Image credit: Stevens industries

AI/ML Software Development

Trusted by Companies

Internet Archive
Tempest
Xperi
Foba
DTS
Stevens

what we do

Autonomous AI Agents

AI that analyzes, decides, and acts—automating complex tasks with minimal oversight.

AI-Powered Automation

Smart workflows that optimize operations and reduce manual effort.

Computer Vision & Video Understanding

AI-driven scene analysis, object recognition, and anomaly detection.

Generative AI

Create images, videos, and text at scale with AI-powered content generation.

Legacy Code Modernization

Analyze, refactor, and upgrade outdated codebases for modern performance.

Stages of development

  • A Machine Learning Team Lead and Business Analyst guide this phase, collaborating with the client to gain a deep understanding of the business. The key deliverables include:

    • Researching existing ML models, datasets, and articles addressing similar problems
    • Selecting the appropriate ML model
    • Defining the SRS (requirements)
    • Providing a budget estimate for the PoC

    The output is a clear roadmap that aligns both technical and business goals.

  • A Machine Learning Team Lead, with support from COXIT’s HR and recruiting team, leads this phase. Key steps include:

    • Building a specialized team of 2-3 members with expertise tailored to the project’s needs
    • Developing a detailed data annotation and collection plan, including an annotation guide
    • Implementing GitOps best practices and establishing a robust SDLC to ensure high-quality, scalable code delivery
  • A Machine Learning Team Lead guides this phase to ensure alignment with client goals. The main activities include:

    • Data collection and automation of a data set formation
    • Training the model on a minimal dataset to validate the approach and prove business value
    • Automating model training and validation processes for continuous improvement
    • Maintaining clear communication through daily Slack updates, ZenHub boards, and weekly meetings to ensure transparency and progress tracking
  • A Machine Learning Team Lead directs this phase, focusing on:

    • Automating data collection, model training, and performance validation processes
    • Setting up the user interface, along with testing and production environments
    • Implementing feedback loops and monitoring systems to ensure continuous improvement and scalability

Industry Specific AI Use Cases

    • [1]
      Analyze and compare bid 
proposals, reducing research time.
    • [2]
      Estimate projects faster by automatically extracting costs, materials, and timelines from plans.
    • [3]
      Monitor project progress with AI-driven video analysis and anomaly detection.
    • [4]
      Automate compliance checks by scanning documents for regulatory requirements.
    • [1]
      Instantly retrieve product specifications and technical documents.
    • [2]
      Track materials and inventory levels with real-time AI insights.
    • [3]
      Detect anomalies in machinery performance and predict maintenance needs.
    • [4]
      Automate quality control by analyzing production images and detecting defects.
    • [1]
      Provide instant access to patient records while ensuring data security.
    • [2]
      Automate appointment scheduling based on real-time availability.
    • [3]
      Generate intelligent reports from medical imaging, lab results, and patient histories.
    • [4]
      Reduce administrative workload by streamlining billing and insurance claims.
    • [1]
      Extract key data from financial reports, contracts, and legal documents.
    • [2]
      Detect fraud patterns using advanced anomaly detection.
    • [3]
      Automate risk assessments and compliance audits.
    • [4]
      Provide personalized investment insights and customer support through AI-powered chat.
    • [1]
      Analyze old codebases and automatically generate documentation.
    • [2]
      Language to language code translation
    • [3]
      Convert monolithic applications into modular, scalable architectures.
    • [4]
      Identify and eliminate redundant or outdated code

Case Study

Machine Learning-Powered Estimation Software

Stevens Industries integrated an AI agent to streamline document search and analysis for large PDFs.

Read full case
Machine Learning-Powered Estimation Software
The system enables users to quickly find product specifications, material details, and other critical information while reducing manual effort and minimizing errors.

our expertise

We specialize in Team Extension and ML/AI Development

Photo of Iryna Mykytyn

Iryna Mykytyn

Founder / Senior Software Engineer

LinkedIn

Photo of Taras Rumezhak

Volodymyr Hresko

CO-Founder / Full Stack software engineer

LinkedIn

Frequently Asked Questions

  • Agentic AI refers to AI systems that go beyond automation—they don’t just follow predefined rules but actively analyze data, make decisions, and take action based on real-world feedback. Unlike traditional automation, these AI agents continuously learn, adapt, and optimize their actions over time, making them more capable of handling complex workflows and dynamic tasks.

  • Traditional AI is designed for specific tasks with limited adaptability (e.g., answering questions, classifying data, or generating content). Agentic AI acts autonomously, reasoning, planning, and executing tasks without constant human input. It can make decisions based on evolving circumstances, allowing businesses to automate entire operational processes rather than just isolated tasks.

    • [1]

      Identify key business challenges

      Where do inefficiencies exist? What tasks require automation?
    • [2]

      Schedule a consultation

      Our AI experts will assess your needs and determine the best approach.
    • [3]

      Develop a proof of concept (PoC)

      We build and test an initial AI agent to ensure it aligns with your goals.
    • [4]

      Scale and optimize

      Once the AI is deployed, it continuously improves through real-world feedback.
    • [1]

      AI Chatbot

      A basic, rule-based system designed to answer questions and provide predefined responses (e.g., customer support bots).
    • [2]

      AI Assistant

      A more advanced system that can process natural language and assist with specific tasks (e.g., voice assistants like Siri or Alexa).
    • [3]

      AI Agent

      A fully autonomous system capable of understanding complex workflows, making decisions, and executing actions without requiring human oversight.

Let's collaborate

Tell us a bit about your project or challenge, and we'll get back to you shortly.

Contact our co-founder

[email protected]
This field is for validation purposes and should be left unchanged.
Full name
By submitting the form, you agree to Coxit’s Privacy Policy.