Company · Our thesis

Authenticity becomes a primitive.

Encryption did not become universal because every internet user independently bought an encryption product. It became universal when it moved into the infrastructure underneath the applications people already used.

We believe voice authenticity follows the same path.

Is it human?
Detect synthetic, cloned or manipulated speech.
Is it them?
Verify that the speaker matches the claimed identity.
Can it be trusted?
Combine authenticity, identity, context and policy before allowing sensitive actions.

DetectifAI is building the model layer underneath those decisions.

Why now

Voice used to be evidence.

A few seconds of speech can now be enough to reproduce someone's voice. The cost of generating convincing synthetic speech is moving toward zero while quality continues improving.

Yet phone calls, speaker verification, financial authentication and human decision-making were designed for a world where hearing someone's voice was strong evidence that the person was real.

As generating speech becomes infrastructure, verifying speech must become infrastructure too.

Where we build

Build for the hardest voice environment first.

India combines many languages, enormous call volume, inexpensive devices, noisy environments, variable networks, heavy digital-financial usage, and fast-growing voice fraud.

This is not merely an underserved market. It is an engineering stress test for building global voice-security infrastructure.

Linguistic diversity: 22+ official languages, hundreds of dialects
Device spectrum: From premium smartphones to entry-level handsets
Network variability: 5G urban centers to 2G rural coverage
Acoustic environments: Traffic noise, crowds, multiple speakers, background activity
Call infrastructure: PSTN, VoIP, mobile networks, telephony codecs
Digital finance: UPI transactions, digital lending, voice-based banking

A model that works across Indian languages, lossy telephony and mid-range devices is being tested against many of the conditions it will encounter globally. We build here so the infrastructure can travel everywhere.

Vision

Fraud protection for every voice call.

There are billions of smartphones in use globally. The phone is where voice impersonation attacks ultimately arrive.

If authenticity models can run efficiently on-device, protection can eventually be distributed through OEM and telecom partnerships instead of requiring every user to separately purchase fraud software.

01Incoming call
02Local / network authenticity analysis
03Risk signal
04User warning or policy action

Incoming callUnknown caller

Possible synthetic voice
Speaker identity could not be verified
High impersonation risk

Illustrative warning states

Our goal is for voice authenticity to become as invisible and universal as spam detection: present by default, operating underneath the interaction, and available to people who will never buy dedicated fraud software.

Research