# DATAFOREST DATAFOREST Reviews (29), Pricing, Services & Verified Ratings - Premier Verified - 5.0 out of 5 average review rating - 4 connections joined DATAFOREST's Network [Visit Website](https://dataforest.ai/?utm_source=clutch.co&utm_medium=referral&utm_campaign=directory) [Get a Custom Proposal](https://clutch.co/profile/dataforest) **Your AI & Data Engineering Partner** **Welcome to the forefront of AI and Data Engineering solutions** **where innovation merges seamlessly with your business goals.** Our team specializes in guiding organizations through the AI adoption journey. We collaborate closely with you to assess your unique needs, develop tailored AI strategies, and seamlessly integrate AI technologies into your existing systems. By leveraging the capabilities of AI, we empower your organization to enhance efficiency, foster innovation, and achieve sustainable growth. **W** **ith 15 years behind us, we've mastered a multitude of domains** **:** - AI-powered Web Applications - Advanced Data Analytics and Predictive Modeling - Scalable Data Gathering from Multiple Sources - Seamlessly Integrating and Managing Complex Datasets - Designing and Implementing ETL Pipelines and API Solutions - BI Insights and Dynamic Dashboards - Machine Learning and Natural Language Processing (NLP) - Automating and Optimizing eCommerce Ecosystems - Streamlining Operational Efficiency through Intelligent Process Automation - Enabling DevOps Excellence in AI and Data Science Environments ## Company Information - Minimum project size: $10,000+ - Hourly rate: $50 - $99 - Number of employees: 50 - 249 - 2 Locations: - Kyiv, Ukraine (Headquarters) - Tallinn, Estonia - Founded in 2018 - 1 languages: English - 1 timezones: Eastern European Time (EET) ## Services, Focus Areas, Industries, and Clients ### Service Lines - 35% AI Development - 35% BI & Big Data Consulting & SI - 10% AI Agents - 10% AI Consulting - 10% Web Development ### Focus Areas - AI Expertise: - 70% AI Recommendation Systems - 15% Machine Learning - 15% Natural Language Processing - AI Consulting Focus: - 60% AI Strategy - 20% AI Deployment - 20% AI Maturation - BI & Big Data Solutions: - 50% Microsoft BI & data solutions - 50% Other BI & data solutions - BI & Big Data Focus: - 45% Operations & process improvement - 35% Other BI & analytics - 10% Data Compliance, Fraud & Risk Management - 10% Marketing analytics ### Industries - 30% eCommerce - 10% Financial services - 10% Medical - 10% Information technology - 20% Retail - 20% Advertising & marketing ### Clients - 35% Small Business (<$10M) - 60% Midmarket ($10M - $1B) - 5% Enterprise (>$1B) ## Pricing Snapshot Average rating for cost based on this provider's reviews: 4.9 out of 5 **What Clients Have Said** *(This summary is based on verified Clutch reviews.)*: DATAFOREST offers competitive pricing, fitting various client budgets. Engagements range from $8,000 to $460,000, indicating flexibility for small to large projects. Clients appreciate the value for cost, timely delivery, and effective communication, emphasizing their expertise and responsiveness. **Most Common Project Size**: $10,000 to $49,000 based on 24 reviews *(Pricing information for this provider is based on reviews where the project size was available.)* ### Pricing by Service - Custom Software Development: $10,000 to $49,000 based on 12 reviews - BI & Big Data Consulting & SI: $10,000 to $49,000 based on 9 reviews - Other IT Consulting and SI: $10,000 to $49,000 based on 4 reviews - Web Development: $50,000 to $199,999 based on 4 reviews - AI Development: $10,000 to $49,000 based on 3 reviews - API Development: Confidential based on 2 reviews - AI Consulting: $10,000 to $49,000 based on 2 reviews - Data Annotation Services: $10,000 to $49,000 based on 1 review - E-Commerce Development: $10,000 to $49,000 based on 1 review - Generative AI: $10,000 to $49,000 based on 1 review - Digital Engineering: $50,000 to $199,999 based on 1 review - Computer Engineering: $10,000 to $49,000 based on 1 review - Cloud Consulting & SI: $10,000 to $49,000 based on 1 review - Application Testing: Confidential based on 1 review ## Reviews Clutch investigates each reviewer's identity and work history. Every review goes through a rigorous, human-led verification process to confirm the reviewer's identity, and reviews that we verify are visibly marked as 'Verified' so you can trust that they come from a real client. [Learn More](https://help.clutch.co/en/knowledge/how-clutch-verifies-reviews) ### DATAFOREST Review Insights Overall Review Rating: 5.0 - Quality: 5.0 - Schedule: 4.9 - Cost: 4.9 - Willing to Refer: 4.9 ### Top Mentions - Timely (14 mentions) - Communicative (10 mentions) - Proactive (5 mentions) - Unique expertise (5 mentions) - Efficient (4 mentions) - Detail-oriented (3 mentions) - High-quality work (3 mentions) - Knowledgeable (3 mentions) - Professional (3 mentions) - Transparent (3 mentions) - Creative (2 mentions) - Exceeds expectations (2 mentions) - Experienced (2 mentions) - Great project management (2 mentions) - Honest (2 mentions) - Accessible (1 mentions) - Collaborative (1 mentions) - Exceptional results (1 mentions) - Flexible (1 mentions) - Great team (1 mentions) ### Review Highlights **Positive Impact on Client Business Performance** Several clients reported significant positive impacts on their business performance due to DATAFOREST's solutions, including increased efficiency, reduced costs, and improved decision-making capabilities. **Impressive Project Management** Clients consistently praise DATAFOREST's project management, noting timely deliveries, proactive communication, and effective collaboration. Their ability to adapt to changing requirements and maintain a structured approach is widely appreciated. **Strategic Partnership and Long-Term Engagements** DATAFOREST often becomes a strategic partner for clients, with many engagements evolving into long-term collaborations. Their ability to align with client goals and deliver strategic value is a key factor. **Challenges with Communication Efficiency** A few clients mentioned that while communication is generally good, there could be improvements in communication efficiency and frequency to ensure all parties are aligned and informed timely. **High Client Satisfaction and Loyalty** Many clients express high satisfaction with DATAFOREST's services, often maintaining long-term partnerships. The company's ability to deliver on promises and exceed expectations contributes to client loyalty. **Client-Centric Approach and Transparency** The company's client-centric approach and transparency in operations are key strengths. Clients feel informed and involved throughout the project lifecycle, enhancing trust and collaboration. **Consistent Delivery of High-Quality Outputs** DATAFOREST consistently delivers high-quality outputs that meet or exceed client expectations. Their attention to detail and commitment to quality are frequently praised by clients. **Strong Communication and Collaboration** DATAFOREST is commended for its clear and proactive communication. They ensure regular updates and engage in collaborative problem-solving, which fosters strong client relationships and successful project outcomes. **Areas for Improvement in Initial Scoping** While generally positive, some clients noted that initial project scoping could be improved to anticipate additional features and reduce scope changes later. A more structured discovery phase may help in this regard. **Expertise in Data Engineering and Automation** DATAFOREST is highly regarded for its expertise in data engineering, creating efficient data pipelines, AI integration, and automation processes. Clients appreciate their technical skills in handling complex data challenges across various industries like finance, marketing, and automotive. ### DATAFOREST Reviews #### Data Consulting Services for Technology Advisory Firm (Featured Review) **The Project** - Services: BI & Big Data Consulting & SI - Project size: Less than $10,000 - Project length: Nov. 2023 - Ongoing **Project Summary**: DATAFOREST was hired by a technology advisory firm for their data consulting services. The team is responsible for managing data and making it accessible through BigQuery. **Review Rating**: 5.0 - Quality: 5.0 - Schedule: 5.0 - Cost: 5.0 - Willing to Refer: 5.0 **The Reviewer** Managing Partner & Founder, Technology Advisory Firm - Industry: Sports - Client size: 1-10 Employees - Review Type: Online Review - Verified **The Review** — Aug 15, 2024 **Feedback Summary**: DATAFOREST's work has been met with positive acclaim, thanks to their regular cadence of delivery. The team is highly receptive from a workflow standpoint, and internal stakeholders are impressed with the service provider's consistent availability. ""If we ever have issues, DATAFOREST is quickly on hand to resolve them."" **BACKGROUND** Please describe your company and position. I am the Managing Partner and Founder of a sports company Describe what your company does in a single sentence.We're an advisory with a difference. Utilising proprietary technology, data and third-party platforms to serve our partners better. **OPPORTUNITY / CHALLENGE** What specific goals or objectives did you hire DATAFOREST to accomplish?Acquiring data from third-party platforms **SOLUTION** How did you find DATAFOREST?Online SearchClutch SiteWhy did you select DATAFOREST over others?High ratingsPricing fit our budgetGood value for costHow many teammates from DATAFOREST were assigned to this project?2-5 EmployeesDescribe the scope of work in detail. Please include a summary of key deliverables.Acquiring data from third-party platforms on a regular cadence and making it available on Big Query **RESULTS & FEEDBACK** What were the measurable outcomes from the project that demonstrate progress or success?Regular, accurate data deliveryDescribe their project management. Did they deliver items on time? How did they respond to your needs?Things don't always go according to plan, but if we ever have issues, DATAFOREST is quickly on hand to resolve them.What was your primary form of communication with DATAFOREST?Email or Messaging AppWhat did you find most impressive or unique about this company?Ability to contact them regardless of the time of the day or day of the week.Are there any areas for improvement or something DATAFOREST could have done differently?No improvements needed. --- ## Portfolio & Awards ### Ottawa Healthcare Network A multi-location healthcare network in Ottawa needed better operational visibility across clinics, care teams, and patient workflows. As the organization expanded, data was fragmented across EHR, telehealth, scheduling, billing, insurance, and communication systems. Leadership lacked a reliable real-time view of patient flow, provider utilization, scheduling performance, and operational bottlenecks. The client needed a solution to: - Connect disconnected healthcare and operational systems.- Standardize workflows across multiple clinic locations.- Give leadership real-time visibility into clinic performance.- Reduce manual coordination between teams and systems.- Improve scheduling, provider utilization, and capacity planning. Our Solution - We implemented CareOps AI OS, an AI-driven operational intelligence platform for healthcare operations. - The platform unified data from 5 core systems into a single operational model. We connected EHR, telehealth, scheduling, billing, and communication data, then structured it into a consistent dataset for analytics, workflow automation, and decision support. - We also introduced standardized SOPs across locations, real-time dashboards for leadership, and predictive scheduling models that analyze appointment patterns, no-show risk, and demand fluctuations. The system helps clinics identify pressure points earlier and optimize provider capacity. Results 5 Systems Unified: The healthcare network gained one synchronized operational layer across core systems. 35% Less Administrative Workload: Automation reduced manual coordination and repetitive operational work. 25% Fewer Operational Bottlenecks: Real-time visibility helped teams detect and address issues faster. 100% Unified Operational Data Model: Fragmented clinic data was consolidated into one structured foundation. Conclusion CareOps AI OS helped the healthcare network move from fragmented operations to a unified, AI-driven management platform. ### Single Source of Truth for U.S. Manufacturer A U.S.-based industrial manufacturer growing through acquisitions needed a scalable data platform to unify fragmented ERP, sales, customer, and location data. Each acquired company used different systems, formats, and naming conventions, making executive reporting slow, manual, and difficult to trust. Project Overview The client needed a solution to: - Unify ERP and acquisition data into one reporting model - Reduce manual CSV and Excel consolidation - Standardize customer, sales, and location data across entities - Improve visibility into YoY sales, customer retention, and acquisition performance Our Solution We designed and implemented a scalable Medallion Architecture on Google Cloud Platform, featuring: * GCP Data Architecture: Built Bronze, Silver, and Gold data layers to structure raw, cleaned, and business-ready data. * Automated Data Processing: Developed a Python-based ingestion and validation framework to map, clean, and harmonize acquisition data. * ERP-Agnostic Integration: Created a standardized layer that processes exports from different ERP systems without rebuilding pipelines for every acquisition. * Power BI Reporting: Connected trusted Gold-layer datasets to Power BI for executive dashboards and acquisition-ready reporting. Results 70% Faster Acquisition Data Onboarding: New company data can now be integrated much faster through reusable processing templates. 80–90% Less Manual Processing: Manual Excel-based consolidation was replaced with automated validation and transformation workflows. 100% Unified Reporting Across Entities: Leadership gained a single source of truth for sales, customer, location, YoY performance, and retention reporting. Conclusion This data platform helped the manufacturer move from fragmented M&A reporting to an automated, scalable reporting foundation. With clean data pipelines, standardized models, and Power BI-ready outputs, the client can make faster decisions and support future acquisition growth with less operational. ### Reporting Platform for U.S. Dessert Franchise A U.S.-based dessert franchise operating across 34 states needed a scalable reporting platform to improve visibility across all locations. As the business expanded, fragmented data, complex nested structures, inconsistent sales formulas, and limitations in the existing solution made reporting slow, unreliable, and difficult to trust. Project Overview The client needed a solution to: - Consolidate franchise performance data across multiple locations.- Process new store data without duplication.- Standardize complex data into a clean reporting model.- Fix inconsistent sales metrics across dashboards.- Give leadership fast, validated performance visibility. Our Solution We rebuilt the reporting flow so franchise data could move from store uploads to executive dashboards quickly and accurately. First, we cleaned and organized complex store data into a structured reporting model. Then we improved the AWS Lambda process so it handled only new files and avoided duplicate data processing. We also reviewed and corrected the sales formulas that caused inconsistent numbers across reports. Finally, we connected the validated data to PostgreSQL and Amazon QuickSight, giving leadership clear dashboards with trusted performance metrics across all franchise locations. Results <5-Minute Data-to-Dashboard Latency: Leadership can now access updated performance data in minutes. 11 Executive Dashboards: The franchise gained validated dashboards for tracking performance across locations. 100% Reporting Issues Resolved: Corrected sales logic and standardized reporting restored trust in business metrics. Conclusion This platform helped the franchise move from fragmented reporting to a trusted, scalable performance visibility system. With clean data processing, validated sales logic, and executive dashboards, leadership can now monitor growth, compare locations, and make faster decisions across current and future stores. ### AI-Powered Virtual Hairstyle Try-On A U.S.-focused online beauty platform needed to create a new AI-powered product that would turn existing website traffic into revenue. The client wanted users to upload a selfie and instantly preview realistic hairstyles while preserving facial similarity and natural hair texture. The client needed a solution to: Generate realistic hairstyle try-ons from a single user selfie.Preserve the user’s facial features, skin texture, and identity.Create natural-looking hair texture across multiple styles.Launch a free trial widget to attract and convert website visitors.Manage registrations, paid transactions, generated photos, and support requests. Our Solution We developed a Gen AI hairstyle try-on platform using Stable Diffusion, multimodal LLMs, face swap, face analysis, and image processing technologies. The system allows users to upload a selfie, select hairstyle templates, and receive realistic generated photos in under 30 seconds. We tested different model settings, prompts, and face swap approaches to improve face resemblance and natural hair quality. We also built a free trial website widget with 21 standard hairstyles. The widget collects user emails during registration, helping the client turn website visitors into product leads. To support operations, we created an admin panel where the client can monitor user registrations, paid transactions, generated images, and support requests. Results 94% Model Accuracy: The solution generates high-quality hairstyle try-ons with strong visual realism. 90% User Face Similarity: Generated images preserve the user’s facial identity and natural look. <30-Second Photo Delivery: Users receive hairstyle previews quickly, improving product experience. 60+ Hairstyle Templates: The platform supports a broad range of realistic hairstyle options. Conclusion With this Generativ AI solution , fast delivery, lead capture, and admin control, the client gained a scalable digital product that improves user engagement and creates a new revenue channel. ### AI Voice Agent for Cold Calling A leading affiliate CPA network wanted to automate outbound sales conversations without losing the quality of human interaction. The client needed a real-time voice-to-voice AI agent that could speak naturally, handle noisy calls, use sales techniques, and connect with their internal CRM and ATS systems. Project Overview The client needed a solution to: Automate cold calling with natural voice conversations.Train the AI agent on sales scripts, product knowledge, objections, and upselling.Maintain strong speech recognition in noisy environments.Integrate call data with CRM and ATS workflows.Scale outreach while reducing cost per interaction. Our Solution We developed a real-time AI voice agent for two-way sales conversations. The system combines speech-to-text, voice activity detection, LLM reasoning, text-to-speech, and SIP-based call handling. We trained the AI using sales calls, scripts, and marketing materials, then built a RAG-based knowledge layer to support realistic sales conversations, objection handling, and upselling. To improve call quality, we added custom noise suppression so the agent could understand users even with background noise. We also integrated the solution with the client’s CRM and ATS to automate call logging, data synchronization, and workflow updates. Results <450 ms Response Latency: The AI agent responds fast enough to support natural, real-time conversations. 1:1–1.5 Sales Quality Ratio: The voice agent matches or exceeds human sales performance in key scenarios. Lower Cost per Interaction: Operating costs are under $4/hour, making outreach more scalable. CRM & ATS Automation: Call data is logged and synchronized automatically across internal systems.Conclusion  With real-time voice interaction, sales-trained AI, noise handling, and CRM/ATS integration, the company can increase outreach capacity, reduce operational costs, & maintain consistent conversion quality. ### Databricks Migration for Healthcare Lab A U.S.-based pathology laboratory needed to replace its fragmented Azure SQL environment with a scalable Databricks Lakehouse. The legacy system covered only part of the required functionality, lacked observability, and created growing limitations for analytics, compliance, and cost control. Project Overview The client needed a solution to: - Migrate diagnostics and billing data from Azure SQL to Databricks.- Unify 21 data sources into one governed analytics platform.- Convert legacy SQL scripts into automated production-ready jobs.- Support HIPAA-ready handling of sensitive patient data.- Improve observability, reporting, and AI/BI readiness. Our Solution We designed and implemented a Databricks-based Lakehouse platform, featuring: - Databricks Lakehouse Migration: Migrated data and pipelines from Azure SQL into a scalable Databricks environment.- Medallion Architecture: Built Bronze, Silver, and Gold layers to structure raw, cleaned, and business-ready data.- Automated Pipelines: Converted legacy SQL scripts into scheduled Databricks jobs with alerts, error handling, and real-time CDC ingestion.- Compliance-Ready Processing: Anonymized sensitive patient data to support secure healthcare analytics.- Data Observability: Added lineage tracking, monitoring dashboards, schema documentation, and alerts to improve data reliability.- Self-Service BI & AI: Delivered dashboards, SQL logic for 20 dashboards and widgets, 3 Genie spaces, and an ML-ready feature store with a denial prediction model. Results ~50% Compute Cost Reduction: Optimized pay-per-use architecture reduced annual compute costs from about $20K to about $10K. 21 Data Sources Unified: Diagnostics and billing data were consolidated into one governed Medallion Architecture. 3 Genie Spaces Deployed: Business users gained self-service BI capabilities for faster insights. AI-Ready Data Foundation: The client now has a scalable Lakehouse for reporting, predictive analytics, and future LLM-powered BI workflows. ### Software for Customer Emotion Tracking Client: State-operated banking company headquartered in Italy, active in several European countries with revenue of more than $40 bln. Challenge: The main goal is to measure the quality of customer satisfaction during the cooperation with each specific bank manager and create a system for tracking customer emotions. Results: Created a Computer Vision solution by means of cameras placed in front of each manager tracking customers’ faces and their emotions (positive, negative, neutral, etc). Analysis of customer emotions enables the administration to track customer satisfaction, as well as the work of each operator individually, hence reducing employee scarcity and the company’s expenses, thereby optimizing the work of departments. The system also analyzes conversation flows in order to control the quality of the managers' work. ### Stock Relocation Client: One of the largest Pharmacy networks in Eastern Europe with a 13k product list in more than 2k drug stores in 30 regions. Challenges: Create an optimal assortment list at each drug store, taking into account their individual specifics. Results: The mathematical model was built according to the location of drug stores, housing density, the routes of metro stations/transport junction, location of hospitals, fitness facilities, shopping/business centers, health, educational institutions/educational facilities, etc. Based on the 48 months of the company’s sales data analysis, were built clusters to determine the optimal assortment list for each drug-stores; Integration with POS terminals. Technologies: Core stack - Python, Pandas, Pyspark, SciPy, TensorFlow, Java; Database - PostgreSQL, Hadoop, Oracle, Mysql.   ### E-commerce Automation Client: US dropshipping company specializes in home furniture and accessories, deals with more than 130k orders monthly, and owns more than 1500 local stores with varying prices on the same products. Results: Created the system with a web interface for monitoring all product availability (12 mln items) with the biggest price variations. Built the full monitoring process (60 mln pages) with custom scripts through distributed microservice architecture. Added cross-checks of the same products on Amazon, Walmart, Lowe’s, etc. Created daily reports with wide statistics. Cashback tracking. Provided access to the system to the client's team with different roles. The tailor-made algorithm for the price arbitrage products gave a positive annual income impact up to $600k. 6 FTE reduction saved $240k annually. Reduced infrastructure annual costs from $60k to $12k. Improved customer experience. Reduced the number of canceled orders by 69%. ### Financial Intermediation Platform Client: The Infrastructure Deal Network - IDN   Project subject: Develop a deal origination platform for private equity investments in infrastructure-related sectors.   Challenges:    Build from scratch a secure interactive B2B platform with sign-up functionality to connect investment firms to proprietary investment opportunities. Build Internal Security Chat for Advisers and Investors. Empower application development with AI functionality.   Results:   Built web-native platform for interacting with Advisors, Investors, and Admins, with registration forms, custom dashboards, and the ability to place and conduct investment transactions. Developed and implemented AI matching algorithms in accordance with specified criteria for the optimal selection of transaction parties. Built a secure system for documents exchange and internal chat for transaction parties. Technologies: Web stack - Back-end - Python/Django REST Framework / Front-end - ReactJS; Database - RDS (Postgres);  Cloud solution: Amazon. ### Infrastructure Cost Reduction Project Subject:  ML startup has encountered infrastructure cost issues during the extensive growth. The main goal was to decrease the monthly operational cost ($75 K) for a large data-driven platform that handles  ~ 240 bln entries monthly ( ~ 30TB), storing raw data for 12 months on AWS. Challenges:  Create AWS infrastructure that performs 2k queries per second and has 99.9 % service availability. Optimize redundancy and 2 times decrease in the cost of infrastructure. Create a failover strategy and possibilities for a larger scale. Results: Cost reduction from $75k to $22k per month with performance 30% over SLA. Removed managed services and set up a self-hosted DB over EC2. Used a cluster of servers for DB sharding, adding Elasticsearch, Kafka, and Redis for different streams of data based on industry standards. Create master/master-slave mirroring in different regions to have a failover strategy. DB architecture was tuned to execute most often queries faster. Updated ETL pipelines to reduce the load on DB.  Technologies: AWS EC2, AWS RDS, PostgreSQL, Python, Kafka, Elasticsearch, Redis. ### Large-scale Data Parsing Client: Law Consulting firm in South America. The client automates, manages, classifies, and stores legal cases files, documents, and contracts of all kinds. Challenges:  To obtain data from millions of pages and documents through five different court websites and to avoid overloading them.  To collect data on a daily basis from the legal law case files. To collect not only structured data, but also embedded PDF, Word, JPG files, and other unstructured data. To keep scripts running continuously, to carry new cases files and update old ones if something changed.  Results: Created distributed architecture with Linux nodes and a dynamic pipeline that allows managing high peaks and set priorities. We scrape new cases files immediately during the daytime based on the site traffic and make massive updates during the nighttime. To overcome bot protection and to crawl 14.8 million pages daily we use proxies and special AI technologies. Each day we download about 14 Gb of necessary data. To keep data we use a cloud SQL database with daily dumps to Elasticsearch, meantime files directly upload to Elasticsearch. Technologies: Core stack - Python; Distributed task execution - Celery; Database - PostgreSQL; Search-engine - Elasticsearch; Cloud solution: GCP. ### Supply Chain Dashboard Client: A transnational company focused on the production of FMCG, products of which are represented in about 150 countries around the world. The company owns more than 400 brands. Challenges: The Reporting department (15 employees) receives daily massive amounts of data (invoices, payments, sales reports, etc) from suppliers, vendors, contractors. The data (PDF files, Excel, Google Sheets, etc from more than 100 systems of suppliers, contractors, vendors) is copied manually by the Reporting department. The Reporting department processes unstructured data analyzes information for trends and generates management reports. Operation process optimization and automation by building integration with all data sources. The reporting system for management and stakeholders development. Results: The developed system unifies unstructured data from all the sources of suppliers, vendors, contractors, and stores it in a structured way. The system monitors the data integration and identifies anomalies. The dashboard with multi-level filtering and dynamic mapping: consolidated dashboard for management; goods delivery reporting; KPIs performance control and prompt notification of the indicators deviations.  access for different groups of employees. 900 hours of manual work reduced monthly. Reports for management and stakeholders. Technologies: Core stack - Python, ReactJS, Django, Pandas; Database  - PostgreSQL; Cloud solution: AWS.   ### Web App for Dropshippers Client: The Software company that provides solutions to eCommerce store owners.   Project: Web App for Dropshippers   Project subject: Develop a subscription-based web app for dropshippers, where subscribers every week can get access to best-selling products sorted by categories from AliExpress.   Challenges: Develop from scratch a responsive web-native app with sign-up functionality (user and admin parts). Collecting and analyzing product/seller data from AliExpress/Alibaba. Integrate data with FB and TikTok.  Build a system to calculate subscriber profit and product statistics based on various factors and indicators. Mapping and displaying the best-selling product data within the app design (description, price, competitors, product ratings, etc). Integration with the payment systems. Results: Designed and built a mobile responsive web app with the user and admin parts. Built high-load scraping algorithms for extracting data from AliExpress/ FB/TikTok. Developed scrapers that get competitor product data from Shopify stores.  Developed AI algorithms that calculate for users the main economic indicators and profit margins. Developed payment system integration with various subscription functionalities.   Technologies:   Scraping stack - Python; Database -  PostgreSQL; Web stack - Back-end - Python/Django REST Framework / Front-end - ReactJS; Cloud solution: AWS. ### Business Digital Transformation Client: Ukrainian distributor for medical devices and drugs.   Challenges:  Reinvent process workflow from legacy (manual, off-line, paper) approach to new ways of working and thinking using digital, social, mobile, and emerging technologies. Improve customer experience and minimize operational risks. Change the way how suppliers, customers, and contractors interact with each other.  Our solution: CRM; Warehouse management system; Products delivery tracking; Suppliers, Customers, and Contractors working panels and dashboards; Built the whole process paperless by implementing a tailor-made web application that includes: the delivery process by integrating the application with a local delivery company; QR codes printing and tracking procedures; data-driven reporting. Technologies: Backend: Python /Django; Frontend: React; Database: PostgreSQL; Cloud solution - AWS. ### Bank Data Analytics Platform Client: Intellidex - financial services and consulting company with operations in South Africa, the UK, and the USA.   Project name: Bank Data Analytics Platform   Project subject: Develop a web app for the Client’s subscribers to query analytics about various banks.   Challenges: Build an interactive B2B web application with custom dashboards and analytics features. Develop a system that interrogates various sets of financial data, calling up time series. Empower application development with AI functionality.     Results: Web-native development from scratch with a design framed within the Client's existing website and UI/UX front-end to facilitate queries, algorithms, and visualizations. High-loaded AI scraping algorithms to generate a real-time database of financial bank data from various open-source websites and financial institutions. The developed system has calculations capacity that enables users to run algorithms on the data, such as comparing, rebasing, etc. and presenting data graphically, and/or downloading it in PDFs.   Technologies: Core stack - Python; Database - PostgreSQL; Web stack - Back-end - Python/Django REST framework / Front-end - ReactJS; Cloud solution: AWS. ### Performance Optimisation & Bottlenecks Elimination Project subject: FinTech company was looking for a DevOps partner to optimize their finance platform. The Infrastructure performance has undergone degradation. Processes stuck in queues for too long. The application started to become an operational bottleneck.  Challenges: Increase application performance, stability, and resilience. Identify and solve bottlenecks and vulnerabilities. Reduce operational costs.  Results: Performed a technical audit of the current AWS infrastructure. Created bottlenecks monitoring system. Re-developed parts of inefficient SQL queries and data pipelines.  Implemented Horizontal scaling and Microservice approach based on Docker managed by Kubernetes. 1,000% performance boost and 20% cost optimization. Technologies:  AWS, Kubernetes, Docker, Nginx. ### Infrastructure Audit and Intelligent Notifications Project subject: The US eCommerce company wants to unify all of its architecture in one cloud provider to eliminate legacy approaches and unmanaged servers. Due to technical debt and active development in the last 5 years, logging and notification systems have a lot of gaps that do not allow the Client to identify issues and react to them promptly. Challenges: Absence of technical documentation, and outdated business requirements. Complex IT infrastructure that utilizes multiple cloud providers.  Lack of system monitoring and any issue notification.  Lack of a software architect strategy. Results:  The current architecture was analyzed and raised recommendations for:  Single cloud provider taking into account system spread;  CI/CD process for new code deployment;  Server unification;  Security and vulnerability mitigation actions. Developed a strategy for Infrastructure and application scaling. Developed a monitoring and notification unified approach. Zabbix logging is implemented, hence all servers and DBs are covered with monitoring. PagerDuty was settled on the call system. Creating a monitoring system has improved reaction speed and reliability and had a 200% impact on performance. Technologies: GCP, AWS, UBUNTU, PagerDuty, Zabbix, Grafana, MongoDB, PostgresDB. ### Clutch Awards - [Clutch Award Badge](https://shareables-prod-static.clutch.co/badges/clutch_1000_2023_award.svg) - [Clutch Award Badge](https://shareables-prod-static.clutch.co/badges/global_award_2023.svg) - [Clutch Award Badge](https://shareables-prod-static.clutch.co/badges/top_clutch.co_bi__big_data_company_2023_award.svg) - [Clutch Award Badge](https://shareables-prod-static.clutch.co/badges/top_clutch.co_bi__big_data_company_2026.svg) - [Clutch Award Badge](https://shareables-prod-static.clutch.co/badges/top_clutch.co_artificial_intelligence_company_2023_award.svg) ### Industry Recognitions - 20 Everyday Challenges For Tech Leaders - 20 Essential Steps For Successful Automation - Forbes Technology Council Partner - Best WebDev Companies You Should Consider in 2024 - Best AI Development Development Companies in 2024 - Databricks Consulting Partner - Emerging Europe Interview - Forbes Council - Top e-Commerce Startups in Kyyiv - What is Data Engineering in Data Science - #9 Top Big Data Consulting Companies - Top IT Outsourcing Companies - Top Python Development Companies - #2 Best Web Dev Companies to Consider in 2022 - #1 BEST Web Dev Companies to Watch out For in 2022 - #2 on Best 48 E-commerce Development companies - #4 on Best 38 Big Data companies - FUTURE OF IT REPORT 2022 - Top Big Data And BI Company - #2 Top Ukraine Database Startups - #1 Top Kyiv Database Startups - Top Rated Plus Agency 100% Job Success on Upwork - #2 The Most Innovative Database Company in Kyiv ## Key Clients - AM Trading ## Packages ## About the Team ### Our Story DATAFOREST gives US and global executives access to a senior in-house team of 150+ engineers, data scientists, PMs, and cloud specialists led by experts with healthcare, banking, insurance, retail, e-commerce, cybersecurity, and consulting experience. Since 2017, we’ve delivered 250+ projects for 200+ clients, combining data engineering, AI, and high-load software development to reduce delivery risk and create measurable business value. ## Verification Premier Verified means Clutch independently confirmed DATAFOREST is a legally registered business, financially sound (Creditsafe: Moderate Risk), and backed by 26 verified client reviews averaging 5.0 stars. - Verified Client Reviews: 26 - Overall Review Rating: 5.0 Last Updated: 2026-08-11T14:20:31Z ### Business Entity - Business Entity Name: Dataforest OÜ - Source: Centre of Registers and Information Systems - Jurisdiction of Formation: Estonia - Date of Formation: May 21, 2019 - Status: Active - Last Updated: November 23, 2021 - ID: 14727250 ### Credit Report Results - International Credit Risk Assessment: Moderate Risk - Source: Creditsafe - Last Updated: September 19, 2024 ## Locations (2) ### Kyiv, Ukraine (Headquarters) - Solomenskaya, 15a - Kyiv 02000 - Ukraine - 50 - 75 employees - Phone: +16469050356 ### Tallinn, Estonia - Sakala 7 - Tallinn 10141 - Estonia - 50 - 75 employees - Phone: +16469050356 ## Connections - Nir Levy (GG Digital Marketing Agency) - Uliana Piasta (Progresia) ## Contact DATAFOREST [Send a message](https://clutch.co/profile/dataforest) ### Connect on Social - [LinkedIn](https://www.linkedin.com/company/dataforest) - [Facebook](https://www.facebook.com/dataforest/) - [X](https://twitter.com/dataforestai) - [Instagram](https://www.instagram.com/dataforest_agency/)