SPUNCH

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Overview

$10,000+

Min Project Size

$25 - $49/hr

Avg Hourly Rate

10 - 49

Employees

2022

Year Founded

ABOUT SPUNCH

AI Driven Software Solutions

SPUNCH specializes in crafting AI solutions for a range of industries. Our skills allow us to enhance operations and tackle intricate business problems with cutting-edge software. Additionally, we focus on developing budget-friendly MVPs for startups and sophisticated applications for established businesses.

Languages Spoken

EnglishUkrainian

HIGHLIGHTS

$10,000+

Min Project Size

$25 - $49/hr

Avg Hourly Rate

10 - 49

Employees

2022

Year Founded

Services

Focus

Industries

Clients

Services Provided

10% AI Strategy & Roadmap Consulting

13% LLM Integration & Prompt Engineering

10% Machine Learning Model Development

10% Custom AI Chatbot Development

7% AI Model Fine-Tuning & Training

30% DevOps Engineering

20% Managed IT Services

Portfolio

Comprehensive AI-Powered Solution For Call Center

Callstation

SUMMARY A call center encountered the issue of manually placing thousands of calls each day, resulting in considerable time wasted dealing with automated answering systems. In search of a solution, the client turned to us to implement automation in their operations, with the goal of optimizing their calling process and effectively handling the high call volume. The project required the automatic identification of answering machines based on the respondent's voice, alongside further automation of the call workflow. To address the challenge of recognizing answering machines, our team employed algorithms from scikit-learn, including LightGBM, CatBoost, and XGBoost. Following extensive testing and validation, we selected the best-performing algorithm, achieving 98% precision and 97% recall. Additionally, we used the Librosa library for sound processing and analysis to ensure precise and efficient management of audio data. TECH STACK Scikit-learn, Librosa, Digital Ocean, Django, Python DELIVERY TIMELINE Solution Architecture Design (1 week) Data Collection & Preprocessing (2 weeks) Answering Machine Detection Model Development (2 weeks) SOLUTION The initial step involved gathering thousands of the client’s call recordings and categorizing them into two groups: calls answered by humans and those handled by answering machines. Using this dataset, we set out to create a detection model. We evaluated both neural network methods and traditional machine learning techniques. Ultimately, we opted for the classical approach, achieving commendable results with 98% precision and 97% recall in our validation tests, which facilitated swift and accurate detection of answering machines, crucial for improving the client's call process.

AI Tool For Generating Lamps In Virtual Interior

AiWorx

SUMMARY An interior design firm approached us to utilize AI technology to save time for their employees. They supplied approximately 5,000 images from around 30 different lamp brands, with the goal of automating lamp generation in various interior environments. The client requested a system where they could enter the lamp's name and its desired location to receive a highly accurate generated image of the lamp in that context. Due to the project's dependence on a large diffusion model, it was essential to use a GPU server to manage the computational demands. The client also wished to host the model, requiring a solid infrastructure for real-time image generation while controlling GPU-related costs. TECH STACK Stable Diffusion, AWS, Python DELIVERY TIMELINE Solution Architecture Design (1 week) Stable Diffusion XL Fine-Tuning & Customization (2 weeks) Model Deployment to AWS & Performance Monitoring (2 weeks) SOLUTION To address the client's requirements for high-quality, customizable image generation, we implemented a combination of sophisticated AI models. We selected Stable Diffusion XL for its strong image generation capabilities, complemented by Lora and Dreambooth to fine-tune personalized elements of the lamps. Moreover, we integrated CLIP to automatically generate descriptive text from images, creating a smooth workflow that delivers accurate, contextually relevant imagery based on simple text inputs. For hosting and server management, we chose Amazon Web Services (AWS), employing GPU instances to efficiently meet the significant computational needs of our AI models. Additionally, we utilized the asynchronous SageMaker Endpoint framework to provide a scalable and adaptable solution for managing model deployment.

Advanced Security Data Computation with CrypTen

Financial Institution

**SUMMARY** The client is a prominent financial institution that prioritizes precision and security in data management. They have extensive expertise in financial market analysis, statistical methods, and advanced forecasting strategies. The client aimed to create a time series forecasting model for financial data, emphasizing the necessity for the data to remain encrypted throughout the entire process. This encryption was essential to maintain the statistical relevance of the data during both the training and inference stages, ensuring that the model could generate accurate predictions without compromising security. Furthermore, the client required the flexibility to utilize this model with both encrypted and unencrypted datasets. This led to the development of a model capable of seamlessly transitioning to a secure version using CrypTen for encrypted data handling and reverting back to a standard model for unencrypted data use. **TECH STACK** PyTorch, CrypTen **DELIVERY TIMELINE** - Solution Architecture Design (1 week) - Creating Transformer Model with CrypTen (4 weeks) - Statistical Validation (1 week) - Deployment & Testing (1 week) **SOLUTION** The team created an Encoder block for the transformer model using PyTorch. They custom-built key components, including multihead attention, layer normalization, and specific activation layers from the ground up. This method facilitated the easy conversion of the standard PyTorch model into one compatible with CrypTen, allowing for secure training and inference on encrypted data. Additionally, the team carried out comprehensive testing and statistically compared inference accuracy between real and encrypted data, finding a mere 0.0001% difference in predictions, demonstrating the model's ability to forecast on encrypted data with negligible variance.

3 more projects

Projects & Clients

Project Size & Rate

Minimum Project Size

$10,000+

Average Hourly Rate

$25 - $49/hr

Pricing Snapshot

Min Project Size

$10,000+

Avg Hourly Rate

$25 - $49/hr

Cost Rating

N/A

What Clients Have Said

"SPUNCH delivers quality projects within budget and timeline expectations based on verified client feedback."

Based on verified reviews and internal benchmark data

PROJECT BUDGETS

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Select a service

AI Strategy & Roadmap Consulting

LLM Integration & Prompt Engineering

Machine Learning Model Development

Custom AI Chatbot Development

AI Model Fine-Tuning & Training

DevOps Engineering

Managed IT Services

Industry Recognitions

SPUNCH Co-Founder in interview for Highload

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SPUNCH Crunchbase

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Locations

Headquarters

Beresteyskaya Square

Kyiv, Ukraine 02000

+380503531665

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