Dashbouquet Development

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Overview

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

50 - 249

Employees

2014

Year Founded

ABOUT DASHBOUQUET DEVELOPMENT

Reliable team aligned to deliver solutions ✅

Dashbouquet is a software development firm that focuses on creating tailored web and mobile applications, AI/ML solutions, and embedded systems. Since 2014, we have been assisting startups and businesses in transforming their ideas into dependable, scalable products, prioritizing quality, transparency, and long-lasting partnerships.

Tech Stack

JavaScriptPythonReduxNodeJSReact Native

Languages Spoken

English

HIGHLIGHTS

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

50 - 249

Employees

2014

Year Founded

Services

Focus

Industries

Clients

Services Provided+1 Services

20% Custom Web Application Development

20% Mobile App Development (iOS & Android)

8% SaaS Product Development

2% Blockchain & Web3 Development

5% AI Strategy & Roadmap Consulting

5% LLM Integration & Prompt Engineering

15% Custom AI Chatbot Development

20% Machine Learning Model Development

About the Team

OUR STORY

Our Story DB was established in 2014 by Dmitry Budko and Anton Shaleinikau, who previously launched several startups in fields such as mathematics, education, and music, collaborating closely with outsourcing firms. This background provided them with valuable insights into the challenges that startups and small to medium-sized businesses encounter, including the necessity for rapid iteration, inflexible project scopes, ineffective communication, and a lack of adaptability. To address these problems, they created Dashbouquet, a specialized development company.

Portfolio

🔥 US startup Pyroscope acquired by Grafana Labs

Pyroscope

By collaborating with Dashbouquet, Pyroscope doubled its revenue and saw a 30% increase in daily active users. The company secured a seed round in 2021 and gained several prominent clients from Silicon Valley. In March 2023, Grafana acquired Pyroscope, intending to integrate its solutions into their projects. The challenges Pyroscope faced included: - Finding a dependable IT partner to expand their in-house team and initiate projects promptly. - Rapidly providing new features and customizations for their new clients while redesigning the user interface. - Having previously worked with over 50 different contractors, the company recognized that sourcing, hiring, and collaborating was challenging and resource-intensive. After thorough evaluations, Pyroscope chose Dashbouquet for the necessary tasks due to their developer expertise, transparency in communication and processes, and alignment with their objectives. As part of the solution, some members of our team focused on cloud and back-end development, while others handled user interface design. We also addressed various requests from Pyroscope's clients, which included notable companies from Silicon Valley, marking a critical step towards potential acquisition by Grafana Labs. For example, our engineers developed a continuous profiler for Node.js, utilizing Node.js and C++ modules alongside Sampling Profiling technology. We also introduced numerous new features, including profiling and tracing functionality for Honeycomb, which helps in data storage and problem identification. As a result of working with Dashbouquet, Pyroscope enjoyed a twofold increase in revenue and a 30% rise in daily active users. With Y Combinator's backing, the U.S. startup successfully raised a seed round in 2021 and attracted several highly regarded clients, many of which are prominent Silicon Valley companies. For additional details on this case study, please visit our website.

Innovative AI-based dating assistant app

AI dating assistant

The AI Dating Assistant is a cutting-edge application designed to improve user interaction and success rates in online dating. Users can upload screenshots of profiles or chat conversations, and the app provides tailored assistance to help them formulate the ideal response. It allows modifications to replies by changing the tone or character traits, making each interaction personalized to fit the user's style and goals. Challenge: As a startup, the AI Dating Assistant faced difficulties in creating a reliable and efficient system for real-time interactions and customized user experiences. Initially utilizing Firebase and Firestore cloud functions, the team ran into challenges like cold starts and the inability to stream outputs from large language models (LLMs) in real-time. This required a transition to a more traditional backend to fulfill performance requirements and enhance user experience. Solution: To overcome performance issues, the AI Dating Assistant moved from Firebase and Firestore cloud functions to a Node.js backend hosted on Railway, supported by a PostgreSQL database. This change eliminated cold starts and facilitated real-time streaming of LLM outputs. The team assessed several LLM models, including GPT-4o, Claude 3 Sonnet, and Claude 3 Haiku, and chose models based on factors like accuracy, speed, and latency for different tasks. The application was developed for both iOS and Android using React Native, ensuring wide accessibility and a uniform user experience across platforms. Results: The AI Dating Assistant successfully established a scalable and responsive backend, improving the app's real-time functionalities. The application of various LLM models specific to certain tasks enhanced the accuracy and relevance of the dating advice provided, resulting in a better user experience and increased engagement rates. Tech Stack: Braintrust, Appsflyer, Mixpanel, PostgreSQL, RevenueCat, React Native, Node.js, ImageKit, Railway, Firebase, Firestore Cloud Functions.

AI-driven data platform for food and beverage

Unknown

This case study illustrates our support for Dashmote, a client that developed a sophisticated database system for tracking beverages listed on menus at cafes and restaurants throughout Europe. To achieve this, our team designed a tool that continuously gathered data from various sources, browsing multiple websites to collect information on available menus and drinks. Initially, we utilized a range of open sources to compile and refresh details about establishments in specific cities. Subsequently, we collected data using AWS S3 for storing interim results, argo workflows for managing workloads, proprietary OCRs for extracting information from images and PDFs, and headless chrome for data extraction. Additionally, we established several metrics to ensure the quality of the data and mitigate issues caused by website changes or downtime. Key features included the use of technologies such as AWS, S3, Kubernetes, Argo Workflows, Puppeteer, SpaCy, PyTorch, Grafana, and Prometheus. The project team comprised two software engineers, two data analysts, and one project manager, with a duration of 16 months. For more details on this case study, please visit our website.

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