
19Domain authority
+8.9%Referring domains · 90 days
Authority & distribution
How strong is Revrag.ai’s web footprint?
Backlinks measure accumulated distribution; referring domains show how broadly that authority is spread. Both are observable on the open web, so both are published for every product here.
Backlinks
377Current snapshot
Referring domains
128Current snapshot
Authority
19Domain authority · category median 22
90-day move
+8.9%Referring domains, last 90 days
At 19, Revrag.ai sits below the 22 median for its category.
Trajectory · Pro
The counts above are the latest of 370 daily observations. The curve through them is on the full profile.
Traffic makes more sense when you can connect it to the actual product promise.
“Revrag.ai”
Revrag.ai offers Emma, an AI-powered sales agent that automates and scales outbound sales processes through intelligent prospecting, personalized communications, and meeting scheduling.
Marketing & SalesSeen 2026-08-21 → 2026-08-21Coverage tier C
What we have measured
4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.
Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.
Canonical website: https://www.revrag.ai
Observed across 1 discovery source.
Research questions
What the evidence answers about Revrag.ai.
Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.
What is the core function of RevRag.ai?
RevRag.ai provides an in-app AI agent platform that embeds directly into financial applications to automate user journeys. It detects user friction like drop-offs and confusion, then triggers either an in-app or calling agent to guide users, complete tasks, and increase conversions.
What are the primary use cases for RevRag.ai?
It is used to recover abandoned loan applications, provide smooth KYC during onboarding, guide users to the right investment plan, help policyholders understand coverage and file claims, and re-engage users who drop off mid-journey.
What are the key features offered by the platform?
The platform offers an In-App AI Agent for real-time guidance and a Calling AI Agent for re-engagement. Key features include detecting user friction, combining screen and behavior data with CRM, triggering the right agent, guiding users, autofilling forms, and completing tasks.
Who is RevRag.ai designed for?
The platform is built for the BFSI (Banking, Financial Services, and Insurance) industry. It is specifically designed for financial applications to help with user onboarding, KYC, checkout, and re-engaging users who abandon processes like loan applications.
What is RevRag.ai?
RevRag.ai is an AI agents platform for financial apps. Its homepage title states it is the '#1 In-App AI Agents Platform' focused on automating user onboarding, re-engaging drop-offs, and driving conversions within BFSI products.
5 of 6 research questions are answered for this product. The rest need source evidence we have not collected yet, so they are left unanswered rather than guessed.
Behind the lock
Unlock full Revrag.ai intelligence.
Backlink trajectoryThe figures above are the latest observation. The daily curve behind them holds 370.
AI referral detailHow much traffic assistants send.
Search structure and ranking depthOrganic against paid, brand against non-brand, and how many keywords rank in the top three against the long tail — which is what decides whether the demand is portable or tied to the name.
The rest of the marketPage through every leaderboard 100 rows at a time, put any five products side by side, and query the whole dataset over API / MCP.
Worldwide estimates · Data period 2026-05-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured