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AI Software
Development

If you’re looking for something more than just slapping “AI” label on your software product you’ve found the right place.

We were solving problems with AI Development Services for both international enterprises like BNP Paribas or Danone as well as for medium-sized companies and startups.
Thanks to our great experience in AI software projects we’ve even developed our own, unique approach that we’re evangelizing the industry with: Rapid AI Development Sprint = RAIDS.

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    We were solving problems with AI Development Services for both international enterprises like BNP Paribas or Danone as well as for medium-sized companies and startups.
    Thanks to our great experience in AI software projects we’ve even developed our own, unique approach that we’re evangelizing the industry with: Rapid AI Development Sprint = RAIDS.

    What is
    Rapid AI Development Sprint?
    Program

    The Rapid AI Development Sprint is an accelerated program designed to help organizations leverage AI technologies effectively and efficiently.

    Schedule

    The sprint unfolds over three weeks, starting with a discovery and analysis phase, followed by a design and prototyping phase, and culminating in the delivery of a proof of concept. The team is composed of an AI Consultant, Developers, and Quality Assurance professionals.

    Effects

    Overall, the Rapid AI Development Sprint is a focused, time-boxed approach to AI development that allows clients to quickly evaluate the potential impact of AI solutions on their business.

    Why iterative, rapid
    approach to AI is
    the right one?

    We come up with this approach as a response to the current AI wave that is making most of businesses eager to exploit new technology to create more value and be more competitive.

    The Makers - most entrepreneurial and innovative people in companies are already coming up with ideas to use GPT and large language models. Our AI Dev Sprint allows them to get to the results and proof of concepts fast and set the path for AI adoption in their companies.

    Benefits of fast AI software
    development Sprint

    A fast AI/GPT development sprint offers several benefits to the client, making it an attractive approach for organizations looking to leverage AI technologies in their business processes. Some key benefits include:

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    Rapid Results

    A time-boxed sprint allows clients to see tangible outcomes in a short period, enabling them to quickly evaluate the feasibility and potential impact of AI solutions on their business.

    Reduced Risk

    Fast development sprints allow clients to test AI-driven concepts with a minimal upfront investment, reducing the risk associated with investing in full-scale development projects.

    Agile Development

    The iterative nature of the sprint encourages continuous improvement and adaptation to changing requirements, ensuring the developed solution remains aligned with the client’s needs and expectations.

    Enhanced Collaboration

    A focused sprint fosters close collaboration between the client and the development team, enabling a better understanding of the client’s needs and expectations, which leads to a more tailored and effective AI solution.

    Cost Savings

    By rapidly developing and testing AI-driven solutions, clients can identify areas for process optimization and automation, leading to potential long-term cost savings and improved efficiency.

    Innovation

    A fast AI/GPT development sprint encourages experimentation and exploration of new ideas, fostering innovation and potentially uncovering novel applications of AI technologies in the client’s business

    Competitive Advantage

    Quick implementation of AI solutions can give clients a competitive edge in the market by enabling faster decision-making, improved customer experiences, and streamlined operations.

    Skill Building

    Engaging in an AI/GPT development sprint exposes clients to cutting-edge technologies and methodologies, fostering an understanding of AI and its potential applications, which can be leveraged in future projects.

    Proof of Value

    A successful sprint demonstrates the value of AI-driven solutions to stakeholders within the client’s organization, helping to secure buy-in and support for future AI initiatives.

    Schedule
    of the Rapid AI Development Sprint

    Week 1

    Sprint Discovery and Analysis

    • Kickoff meeting with the client
    • Understand the client’s business and requirements
    • Define the scope and goals of the sprint
    • Identify potential AI use cases and opportunities
    • Map out client’s business processes
    • Determine high-impact areas for AI intervention
    • Select the most suitable AI technologies (e.g., large language models, audio transcriptions, etc.)
    • Develop a project roadmap and timeline

    Week 2

    Sprint Design and Prototyping

    • Define tasks and milestones
    • Allocate resources and set expectations
    • Rapid prototyping and testing Design and develop AI-driven solution components
    • Iterate on client feedback and adjust the solution as needed
    • Weekly status update and review with the client
    • Share progress and insights
    • Adjust the project scope and roadmap if needed

    Week 3

    Sprint Proof of Concept and Demonstration

    • Finalize the development of the proof of concept
    • Integrate AI components and ensure functionality
    • Test and validate the solution against client requirements
    • Prepare and deliver a solution demonstration
    • Showcase the proof of concept in a live environment
    • Highlight the benefits and improvements to the client's business processes
    • Final review and feedback session with the client
    • Discuss the results of the sprint
    • Identify potential areas for further development or iteration

    Week 1

    Sprint Discovery and Analysis

    Week 2

    Sprint Design and Prototyping

    Week 3

    Sprint Proof of Concept and Demonstration

    1
    • Kickoff meeting with the client
    • Understand the client’s business and requirements
    • Define the scope and goals of the sprint
    • Identify potential AI use cases and opportunities
    • Map out client’s business processes
    • Determine high-impact areas for AI intervention
    • Select the most suitable AI technologies (e.g., large language models, audio transcriptions, etc.)
    • Develop a project roadmap and timeline
    2
    • Define tasks and milestones
    • Allocate resources and set expectations
    • Rapid prototyping and testing Design and develop AI-driven solution components
    • Iterate on client feedback and adjust the solution as needed
    • Weekly status update and review with the client
    • Share progress and insights
    • Adjust the project scope and roadmap if needed
    3
    • Finalize the development of the proof of concept
    • Integrate AI components and ensure functionality
    • Test and validate the solution against client requirements
    • Prepare and deliver a solution demonstration
    • Showcase the proof of concept in a live environment
    • Highlight the benefits and improvements to the client's business processes
    • Final review and feedback session with the client
    • Discuss the results of the sprint
    • Identify potential areas for further development or iteration
    Post-Sprint Activities

    • Outline key findings, insights, and deliverables

    • Include recommendations for future development

    • Schedule follow-up meetings and consultations to discuss the next steps

    • Discuss potential for additional sprints or full-scale implementation

    • Address any concerns or questions the client may have

    What are the most important results
    of iterative approach to AI software development

    The material results of an AI development sprint typically include several tangible deliverables and outcomes that demonstrate the value and potential of the AI-driven solution. The most important one is PoC itself.

    The proof of concept is a crucial material result of the AI development sprint. It serves as a tangible representation of the AI-driven solution’s core functionalities, allowing the client to experience and evaluate the solution firsthand. OUr proof of concept typically includes the following components:

    Core
    Probable
    1
    Core Features

    proof of concept focuses on implementing the most critical features of the AI-driven solution, which directly address the client’s business needs and objectives. These core features should demonstrate the solution’s potential to automate processes, optimize performance, or solve specific problems.

    2
    AI Integration

    The proof of concept integrates the developed AI models and algorithms, such as GPT and large language models, to showcase the effectiveness of AI in addressing the client’s requirements. This integration demonstrates the value of incorporating AI technologies in the client’s business processes.

    3
    User Experience

    Although the proof of concept may not have a polished user interface, it should still offer an intuitive and user-friendly experience to help the client understand how the solution works and how it can benefit their organization.

    4
    Functional Demonstration

    Proof of concept should be functional enough to provide the client with a clear understanding of the AI-driven solution’s capabilities. This demonstration enables the client to evaluate the solution in a realistic context and identify any potential gaps or areas for improvement.

    1
    AI Models and Algorithms

    Developed and fine-tuned AI models, such as GPT or other large language models, along with the associated code, data, and configurations.

    2
    User Interface Design

    Wireframes, mockups, and prototypes of the user interface, showcasing the usability and user experience of the solution.

    3
    Source Code and Repository

    The source code for the AI-driven solution, typically stored in a version control system (e.g., Git) for easy collaboration and future development.

    4
    Deployment and Integration Guidelines

    Instructions and recommendations on how to deploy and integrate the AI-driven solution within the client’s existing systems and infrastructure.

    5
    Sprint Retrospective

    A summary of the sprint, including any challenges encountered, areas of improvement, and potential next steps for further development or full-scale implementation of the AI-driven solution.

    6
    Client Feedback and Recommendations

    Client feedback on the developed solution, including any suggestions for refinement or potential future enhancements.

    7
    Post-Sprint Roadmap

    A proposed roadmap outlining the next steps for the project, such as additional sprints, full-scale development, or integration with other systems.

    Questions?
    Save time & Contact us

    Team composition
    for Artificial Intelligence development projects

    In this streamlined team composition, the roles and responsibilities are as follows:

    AI Product Manager

    Gathers requirements and provides domain-specific knowledge Translates business needs into technical requirements Identifies suitable AI technologies and potential use cases Acts as a liaison between the client and the development team Ensures alignment of the AI-driven solution with the client’s goals and objectives. Provides guidance on best practices and technical feasibility.

    Developers

    Implements both front-end and back-end components of the AI-driven solution Develops and fine-tunes AI models, such as GPT or other large language modelsConducts data preprocessing, feature engineering, and model validation. Data vectorization for large language models. Ensures seamless integration of AI models and APIs. Collaborates with the AI Preoduct Manager to create an intuitive user interface.Adapts and iterates on the solution based on feedback from the client and the AI Business Consultant.

    Quality Assurance

    Develops test plans and test cases for the AI-driven solution. Conducts functional, integration, and performance testing. Works with the Developer to identify and resolve issues. Ensures the usability, reliability, and performance of the solution. Validates that the solution meets the client’s requirements and expectations.

    Case study from our CTO - Mario Tarnaski

    Chat GPT   implementation

    Context

    We have several TB of data. These are files of various types, some of them are actually text materials (in various forms, most often PDF, DOC, PPT, etc.), but by volume the largest part of the data is various types of programs, ISO images and various other files, including those specialized for the client’s industry. What all these files have in common is that there is no way to generate any material about them, and the only information we have about them is their name, with a full path in the directory tree.

    case study icon
    Client's challenge

    The client is having trouble finding information in this thicket of data. He would like to be able to search both the content and look for specific files by their names (without worrying about typos, punctuation, etc.). For now, the solutions used by the client do not meet their expectations.

    PoC implementation:

    As part of our cooperation, we have done two separate projects:

    AI-based content search engine
    Content search engine based on ElasticSearch (a popular search engine mechanism)
    The first stage

    of the project was to convert a sample of data (~45GB) into text form. Where we could, we extracted the text data, otherwise we saved the basic information about the file.

    In addition, for a selected group of several dozen image files, the client provided descriptions, which we also included.

    The second stage

    generating vectors for AI and connecting the language model.

    The third stage

    indexing the data in ElasticSearch.

    The result of our work
    Two web interfaces for content search:

    The first, based on AI, provided three search methods: by file names, in content and, finally, a substantive answer to the question asked was generated by the language model.

    The second interface provided results for the same queries, based on ElasticSearch.

    Tests showed

    that in the simplest queries, the content searches by AI and ElasticSearch were very similar to each other.

    However, the more complex the query we asked, the better the results AI achieved and the worse ElasticSearch did. The advantage of AI is that it „understands” the context and can find and process content that does not always clearly answer our question. In addition, we achieved fantastic search results by file names, resistant to various errors, typos, etc.

    Finally, the language model was able to respond substantively to the questions we asked where ElasticSearch does not provide such functionality and gets lost in such tasks.

    Cost estimate
    of Rapid AI Development Sprint

    Role
    Hourly Rate
    Total Hours
    Total Cost
    AI Consultant
    $100
    80
    $ 8 000
    Developers
    $70
    120
    $ 8 400
    Quality Assurance
    $35
    80
    $ 2 800
    Total (3-week sprint)
    280
    $ 19 200

    AI Development Sprints for Digital First AI

    Background

    Digital First AI is a Polish startup that operates in the MarTech (marketing technology) sector. Their primary goal is to empower entrepreneurs with the knowledge, tools, and tactics needed to create effective marketing campaigns. They reached out to us with a challenge: to build an AI-powered platform that simplifies the process of creating marketing strategies and sales funnels for entrepreneurs, especially those who lack comprehensive knowledge in the field of marketing.
    case study icon

    Challenge

    Our main challenge was to develop an AI system capable of recommending the most suitable marketing tactics for the clients of Digital First’s platform. These clients, often without a full understanding of marketing strategy, provided minimal input. The task was to ensure that even with this limited data, the system could generate effective marketing tactics and provide guidance on their execution.

    The Sprint Process

    Sprint 1

    Product discovery and ideation

    Duration: 4 days
    We began our first sprint by deep diving into the problem. We liaised closely with the Digital First team, understanding the landscape of their clients’ knowledge and requirements. We also did a comprehensive study of the MarTech landscape, identifying the gaps and opportunities that our AI system could fill. Then we brainstormed and evaluated a series of innovative solutions. We settled on an AI system that could learn from minimal user input and recommend effective marketing strategies

    Sprint 2

    Designing the AI system

    Duration: 12 days
    The third sprint was about designing the AI system. We decided on a hybrid AI model that combines different machine learning techniques, such as Natural Language Processing for understanding user input, and a recommendation system for suggesting marketing tactics. We managed to develop a prototype of the AI system that was able to take minimal user input and recommend a basic set of marketing strategies.

    Sprint 3

    Testing and feedback

    Duration: 6 days
    The fifth sprint was dedicated to testing and gathering feedback. We let a group of Digital First’s clients use the prototype and give us feedback. We learned a lot from this, especially about how to make the system more intuitive and user-friendly. Than we iterated and improved the system based on the feedback received. We made the AI system more intuitive and added features that would make the recommendations more actionable.

    Sprint 1

    Product discovery and ideation

    Sprint 2

    Designing the AI system

    Sprint 3

    Testing and feedback

    1
    Duration: 4 days
    We began our first sprint by deep diving into the problem. We liaised closely with the Digital First team, understanding the landscape of their clients’ knowledge and requirements. We also did a comprehensive study of the MarTech landscape, identifying the gaps and opportunities that our AI system could fill. Then we brainstormed and evaluated a series of innovative solutions. We settled on an AI system that could learn from minimal user input and recommend effective marketing strategies
    2
    Duration: 12 days
    The third sprint was about designing the AI system. We decided on a hybrid AI model that combines different machine learning techniques, such as Natural Language Processing for understanding user input, and a recommendation system for suggesting marketing tactics. We managed to develop a prototype of the AI system that was able to take minimal user input and recommend a basic set of marketing strategies.
    3
    Duration: 6 days
    The fifth sprint was dedicated to testing and gathering feedback. We let a group of Digital First’s clients use the prototype and give us feedback. We learned a lot from this, especially about how to make the system more intuitive and user-friendly. Than we iterated and improved the system based on the feedback received. We made the AI system more intuitive and added features that would make the recommendations more actionable.

    Results

    The newly created AI platform was a success. It could take even minimal user input and generate effective, actionable marketing tactics. It also guided users on how to execute these tactics. This not only simplified the process for entrepreneurs but also made their marketing efforts more efficient and effective. Our AI development sprints led to the creation of a platform that filled a significant gap in the MarTech landscape. It was a win-win situation for Digital First and their clients, and a testament to the power of AI in transforming industries.
    Inspiration

    Examples of the jobs that can be fulfilled by your next AI-supported solution. Focus on Large Language Models

    01

    Sentiment anaysis

    LLM’s sentiment analysis capability identifies the tone of texts, helping to gauge customer sentiment in real-time.

    02

    Data indexing

    Our advanced indexing of various data types, including PDFs, DOCs, and system files, enhances search efficiency and context understanding. Unlike standard search engines, our LLM retrieves results and cites sources, providing accurate, context-rich answers.

    03

    Elevating bot's capabilities

    We offer the construction of memory-enabled bots, elevating your bot's learning and interaction capabilities.

    04

    Integrations

    Our LLM allows for diverse interface development, ensuring seamless communication with your existing IT systems. It can be integrated into any system, respond to specific actions, perform regular background data analysis, and deliver results via multiple channels, including email and Slack.

    05

    Detailed tasks

    Our AI solutions go beyond Q&A. Our bot actionably responds to commands or discussions, interacting with various systems and services. For instance, it can provide real-time order statuses or process detailed tasks sent via files.

    06

    Efficient data extraction

    Our bot can also receive and instantly analyze files, extracting useful data for further actions. This allows for efficient handling of documents such as invoices.

    07

    Complex workflow facilitation

    The bot’s capability to chain actions enables continuous data processing and multi-step task completion, facilitating complex workflows.

    08

    Accessibility Improvement

    Our LLM’s audio/video transcription and text-to-speech processing capabilities add another layer of accessibility and convenience to your operations.

    01

    Sentiment anaysis

    LLM’s sentiment analysis capability identifies the tone of texts, helping to gauge customer sentiment in real-time.

    02

    Data indexing

    Our advanced indexing of various data types, including PDFs, DOCs, and system files, enhances search efficiency and context understanding. Unlike standard search engines, our LLM retrieves results and cites sources, providing accurate, context-rich answers.

    03

    Elevating bot's capabilities

    We offer the construction of memory-enabled bots, elevating your bot's learning and interaction capabilities.

    04

    Integrations

    Our LLM allows for diverse interface development, ensuring seamless communication with your existing IT systems. It can be integrated into any system, respond to specific actions, perform regular background data analysis, and deliver results via multiple channels, including email and Slack.

    05

    Detailed tasks

    Our AI solutions go beyond Q&A. Our bot actionably responds to commands or discussions, interacting with various systems and services. For instance, it can provide real-time order statuses or process detailed tasks sent via files.

    06

    Efficient data extraction

    Our bot can also receive and instantly analyze files, extracting useful data for further actions. This allows for efficient handling of documents such as invoices.

    07

    Complex workflow facilitation

    The bot’s capability to chain actions enables continuous data processing and multi-step task completion, facilitating complex workflows.

    08

    Accessibility Improvement

    Our LLM’s audio/video transcription and text-to-speech processing capabilities add another layer of accessibility and convenience to your operations.
    Try it yourself: chat.develtio.com

    Develtio’s GPT-based chatbot

    Try it yourself:
    chat.develtio.com
    check.it

    Capabilities

    Is trained on Develtio's knowledge base, employees' handbook, and documentation Allows users to provide follow-up corrections Mostly correct but sometimes makes funny mistakes ;)

    Examplres of questions You can ask

    • Tell me about your product discovery process
    • What will be the team's composition for the crowfunding platform development project?
    • What is your tech stack?

    Develtio’s GPT-based chatbot

    Capabilities

    Is trained on Develtio's knowledge base, employees' handbook, and documentation Allows users to provide follow-up corrections Mostly correct but sometimes makes funny mistakes ;)

    Examplres of questions You can ask

    • Tell me about your product discovery process
    • What will be the team's composition for the crowfunding platform development project?
    • What is your tech stack?
    Try it yourself:
    chat.develtio.com
    check.it

    Artificial intelligence solutions across industries

    There are numerous industries and types of companies that can benefit from an AI development sprint focused on integrating LLMs like ChatGPT into their existing systems. Here are some potential use cases and industries that could be targeted in your outbound campaign
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    Real Estate

    list item icon Automate property listing data input and categorization
    list item icon Enhance customer support with chatbots for property inquiries
    list item icon Provide personalized property recommendations based on user preferences

    Healthcare

    list item icon Automate patient data input and categorization
    list item icon Assist with medical coding and billing
    list item icon Provide symptom analysis and recommendations based on patient input

    E-commerce & Retail

    list item icon Automate product categorization and tagging
    list item icon Enhance customer support with chatbots for common queries
    list item icon Personalize customer experience by analyzing user behavior and preferences

    Finance & Banking

    list item icon Automate transaction categorization for expense tracking
    list item icon Improve fraud detection with pattern recognition
    list item icon Enhance customer support with chatbots for common queries

    Legal

    list item icon Automate document review and classification
    list item icon Extract relevant information from legal documents
    list item icon Automate the creation of basic legal documents

    Human Resources

    list item icon Automate resume screening and candidate matching
    list item icon Assist with onboarding by providing personalized resources and guidance
    list item icon Enhance employee support with chatbots for policy and procedural queries

    Customer Support

    list item icon Automate the classification of customer support tickets
    list item icon Improve response times with AI-generated suggestions or draft replies
    list item icon Enhance support with chatbots for common queries

    Education

    list item icon Automate content summarization and note-taking
    list item icon Provide personalized learning recommendations based on student input
    list item icon Enhance tutor support with chatbots for common questions and guidance

    Manufacturing & Supply Chain

    list item icon Automate inventory management and classification
    list item icon Analyze and predict equipment maintenance requirements
    list item icon Enhance communication and collaboration with AI-driven language translation

    Marketing & Advertising

    list item icon Automate content generation for social media, blog posts, and ad campaigns
    list item icon Enhance audience segmentation and targeting through data analysis
    list item icon Improve sentiment analysis and campaign performance tracking

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      Mariusz Tarnaski
      CTO, Develtio
      info@develtio.com

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