CodeIT

For CodeIT

Is this your company?

Edverise AI compiled this profile from public information so buyers could find you. Claim it to edit every field, add your portfolio and respond to reviews.

Claim & verify for free

Manage the listing yourself. Verify by work email or company domain to earn the verified badge.

Claim this profile

Suggest an edit

Not ready to claim? Send a correction and our team will review and update verified factual errors.

Suggest an edit

Overview

$50,000+

Min Project Size

$25 - $49/hr

Avg Hourly Rate

50 - 249

Employees

2007

Year Founded

ABOUT CODEIT

CodeIT AI Lab - From Concept to Scalable AI

We are a company focused on engineering, boasting 18 years of experience in delivering tailored software solutions for businesses around the globe. In 2023, we recognized the significant potential of artificial intelligence and established a dedicated AI Lab—an advanced unit within our Engineering Domain—to assist organizations in strategically implementing AI and realizing genuine business benefits. What CodeIT Offers: - Free AI Strategy Session: A no-obligation discovery call to explore potential opportunities. - Complete AI Implementation: Covering everything from consulting to large-scale deployment. - Quick Proof of Concept Development: Validate AI ideas in just 3 to 5 weeks. - Expert AI Team: A group of over 10 proficient AI/ML engineers with more than 12 years of combined experience. The AI solutions we provide include: - Personalization - AI chatbots - Content summarization - Recommendation systems - Knowledge base search and responses - Text-to-SQL API integration - Document retrieval - Entity linking Industries We Serve: We have extensive expertise in developing custom digital and AI-driven solutions across various sectors, including: - Healthcare - Manufacturing - Supply Chain & Logistics - Fintech - Insurance - Trading Platforms - E-learning & EdTech - E-commerce & Retail CodeIT operates through 5 R&D and sales offices located in the European Union, the United States, and Ukraine. Our company adheres to GDPR standards to ensure safe collaboration environments. To learn more about our AI services, visit: https://codeit.us/services/ai-services For inquiries, feel free to contact us at hello@codeit.us and we’ll respond within 24 hours.

Tech Stack

FlutterNodeJSJavaScriptPython

Languages Spoken

English

HIGHLIGHTS

$50,000+

Min Project Size

$25 - $49/hr

Avg Hourly Rate

50 - 249

Employees

2007

Year Founded

Services

Focus

Industries

Clients

Services Provided

10% AI Strategy & Roadmap Consulting

13% Custom AI Chatbot Development

10% Machine Learning Model Development

7% Conversational AI & Voice Assistant Development

10% LLM Integration & Prompt Engineering

28% Custom Web Application Development

15% Backend Development

7% Frontend Development

About the Team

OUR STORY

Our Journey Began in 2007 when three individuals came together with a simple conviction: exceptional software is rooted in a deep understanding of the underlying business. Starting as a small team in Ukraine, we have expanded into a global organization with over 200 specialists and offices in Ukraine, Poland, the US, the UK, and Estonia. Throughout our growth, we have attained ISO/IEC 27001 certification and established a focused AI & ML division. Our approach goes beyond coding; we fully engage with our clients' objectives to provide solutions that yield tangible business outcomes.

Portfolio

Drug Discovery Acceleration with In-House RAG

Unknown

Business challenge: R&D teams were spending too much time managing a disorganized knowledge base consisting of over 500,000 documents. Project objectives: Speed up in silico hypothesis testing and compound analysis. Client requirement: Create an AI-driven research assistant that provides accurate, real-time responses to scientific inquiries using both internal and external data sources. Key performance indicator: Achieve a 50% reduction in analytical workload. Summary: We implemented a RAG assistant utilizing vector search across internal reports, PubChem, and scientific literature. The solution featured a fine-tuned large language model (LLM) specifically designed for pharmaceutical terminology. The web-based chat interface presents answers along with complete citations and explanations. Interface display: A chat window showcasing responses, citations, and links to internal documents. Main features: A RAG-based assistant that utilizes semantic search, provides citations, and conducts scientific Q&A through natural language processing. Administrative tools are included for managing data, models, and users, as well as for monitoring, feedback, and compliance. Technologies utilized: Python, LangChain, LangGraph, Weaviate, FastAPI, React, self-hosted LLM (Qwen 2.5 32b), LangFuse, and a Grafana stack (Prometheus, Loki, Tempo). Team composition: 1 Solution Architect, 2 ML Engineers, 1 Backend Engineer, and 1 QA. Results: Reduced hypothesis validation time from 4 hours to 1.5 hours, leading to increased confidence in outcomes. Client feedback: “The assistant comprehends our data more effectively than most new hires.”

AI-Driven Rental Property Management Platform

Unknown

The client is a mid-sized property management company based in Perth, overseeing more than 120 rental units, which include apartments and vacation rentals. They facilitate interactions among property owners, tenants/guests, and service staff. In a competitive rental market, they aimed to optimize operations and enhance returns for property owners. **Challenges** - **Inefficient occupancy and pricing**: Vacancies persisted longer than necessary due to a manual, experience-reliant pricing strategy. - **High manual workload**: Staff were burdened with repetitive communications with tenants and owners. - **Ineffective maintenance coordination**: Scheduling for cleaning and repairs faced delays, leading to unnecessary travel expenses. - **Limited scalability**: Current processes struggled to adapt to portfolio growth. - **Market competition pressure**: The firm sought to enhance service quality while managing operational costs. **Solution** - **Automated communications platform**: AI-powered messaging addressed tenant inquiries, maintenance requests, and notifications to owners. - **Dynamic pricing driven by AI**: Machine learning models adjusted rental prices in response to demand, seasonal trends, events, and competitor pricing. - **Intelligent task scheduling**: Optimization via machine learning improved the routing and assignment of cleaning and maintenance personnel. - **Owner analytics dashboard**: AI insights identified properties performing below expectations and opportunities for revenue enhancement. - **Mobile apps for tenants and staff**: These enabled payment processing, service requests, real-time task updates, and optimized daily routes. **Results** - **Increased occupancy and revenue**: Occupancy rose by 23%, accompanied by an 18% increase in average rental income per unit. - **Operational efficiency improvements**: Administrative tasks decreased by 65%, facilitating portfolio expansion without the need for additional hires. - **Reduced field operation costs**: Streamlined scheduling cut maintenance travel and idle time by 20%. - **Enhanced tenant satisfaction**: Quicker response times and proactive communication bolstered renewal rates and garnered positive reviews. - **Scalable growth**: The portfolio grew by 30% within one year with minimal additional costs.

Predictive Maintenance for Offshore Equipment

Unknown

An offshore oil and gas company operates platforms and underwater pipelines. The client aimed to minimize unexpected downtime of pumps and compressors, enhance production availability, and bolster safety in remote locations. **Challenges:** - **Critical Equipment Failures:** Export pumps and compressors experienced intermittent failures, halting offshore production. - **High Downtime Costs:** Each day of downtime led to significant losses in production revenue, amounting to hundreds of thousands of dollars. - **Logistics and Safety Issues:** Emergency repairs necessitated the use of helicopters, vessels, and involved high-risk operations offshore. - **Downtime Reduction Goals:** Management aimed for at least a 20% reduction in unplanned downtime. **Solution:** - **Predictive Analytics Platform:** Implemented real-time monitoring of the health of offshore equipment. - **Sensor Data Integration:** Collected pressure, flow, temperature, vibration, and electrical data from existing sensors. - **Normal Behavior Modeling:** Machine learning models were developed to understand the operational parameters of pumps and compressors under different conditions. - **Pre-Failure Pattern Detection:** Subtle shifts that typically occurred before failures were identified for early intervention. - **Actionable Alerts:** Risk notifications were embedded in control dashboards to prompt proactive inspections and maintenance. **Results / Business Value:** - **Reduced Unplanned Downtime:** Predictive alerts lessened unexpected stoppages in production. - **Increased Annual Output:** Higher uptime led to significantly increased recovery of oil and gas. - **Lower Emergency Logistics Costs:** A reduction in the number of urgent offshore interventions cut high support expenses. - **Improved Safety and Compliance:** Early interventions decreased the likelihood of high-risk failure incidents. - **Strong ROI:** The increase in revenue and savings allowed for a return on investment within the first year. **Key Metrics:** - Achieved a 20% decrease in unplanned offshore downtime. - Approximately 400,000 additional barrels produced annually due to enhanced uptime.

23 more projects