Tag: ai production

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  AI Avatar: How to Add a Face to a Voice Assistant and Avoid Pitfalls

  Integrating an AI avatar into the existing voice AI administrator Aika is not just about adding a face; it involves a whole chain of technical challenges. We aimed to preserve the current architecture on Yandex Realtime so that the new service would only animate lips to already generated sound. During testing of Simli and Anam, nuances emerged with pauses before responding, video playback on iPhone, and mobile internet stability. And when creating the second character, Eva, it became clear that the persuasiveness of the neural network depends not only on the face itself but also on the context.

  Our voice engine was already functional, so the task was solely to animate the face. Aika communicates with website visitors directly in the browser via WebSocket, which is connected to our FastAPI server, and that, in turn, maintains a session with Yandex Realtime. This ensures a “speech-to-speech” mode, eliminating the need for separate recognition chains, text models, and synthesis.

  The server transmits uncompressed sound in PCM16 format at 24 kHz in chunks (pcm_chunk) to the browser, and it was precisely to this stream that the avatar’s facial expressions needed to be synchronized.

  Choosing a platform for the AI Avatar: Simli vs Anam

  Both tested services — Simli and Anam — offer a mode where the user provides their audio stream, and the service generates video with an animated face synchronized with the sound, returning media via WebRTC. Thus, Yandex continues to generate speech, and the avatar service is responsible for the visual component.

  Technical aspects of sound integration

  • Sampling rate: Yandex outputs sound at 24 kHz. Simli requires 16 kHz, Anam was also configured for 16 kHz, although it supports other formats.
  • Processing audio chunks: Sound arrives in chunks. To avoid artifacts at the junctions, the fractional position and the last sample are carried over to the next chunk. This ensures smooth animation, although the quality of frequency recalculation without a full-fledged filter still requires improvement.

  Security and session management

  The service key does not reach the browser. The page requests a short-term session token from our server via POST /voice/avatar-session. The server checks the origin and limits token issuance: 6 per hour per IP and 30 per hour total.

  If the token is not received or the video does not start, the page switches to normal voice mode. 8 seconds are allotted for the token request, and 15 for SDK launch. It is important that a successful SDK launch does not guarantee the immediate appearance of the first frame; the face here is an addition, not a critical function.

  Managing Avatar State in Anam

  Anam provides two key commands:

  • endSequence(): called after sending the last audio segment of a replica.
  • interruptPersona(): stops the avatar’s current speech when interrupted, after which endSequence() is also called.

  Comparison of Simli (old generation) and Anam Cara-4

  We compared the versions available to us: Simli of the previous generation and Anam Cara-4. This is not an exhaustive comparison of all features, but a selection for a specific task.

  Parameter	Simli (previous generation)	Anam Cara-4
  Mimicry	Lips move, other mimicry is limited	Blinks, smiles, teeth visible, tilts head
  Face creation	From photo via previous generation API	From a single photo, trial video available

  Subjectively, Anam showed more lively and natural mimicry. For us, this was a decisive factor. Next, we encountered a number of specific problems.

  Media Playback Features

  • Sound: In Simli, sound was played through a separate audio element; in Anam, it was played through the same video element. This means that the Anam video element cannot be left muted, otherwise the avatar will “speak” silently.
  • Resolution: Video often started at a low resolution (384×576) and only switched to high resolution (768×1152) after a few seconds. The sessionOptions.videoQuality: "high" option helped, but did not guarantee a high-quality start.
  • Frame generation delay: Anam needs to receive 0.8 seconds of audio before starting frame generation. This is not always equal to 0.8 seconds of real-time waiting, as audio can arrive faster than it is played.

  Microphone and Session Management

  • Echo suppression: On the phone, while Aika is speaking, the microphone is muted to avoid picking up her voice from the speaker. On the PC, the microphone is active, and when the user starts speaking (barge_in event from Yandex Realtime), the avatar stops its current speech.
  • Busy session slot: On the free plan, one concurrent session is available. If the browser crashed, the slot could remain occupied. The GET /v1/sessions/concurrency request helps diagnose slot occupancy.
  • Connection cancellation: If the user clicks “stop” during video connection, it is necessary to correctly complete asynchronous operations so as not to leave an unnecessary session. For this, a generation counter videoGen was introduced.

  Problems with Safari on iPhone

  On iPhone in Safari, the video did not start. The hypothesis is related to autoplay rules. A preliminary call to play() on the video element with temporary stream feeding from canvas and unmuting helped in a test run, but is not a universal solution.

  Optimizing the Response Pause

  A long pause before an avatar’s response, where it silently stares at the user, feels much heavier than without it. Initially, the Voice Activity Detection (VAD) threshold was set to 2.2 seconds to prevent Aika from interrupting users dictating information with pauses.

  We transferred the logic of the telephone bridge to the website: in a normal conversation, the threshold is 1500 ms, when dictating — 2200 ms. This allows dynamic adjustment of VAD sensitivity. Settings are applied via session.update without reconnection.

  
  

  Creating a Second Character: Eva

  For the studio administrator Eva, we faced the problem of a “stock blonde” against an empty background. The turning point came when an interior appeared behind her: warm lamps, shelves, plants, a blurred background. This made the image much more convincing, transforming a “catalog” into a “person at their workplace.”

  Details of Background and Portrait Creation

  • Background: We used a ready-made interior in our style, removing people with a generator.
  • Character Cutout: Eva was cut out using macOS Vision (VNGeneratePersonSegmentationRequest with .accurate level). The mask edge was narrowed and softened to eliminate the halo.
  • Scale: It is important to maintain the scale of objects in the background to avoid comical situations (e.g., a giant mug behind her). The background was taken only above the countertop and blurred for a portrait lens effect.
  • Resolution: For Cara-4, a square source of 1152×1152 or higher is recommended. Pure Real-ESRGAN ×4 upscaling made the skin too smooth. A combination of neural network upscaling with regular scaling, sharpening, and grain addition looked better.

  Eva’s Voice Acting

  For Eva, we chose a voice from Yandex’s LiveTTS family. In SpeechKit API v3, the model is selected via "model": "livetts", and the voice and role are passed via hints. For long texts, the text was broken down into sentences and synthesized separately.

  Out of seven options for voicing one phrase, we chose the vera voice in the casual role. We had to rewrite the phrase because the synthesis consistently made a mistake with the stress.

  Recording Videos with the Avatar

  Eva’s video was recorded using the same animation engine as a live call, using MediaRecorder directly from video.srcObject (video and audio together, WebM with VP9 and Opus). For correct recording start, the stream was pre-“woken up” with a second of silence so that the video would not start with a black screen.

  Frequently Asked Questions

  What is an AI avatar and how does it work?

  An AI avatar is a digital character capable of animating facial expressions and lip movements in accordance with provided audio. It uses neural networks to create a realistic visual image that synchronizes with speech, making interaction more natural and engaging.

  Can I use my own voice for an AI avatar?

  Yes, most platforms, such as Anam and Simli, allow you to use your own voice engine or pre-recorded audio. The avatar will animate its face to this audio stream, maintaining your brand or persona’s recognition.

  What are the main challenges when integrating an AI avatar?

  The main challenges include audio and video synchronization, processing audio chunks, optimizing latency, ensuring stable operation on various devices (especially mobile), and creating a convincing visual image that matches the context and brand.

  How does the background affect the perception of an AI avatar?

  The background plays a critical role in creating a convincing image. An empty or inappropriate background can make the avatar appear unnatural. An interior that matches the character’s or brand’s style significantly increases the avatar’s realism and trustworthiness, creating the feeling of a “person at work.”

  How much does it cost to create an AI avatar?

  The cost of creating an AI avatar can vary greatly depending on the chosen platform, the level of customization, the detail of facial expressions, and the need for unique functions. Some services offer subscription models, while others may charge per minute of generated video or for the development of a unique character.

  Conclusion

  Integrating an AI avatar is a powerful tool for increasing user engagement, but it requires deep technical development. From platform selection to fine-tuning backgrounds and pauses, every step affects the final result. We have seen that neural networks can breathe new life into voice interfaces, but only with careful attention to detail.

  If you want to scale your content and create AI video for advertising, consider the possibilities of AI production by subscription. This allows you to generate hundreds of videos, whether it’s AI UGC for a crypto project or an AI video factory for e-commerce. We are ready to help you with this! Contact us to find out how AI content production can transform your business.

  18.10.2026

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  Melius: From Ramp Engineers to $25 Million for AI Video Generation

  In a world where speed and efficiency play a key role, Melius, a platform for creating advertising campaigns, images, and videos using artificial intelligence, is making a real breakthrough. The company announced that it has raised $25 million in investments, which underscores the enormous potential of AI video generation in the modern industry. This significant financial influx includes a $20 million Series A round led by CRV and an initial $5 million round from General Catalyst. Founded by former Ramp engineers, Melius demonstrates impressive growth, achieving over $1 million in annual recurring revenue just two months after emerging from stealth mode in July.

  Melius Raises $25 Million: Revolutionizing AI Ad Generation

  From Idea to Scaling: How Melius Reinvented Itself

  Melius co-founder Juwon Kim admits that the company’s journey was not smooth. Initially, over a year ago, the team, consisting of Juwon Kim, Yang Kim, and Arnav Ramu (all three former Ramp engineers), focused on creating a tool to optimize advertising spending.

  However, as Kim notes, after six months, it became clear that the initial idea “had no prospects.”

  “We completely destroyed the entire codebase; we burned it all down,” Kim told TechCrunch.

  This decisive step allowed Melius to reorient itself to a much larger task: developing tools for generating creative assets themselves and advertising campaigns. This marked the beginning of a new era for the company, creating an “agent lab for creative work.”

  Melius in the Competitive Landscape of AI Production

  The market for AI creative generation is rapidly evolving. Among Melius’s competitors are projects such as Higgsfield, which was valued at $5.4 billion in August and demonstrates annual revenue of over $700 million. There are also smaller players, such as Krea and Flora AI. Juwon Kim sees no problem with this, emphasizing the enormous potential of the market.

  • Higgsfield: Valuation of $5.4 billion, annual revenue of $700+ million.
  • Krea: A young startup in the field of AI generation.
  • Flora AI: Another player creating creatives with AI.

  “There are a lot of players out there, and it’s pretty exciting,” Kim noted. “It means there are customers to win, and there’s demand in this space.” This confirms our position that the cost of one AI creative today is significantly lower than traditional production, which opens up huge opportunities for mass video generation by neural networks.

  
  

  From YouTube Dream to AI Avatars: The Founder’s Personal Motivation

  Interestingly, behind the decision of the former Ramp engineers to engage in advertising generation is Juwon Kim’s personal story. He describes himself as a social media influencer and dreamed of creating short videos since childhood. “I tried my best to make vlogs,” Kim recalls. “My father was great at FinalCut Pro, and I wasn’t.”

  These early attempts were unsuccessful, but his passion for digital media remained. Now, with Melius, Kim is building the tool he always dreamed of: a platform where anyone, from an experienced creative director to a novice, can bring their ideas to life using video generation from text.

  This will allow content creation to be scaled in ways previously impossible, making subscription-based neuro-production accessible to everyone looking for AI efficiency versus real UGC.

  Frequently Asked Questions

  Question	Answer
  What is Melius and what services does it offer?	Melius is an AI platform for generating advertising campaigns, images, and videos. It allows users to create creative assets using artificial intelligence, which significantly simplifies and speeds up the content production process.
  How much investment has Melius attracted?	Melius has attracted a total of $25 million: $20 million in a Series A round and $5 million in an initial round.
  Who founded Melius?	Melius was founded by former Ramp engineers: Juwon Kim, Yang Kim, and Arnav Ramu.
  What is Melius’s annual revenue?	Melius achieved over $1 million in annual recurring revenue just two months after emerging from stealth mode in July.
  Does Melius have competitors in the AI ad generation market?	Yes, Melius has competitors such as Higgsfield, Krea, and Flora AI. However, Melius founders are confident that the market is large enough for multiple players.

  Conclusion

  The story of Melius is a vivid example of how flexibility and foresight can lead to success in the rapidly changing world of technology. By abandoning its initial idea, the team created a platform that not only attracts significant investment but also promises to revolutionize AI video for advertising. We see how neural networks are transforming content creation, making AI conveyor 500 videos per day possible and offering new opportunities for scaling creative processes.

  If you want to know where to order AI UGC for a crypto project or how to generate 100 videos using neural networks, Melius demonstrates that the future is already here. Join this neuro-revolution and contact us to find out how AI can scale your content production!

  07.10.2026

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  AI Industry: Slowdown or Strategic Move?

  In the world of artificial intelligence, a serious discussion is brewing: should we slow down the pace of development or continue to move forward without looking back? Against the backdrop of Anthropic CEO Dario Amodei’s plans to “pace the frontier” and Nvidia CEO Jensen Huang’s statements about the unnecessary nature of regulation, the question arises: how serious are the intentions of industry leaders like Amodei and OpenAI’s Sam Altman regarding a slowdown?

  Slowing Down AI Progress: Myth or Reality?

  On the latest TechCrunch Equity podcast, Kirsten Korosec, Sean O’Kane, and Anthony Ha discussed these conflicting opinions. Anthony Ha was surprised by how many industry leaders supported Amodei’s plan.

  However, Sean O’Kane noted that the plan looks vague and lacks specifics, which raises doubts about its feasibility.

  Lack of Regulation and Consumer Choice

  “The federal government right now is certainly not eager to broadly apply regulation, let alone in specific cases,” Sean O’Kane stated, emphasizing that existing laws are not always effective in a rapidly changing technological environment.

  He also noted that in the market for AI solutions, there is virtually no consumer choice that could serve as leverage over companies.

  “It doesn’t feel like consumer choice is heavily influencing this market in the sense that if one of these companies does something really bad, they’re going to see the consequences in terms of cancellations or anything,” O’Kane added.

  This means that even serious missteps by major players may not lead to significant financial losses due to the loyalty of corporate clients and huge investments.

  Jensen Huang and Nvidia’s Position

  Nvidia CEO Jensen Huang publicly spoke out against a slowdown, supporting Donald Trump’s view that fears about AI are a “hoax” and regulation is unnecessary.

  His position raises questions, given that Nvidia is one of the main beneficiaries of the rapid development of AI technologies, supplying the necessary equipment for rendering and training neural networks.

  Why is Nvidia Against a Slowdown?

  • Economic interests: The rapid growth of AI directly correlates with the increased demand for Nvidia’s graphics processors. A slowdown could negatively impact their revenues.
  • Role of the “adult in the room”: Sean O’Kane believes that Huang positions himself as the “adult in the room,” aiming to be a useful liaison between the industry and the administration. This allows him to influence decisions and shape the agenda.
• Lack of direct competition: Nvidia does not develop end-user AI models, but provides infrastructure, so the direct risks from “unsafe” AI are lower for them.

Specifics of “moderate development” proposals

Kirsten Coroset mentioned proposals for “moderate development,” which include:

1. Attracting independent third-party evaluators to monitor AI safety within companies such as Anthropic and OpenAI.
2. Coordination between major AI companies in democratic countries to develop common safety standards and restrictions.
3. International coordination of efforts to regulate AI.

However, as Coroset noted, these proposals suffer from a lack of specifics. It is unclear who will act as independent evaluators, what standards will be applied, and how international cooperation will be carried out.

This creates the impression that statements about a “slowdown” may be more declarative than practical.

AI pipeline: from text to video in seconds

While discussions about regulation continue, the AI video generation industry is demonstrating unprecedented growth rates. Companies like Synthesia and other market players offer solutions for mass video generation by neural networks, opening new horizons for business.

We see how neural networks allow the creation of AI commercials, animated AI characters, and even AI avatars with subtitles and AI voiceovers.

Advantages of AI production:

• Speed: An AI pipeline can produce hundreds of videos per day, which was impossible to scale – now possible.
• Cost savings: The cost of 1 minute of AI video is significantly lower than traditional production. This makes neuro-production by subscription accessible even for small businesses.
• Personalization: The ability to generate video from text and use mass generation API allows for the creation of unique content for each client.
• Efficiency: The efficiency of AI versus real UGC (User-Generated Content) can in some cases be higher, especially when dealing with large volumes of content.

These technologies are actively used in e-commerce, for AI UGC advertising, and even in crypto projects, where fast and effective communication with the audience is required.

The ability of neural networks to write scripts and edit videos significantly reduces the time and cost of content creation.

Frequently Asked Questions

What is AI content production?

AI content production is the process of creating video, audio, and text content using artificial intelligence technologies. This includes generating video from text, creating animated AI characters, AI voiceovers, and even neural networks writing scripts and editing.

How much does one AI creative cost?

The cost of an AI creative can vary depending on complexity, duration, and features used. However, in general, the cost of 1 minute of AI video is significantly lower than traditional production methods, making it accessible to a wide range of companies, including startups and projects in the cryptocurrency sector.

Where to order AI video for crypto?

For AI UGC for a crypto project, you can turn to specialized platforms and studios that offer services for mass video generation by neural networks. Many of them provide AI production by subscription and use mass generation API to create content tailored to the specific needs of the crypto industry.

Conclusion

The discussion about slowing down the development of the AI industry, while important for ensuring safety, faces pragmatic interests and a lack of clear implementation mechanisms. At the same time, AI video generation continues to develop rapidly, offering businesses unprecedented opportunities for content creation.

Instead of slowing down, we are likely to see increased competition and the emergence of new, even more effective solutions for AI production. Want to know how AI video for advertising can scale your business? Contact us to discuss the possibilities of an AI video factory for e-commerce and generating 100 videos for your marketing campaigns.