AI & ML

Choosing the Right Model

We research and evaluate different AI models based on business needs to find the best fit. We take into account factors such as available data, training time, and other key considerations.

Training and Customizing the Model

We build models from scratch or fine-tune existing ones to tailor them specifically to the client’s business using its own data.

Putting the Model to Work and Making it Better

We set up a system to allow continuous training and updates to the model. This system is further optimized to enhance performance and reduce costs.

Data Collection and Labeling

We assist in collecting and labeling new data unique to the business. We use tools and other models, combined with human supervision, to effectively label the data.

Managing Data

We build systems to manage and prepare data for further model training. We also set up data storage and business intelligence tools.

Large Language Models (LLM)

We stay current with the latest in LLM and have built applications on top of paid services such as ChatGPT, GPT-4, or open-source solutions such as LLaMA, Alpaca, or Stability AI. We have also utilized autonomous libraries such as LangChain, Auto-GPT, or BabyAGI.

Computer Vision GenerativeAI

We use models for tasks like generating videos and images based on source files, categorizing images, adding captions to images, and converting text to images or images to other images.

MLOps

We put multiple models into use on cloud platforms, set up to keep learning and improving. We also make sure these systems are cost-effective and high-performing.

Text-to-Speech

We use different models to convert written text into spoken words, applying different Spectrogram and Vocoder models such as Tacotron2, FastSpeech, and HifiGan.

Speech-to-Text

We use models to turn spoken words into written text, which can be used to automatically add captions to voice data or analyze what’s being said.

Face Recognition & Full-Body Tracking

We utilize machine learning and computer vision solutions, including facial landmark detection, feature extraction, and deep learning models, to track faces, hand gestures, and poses in real-time for various applications.

Object Detection

We use lightweight computer vision models to detect objects in real-time.

Entertainment

We used a collection of models to create AI-generated art and realistic photos based on a few source photos.

Marketing Lead Generation

We use AI to guide large language models to complete multiple steps and tasks in the lead generation pipeline.

Marketing Media

We use a collection of models to create marketing videos based on a few source photos.

Education

We used emotion detection, speech-to-text, and text analysis to grade answers, provide feedback, and make recommendations for language learners.

News Aggregation

We collected voice talent recordings, fine-tuned text-to-speech models to generate top-quality audio, and used models to curate and summarize text news content to listeners through audio.

Business Communication

We collected and labeled data from business communication to understand conversation tone and context to make recommendations on the next task actions.

Candidate Placement

We used computer vision, face tracking, emotion detection speech-to-text, and text analysis to assess and support interview candidates in perfecting their interview skills.

Customer Support

We used LLM models to assist customer support in generating responses that fit with the brand tone and suggested accurate responses for user requests.