Voice authenticity belongs in the infrastructure.

Voice can now be generated as easily as it is recorded. We build the model layer that helps businesses verify speech, identify the caller and decide what can happen next.

Detection, verification, call intelligence and voice agents.

Aletheia · Authenticity

Is this a real voice?

Analyze live calls and recordings for synthetic, cloned or replayed speech. Return a synthetic score and identify the segments that require review.

Eidos · Identity

Is this the right person?

Compare a caller’s voice with an enrolled reference and return a speaker match score. Support verification during account access, recovery and transaction confirmation.

Noesis · Intelligence

Who or what answered?

Recognize human speech, IVR systems, voicemail and voice agents. Classify the recipient at call start and update the route as the conversation changes.

Lexis · Conversation

What can the agent do next?

Handle defined customer requests with authenticity and identity checks before sensitive actions. Transfer the conversation and its context to a specialist when needed.

Detectif.ai is building the model layer underneath those decisions.

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.

Build for demanding call conditions.

India brings together many languages, varied handsets, noisy environments and extensive digital-financial usage. These conditions shape how we evaluate voice-security infrastructure for real calls.

Linguistic diversity: Many languages and regional 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: Instant payments, 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.

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.

Incoming call
Local / network authenticity analysis
Risk signal
User warning or policy action

09:415G

Incoming callUnknown caller

High impersonation risk

Possible synthetic voice

Speaker identity could not be verified

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.