BroutonLab

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Overview

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

10 - 49

Employees

2017

Year Founded

ABOUT BROUTONLAB

Strategic Tech Partnership for AI/ML Startups

At our AI/ML startup studio, we focus on crafting tailored solutions for businesses aiming to harness the advantages of artificial intelligence and machine learning. Our knowledgeable team has extensive experience in developing customized solutions that cater to our clients' specific needs, allowing them to enter the market more quickly and reach their objectives more effectively. Additionally, we invest in startups and serve as co-founders, assuming the role of CTO to provide comprehensive technical support for their success. Whether you need a minimum viable product or a sophisticated solution, we possess the expertise and experience necessary to help you achieve your aspirations. Don’t hesitate—get in touch with us today to see how we can assist your business in thriving in the digital era.

LANGUAGES SPOKEN

ArabicEnglishHebrew

HIGHLIGHTS

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

10 - 49

Employees

2017

Year Founded

Services

Focus

Industries

Clients

Services Provided

70% Custom Web Application Development

30% SaaS Product Development

80% Machine Learning Model Development

20% AI Strategy & Roadmap Consulting

About the Team

OUR STORY

Our company is a dedicated group of experienced engineers, machine learning researchers, and product developers who prioritize delivering tangible results. Unlike typical outsourced firms, our team members bring years of hands-on experience from operating companies, allowing us to make well-informed, impactful decisions. Led by Michael Yurushkin, our founder and CEO with a PhD in Applied Mathematics, we leverage deep academic insights alongside practical expertise. Michael's commitment to advancing AI knowledge extends to his role as a university instructor, ensuring our foundations remain strong. Our team actively contributes to the scientific community, publishing peer-reviewed research on topics like pattern recognition, machine learning, parallel computing, and computational biology in esteemed platforms such as Elsevier, Springer LNCS, IEEE, and ACM.

Portfolio

Crop Yield Analysis for an AgTech Startup

Unknown

Client: The client is a US-based AgTech and Robotics startup that utilizes autonomous rovers for ground-level image capture and deep learning-driven analysis to assess crop health and estimate yields for growers. They aim to automate image preprocessing, localization, and analysis to enable automatic crop yield predictions through Deep Learning and Computer Vision algorithms. The client sought a long-term partnership with BroutonLab for ongoing development of Deep Learning models and backend engineering to effectively manage their complex datasets and infrastructure. Solution: Leveraging a custom dataset that the client gathered and labeled, BroutonLab's data scientists developed a model for plant segmentation (instance segmentation) with a tailored pipeline. To amalgamate overlapping data from multiple images, various models and algorithms were evaluated for automatic panorama generation and 3D reconstruction, creating point clouds from different views and tracked features within images. BroutonLab devised a specialized 3D algorithm to reconstruct plant clusters from images, accounting for both visible and hidden fruits. Machine Learning techniques and data analytics were employed to incorporate additional known data from the farmers. Ultimately, BroutonLab developed algorithms to estimate total yield by counting individual fruits and assessing cluster weights. Our efforts also involved optimizing internal processes using AWS Lambda. Results: The project is ongoing, and BroutonLab's contributions have enabled the client to attract more customers, improve turnaround times, and secure investments from backers.

AI for Resume Parsing and Job Matching

Opening.io

Client: The client is a startup located in Dublin that specializes in creating an AI-driven talent recommender engine for vendors in HR technology, businesses, and staffing agencies. Problem: The client aims to build a resume parser and summarizer that can handle unstructured CVs submitted by candidates in various document formats. This engine should extract crucial information about candidates, such as personal details, seniority level, duration of employment at different companies, and skill-related experience. Additionally, it should generate a concise summary paragraph about the candidate. They approached BroutonLab to create a customized engine, despite having a relatively limited labeled training dataset. Solution: The final solution can be divided into several components. The project focused on analyzing two types of information: resumes and job postings sourced from public job boards and paying clients. Document labeling was conducted using outputs from several commercial resume and job post parsers. To address various NLP challenges, we implemented stacked bidirectional GRU/LSTM recurrent layers. We explored different vector embeddings, including fastText and a custom Word2Vec. GoLang was utilized for deploying the developed neural networks, and communication between services was established using the gRPC protocol. Results: The client received a comprehensive solution for automated resume parsing and summarization, which contributed to a 20% increase in market share and enabled them to engage with larger clients, with 80% of their clients being Fortune 100 companies. Eventually, the startup was successfully acquired by a major US cloud talent platform, leading to the launch of a new AI solution within the larger organization.

Automated Sports Fan Video Generation

15SOF

**Client** The client is a mobile app developer based in the US that has created a sports video platform. This platform allows fans to sign up and receive videos of themselves from live events directly on their phones. **Problem** The client aims to automate the process of creating fan videos from extensive video content. Currently, it's a time-consuming task to sift through three-hour videos to find shots of fans, recognize them, and compile a video montage. They approached BroutonLab for assistance in developing a system for gender recognition, facial detection and recognition, shot quality assessment, and video captioning to automate the production of fan videos. **Solution** The solution is a sophisticated engine divided into multiple models. We organized the data collection and labeling process and established pipelines to manage data input and output using Luigi and DVC. Several advanced models were tested and utilized throughout the project, including ResNet, Xception, EfficientNet, and custom architectures. These models were deployed as microservices via Flask on Azure Cloud. **Results** Our solution has decreased the labor costs associated with video preparation by 90%. Thanks to the quick response times for fan requests, the client secured 20 additional contracts with TV channels, and the time required to process each video was reduced from three hours to just 30 minutes. This significant reduction in workload and expenses related to video preparation has enabled our client to concentrate on other venues and aspects of their platform.

6 more projects

Projects & Clients

Project Size & Rate

Minimum Project Size

$10,000+

Average Hourly Rate

$50 - $99/hr

Pricing Snapshot

Min Project Size

$10,000+

Avg Hourly Rate

$50 - $99/hr

Cost Rating

N/A

What Clients Have Said

“

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

Based on verified reviews and internal benchmark data

MOST COMMON PROJECT SIZE

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Select a service

Custom Web Application Development

SaaS Product Development

Machine Learning Model Development

AI Strategy & Roadmap Consulting

Industry Recognitions

Best Data Research & Consulting Company - Russia

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Locations

Headquarters

Bar Rav Hai David 30

Haifa, Israel

+38761889825

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