Our Offerings

AI- Artificial Intelligence

Developing software with AI capabilities entails creating new software or adapting existing software to output AI analytics findings to users (e.g., demand forecasting) and to trigger particular actions depending on them (e.g., blocking fraudulent transactions).

With the assistance of AI, a business application may automate operations, customize service delivery, and provide company-specific insights. 90% of seasoned AI users believe that “AI is vital to their business’s success today.

Case Studies for AI-Enabled Software

Business process automation

Engine for optical character recognition used to extract data from paper documents.

Production management

Analysis of the fundamental causes of production losses and forecast of process quality.

Customer analytics

Customer sentiment analysis and behavior prediction.

Risk management

Analyses of counterparty risk and possible harm prediction.

Supply chain management

Forecasting lead times and inventory optimization

Personalized service delivery

Segmentation of customers and recommendation engines.

Strategy For Developing AI-Enabled Software

  • Feasibility study

    Duration: 1 month
    Developing a proof of concept (PoC) for AI to determine the technical and economic viability of incorporating it into software, as well as the scope of work, timeframe, budget, and hazards. Outlining software needs AI at a high level (in the case of new software).

  • Conducting a business study to ascertain AI needs

    Duration: 1-6 weeks
    Defining comprehensive functional and non-functional criteria for AI, such as the needed degree of AI accuracy. In certain circumstances, the value may be generated with as little as 65-80% accuracy, explainability, fairness, and privacy, as well as the required reaction time.

  • Designing a solution architecture

    Duration: ary depending on the software's overall complexity
    Choosing patterns and methods for integration. Architecting the solution's architecture, including points of interaction between its components, including integration with an AI module.

  • Preparation of business procedures

    Duration: 1-3 months
    Launching a program to integrate AI into mission-critical software may need organizational adjustments to ensure the initiative's success and uptake.

  • Software Advancement

    Duration: 3-36 months
    Developing software's front and back end (the server-side and APIs, including necessary APIs for AI module integration). Conducting quality assurance methods throughout the software development process to ensure the program's quality.

  • Development of AI modules

    Duration: 1-2 weeks
    Consolidating data from various relevant sources (internal and external, which can be acquired via one-time purchase or a subscription). The generated data is divided into three sets: training, validation, and test.

  • ML model training

    Duration: 1-4 weeks
    Choosing appropriate machine learning algorithms and developing machine learning models. After training the models on training data and validating them against a validation dataset, fine-tuning hyperparameters enhance their performance.

Cloud Services to Boost AI Software Development

Amazon SageMaker

Azure Machine Learning Services

Ascertain the Feasibility of Your AI Development Initiative

Blocknize may undertake a feasibility study on your behalf to ascertain the unique combination of elements that will determine the path for incorporating AI capabilities into software in your specific scenario.