Keshav Reddy's Equal AI raises $30M to answer Indians' unwanted calls

Why it matters

Equal AI is testing whether India's flood of unknown calls can become a consumer AI wedge. The round backs a founder trying to move from caller identification to delegated communication, but the disclosed metrics still leave revenue, retention and valuation unanswered.

Keshav Reddy's Equal AI has raised $30 million in Series B funding to build an AI call assistant for Indian consumers, TechCrunch reported, putting fresh capital behind a product that is trying to do more than identify unknown callers.

Reddy founded Equal AI in 2022, according to TechCrunch, after Equal AI began in a different corner of the same market: data-sharing for financial services. TechCrunch reports that Equal AI still offers data for financial analysis and KYC verification for employers, but the consumer call assistant is the sharper bet. Reddy, who comes from the family behind Indian conglomerate GVK, told TechCrunch that Equal AI "always wanted to be a customer-facing company," and that call handling became the first use case because users were being hit by calls tied to financial services and jobs.

"If you are buying car insurance, you might get 20 calls over a week, and that is hard to tackle for a human," Reddy told TechCrunch.

The round buys time for a consumer habit change

The Series B was led by Prosus Ventures and Tomales Bay Capital, with participation from Think Investments and Valiant Fund, according to TechCrunch. Individual backers named in the report include PhonePe founder Sameer Nigam, Zubin Bharti Mittal from Airtel Family Office, Skyflow AI co-founder Anshu Sharma, Meta India and Southeast Asia VP Sandhya Devanathan, and CtrlS Datacenters chairman Sridhar Pinnapureddy.

TechCrunch reports that Equal AI has raised more than $42 million to date. The valuation was not disclosed. That omission matters because the round is structured in three tranches, each with a different valuation tied to predetermined targets, according to TechCrunch. In plain terms, investors are not simply underwriting the current app. They are pricing future performance into the financing structure.

That structure fits the stage of the business. Equal AI says its Android app has grown to more than 1 million monthly active users and more than 300,000 daily active users since launching last year, according to TechCrunch. Those are company-supplied usage numbers, not audited metrics, and TechCrunch does not report retention, revenue, paid conversion or call completion rates. Equal AI's own homepage still carries a beta label and says it is "Trusted by 300K+ Indians," which may be a stale or different metric, but it underscores why the headline user count should be read precisely rather than flattened into market proof.

Beyond caller ID

India already has tools meant to reduce uncertainty around phone calls. TechCrunch frames Equal AI against Truecaller and the government's Calling Name Presentation system, or CNAP, both of which focus on identifying who is calling. Equal AI's argument is that caller ID leaves the hardest work untouched: deciding whether a call is worth answering and, if not, what response to send.

Equal AI's app currently answers unknown calls for Android users, speaks with the caller, captures the reason for the call and shows that context to the user, according to TechCrunch. The app can present quick replies such as "Leave the delivery near the door" or "Give it to the neighbor," which the AI reads back to the caller. Users can type a custom response as well. Equal AI records calls and stores recordings, transcriptions and summaries in the app.

That makes Equal AI closer to a consumer AI receptionist than a spam filter. The distinction is important. A spam filter tries to block the call. Equal AI is betting that a large share of unwanted phone friction is not strictly spam: deliveries, recruiters, insurance salespeople, banks and service providers may all have legitimate reasons to call, but the user does not want to negotiate each interaction in real time.

The hard part is trust, not speech

TechCrunch reports that Equal AI uses a mix of automatic speech recognition, speech generation models and its own orchestration layer. The technology stack is less interesting than the permissions the product asks for. Equal AI is inserting software into live phone conversations, storing transcripts and summaries, and eventually wants to move from screening to action.

According to TechCrunch, Equal AI plans to expand from unknown-number screening to known-number calls, text delivery workers with an address when the user consents, make outbound calls to book appointments and launch a paid subscription tier with added features. Those features would move Equal AI from answering the phone to representing the user.

That is where Reddy's original financial-services path becomes relevant. KYC and data-sharing are regulated, trust-heavy categories. The consumer app gives Equal AI a much larger surface area, but it also puts Equal AI in the middle of personal communications where mistakes are visible immediately. The Series B gives Reddy room to find out whether Indian consumers will delegate one of the most persistent parts of daily mobile life to an AI agent, and whether they will pay for it once the novelty wears off.