Connect with us

Tech

My Main AI Turns Complex Workflows into Simple, Voice-Driven Conversations

mm

Published

on

Photo Courtesy of My Main AI Inc.

By: Chelsie Carvajal

Managing modern workflows often means juggling dashboards, documents, and long email threads before a single task is complete. My Main AI Inc, an AI technology platform that spans text, image, voice, and video, has built a system where many of those steps can be handled through spoken or written prompts instead of manual clicks.

Turning Tasks Into Conversations

My Main AI groups several automation tools around a voice and chat layer so users can move through work by giving instructions rather than configuring each step. The platform lists AI Web Chat, AI Realtime Voice Chat, AI Speech‑to‑Text Pro, and AI Text‑to‑Speech engines from providers such as Lemonfox, Speechify, and IBM Watson, creating a loop between spoken input and generated output.

Speech‑to‑text tools support accurate transcription of audio content in multiple languages, with options to translate those recordings into English. That capability gives businesses a way to record meetings, calls, or field conversations, then convert the results into text that can be summarized, edited, and turned into documents or scripts. Text‑to‑speech tools, including multi‑voice synthesis with up to 20 voices and SSML controls, take written content in the other direction, producing voiceovers for training, marketing, and support material.

Chat assistants extend the same pattern to files and websites. My Main AI lists AI Chat PDF, AI Chat CSV, and AI Web Chat, which allow users to ask questions of documents or site content through natural language prompts. Instead of sorting through long reports, a user can query a file, receive concise answers, and then send follow‑up requests to generate emails, briefs, or summaries in the same environment.

From Content Pipelines to Voice‑Led Workflows

The company reports that its platform connects to more than 100 models from OpenAI, Anthropic, Google Gemini, xAI, Amazon Bedrock and Nova, Perplexity, DeepSeek, Flux, Nano Banana, Google Veo, and Stable Diffusion 3.5 Flash. Public materials state that these models support text, image, voice, and video generation in more than 53 languages, giving the voice‑driven tools reach across several regions and markets.

Content creation sits at the center of many of these workflows. My Main AI offers modules for blog posts, email campaigns, ad copy, social captions, video scripts, and structured frameworks such as AIDA, PAS, BAB, and PPPP. A user can dictate key points or paste a brief into the chat, receive draft text, ask the assistant to adjust tone or length, and then pass the result into voice synthesis to create a narrated version.

Visual tools fit into the same flow. DALL·E 3 HD, Stable Image Ultra, and an AI Photo Studio support image creation, product mock‑ups, background changes, and multiple variations from a single upload. AI Image to Video and text‑to‑video connections with engines such as Sora and Google Veo, alongside an AI Avatar feature labeled “coming soon,” make it possible to turn a spoken or typed brief into images, then into short clips that accompany the newly generated audio.

Why Businesses See Conversation as Infrastructure

Company data shared with partners cites more than 77,000 customers worldwide, annual revenue near 3 million dollars, and monthly revenue growth around 250,000 dollars, driven largely by subscription sales. The 49‑dollar plan is described as the best‑selling tier, with My Main AI presenting it as the entry point to the broader suite of conversational and automation tools.

Business‑oriented features show how these voice‑driven workflows connect to operations. The platform lists payment gateways such as AWDpay and Coinremitter, integrations with Stripe, Xero, HubSpot, and Mailchimp, and tools for SEO, finance analytics, dynamic pricing, wallet systems, and referrals. A manager can ask a chat assistant to pull figures, draft a report, and prepare customer messages, then move directly into sending campaigns or reviewing payments through linked services.

Company communications describe ongoing work on proprietary models, expanded training flows from text, PDFs, and URLs, and deeper tools for chat, analytics, and video. That roadmap suggests that My Main AI views conversation—spoken or typed—as a central control surface for complex workflows, with automation stepping in behind the scenes so users can focus on clear instructions rather than manual configuration.

From television to the internet platform, Jonathan switched his journey in digital media with Bigtime Daily. He served as a journalist for popular news channels and currently contributes his experience for Bigtime Daily by writing about the tech domain.

Continue Reading
Advertisement
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Tech

CypherFace Targets Payment Fraud with Pre-Transaction Biometric Verification

mm

Published

on

Photo courtesy of CypherFace

A U.S.-based fintech company has deployed a facial biometric system that verifies user identity before digital payments are processed. CypherFace, which began commercial operations in 2024, is positioning its technology as a proactive defense against payment fraud that now costs billions annually.​

Founder Syed Samir Hassan said the company developed the platform in response to the limitations of fraud detection systems that identify problems after transactions have already occurred. “Traditional fraud tools are reactive by design. They analyze patterns and flag suspicious activity, but the money has often already moved. We’re stopping it before the transaction completes,” Hassan said.

The Fraud Problem

Digital payment fraud has grown substantially despite existing security measures. Payment fraud in the European Economic Area increased to €4.2 billion in 2024, up 17% from 2023, according to data from the European Central Bank and European Banking Authority. Credit transfer fraud alone saw a 24% increase.​

Synthetic identity fraud, which involves creating fictitious identities using combinations of real and fabricated personal information, has become particularly problematic. False identity cases increased 60% in 2024 compared to the previous year. These synthetic identities often pass initial verification checks because they use legitimate data elements.​

Hassan said CypherFace was designed specifically to address this threat vector. “Synthetic identities work because they look clean on paper. They pass KYC checks. They build credit histories. But they can’t pass a live biometric verification tied to a real person. That’s the fundamental flaw we exploit.

The company reports that fraudsters increasingly use AI-generated documents and deepfake technology to bypass security systems. CypherFace’s liveness detection technology is designed to identify these sophisticated spoofing attempts during the authentication process.​

How the Technology Works

CypherFace provides businesses with an API that integrates into payment infrastructure. When a user initiates a transaction, the system prompts for facial verification. The technology captures and encrypts a facial scan, then applies AI-driven liveness detection to confirm a physically present individual is authorizing the payment.​

The system processes the verification in real time without storing raw biometric data. Facial scans are converted into encrypted, non-reversible hashes. The platform returns only a verification result to the merchant, indicating whether the transaction should proceed.​

We designed this to be invisible to legitimate users and impossible for fraudsters,” Hassan said. “A real customer takes two seconds to verify. A criminal using a stolen card or synthetic identity can’t get past the liveness check. The math is simple.

Deployment and Results

An e-commerce payment processor deployed CypherFace across its checkout infrastructure in late 2024. The processor was experiencing elevated chargeback rates driven by card-not-present fraud. Within 45 days of implementation, CypherFace flagged more than 1,200 fraudulent transactions that had previously bypassed existing security layers.​

The integration reduced chargebacks by 62% in the monitored segment. The processor reported improved merchant satisfaction as legitimate transactions experienced minimal additional friction. The company has since expanded CypherFace to additional merchant accounts.

Hassan noted that the technology addresses a specific gap in payment security. “Most fraud prevention happens at the network level or through transaction monitoring. We’re adding a layer that asks a simple question: is the person trying to make this payment actually who they claim to be? If they’re not, the payment doesn’t happen.

Market Expansion

CypherFace currently operates in the United States and is preparing to expand into Canada and Mexico in 2026. The company is targeting payment processors, merchant acquirers, and platforms with high transaction volumes and elevated fraud exposure.​

Hassan said the company sees demand from businesses struggling with the cost of chargebacks and fraud losses. “Every fraudulent transaction costs more than the transaction value when you factor in chargeback fees, lost merchandise, and reputational damage. Businesses are looking for solutions that actually prevent fraud rather than just detect it after the fact.

The fintech sector has broadly adopted biometric authentication, with major banks and digital financial platforms using facial recognition and fingerprint scanning for account access and transaction authorization. CypherFace is focusing specifically on payment verification rather than account login.​

We’re not trying to replace existing security. We’re adding a verification layer at the most critical point in the transaction flow,” Hassan said. “When money is about to move, we make sure the right person is authorizing it. Everything else is secondary to that.

Continue Reading

Trending