AI development is much like research activities.

It all begins with the business idea. You create a hypothetic solution and design a plan to reach the goal. Next, you experiment, make mistakes and collect observations following the roadmap. Iteratively, you improve the approach and come up with the solution.

Typically, we go through the following phases:

OPTIMIZATION DEPLOYMENT AND INTEGRATION DATA LABELING DATA PREPARATION MLOPS DEVELOPMENT DISCOVERY PREVIEW DISCOVERY PREVIEW MLOPS DEVELOPMENT

Preview

We start with a free-of-charge preview talk. Knowing your needs and pains, we elicit the value AI can produce for your business and make rough time and budget estimates.

Results (on demand)

  1. Communication setup
    To ensure everyone has a comprehensive understanding of the business objectives, assumptions, and limitations.
  2. List of AI solutions
    That has the best chance to bring you quick wins and move your services/products to an entirely new level.
  3. Rough estimates
    Of the project timeline and price

Time: 1-2 hours

Discovery

The discovery phase is a thorough investigation of the business goals aligned with available data analysis and evaluation of existing open-source solutions.

Along with a number of AI approaches we evaluate the technical feasibility of the idea, create an implementation roadmap, detailed time and costs estimates, team composition, and technology stack.

Usually, the discovery phase ends when the development phase begins.

But we may find that you do not have enough data to start or it is not labeled correctly, or the business idea is not feasible. We help you collect (Data Preparation) and label the data (Data Labeling) correctly in these cases, or we encourage you to reformulate the goal.

Results (on demand)

  1. Discovery Report
    Present it in front of stakeholders and convince them that AI is worth a shot. <br>
  2. Analysis of open-source repositories
    And conclusions on their reusability/applicability
  3. Competitor analysis
    At what technological level your rivals are? Is it worth entering a race with them?
  4. Detailed implementation roadmap
    With expected deliverables aligned to the project timeline to know what steps to take and what outcomes you get at each stage.
  5. Team size & composition
    Get to know your future team and their skillset
  6. Time & budget estimates
    Get ready for the journey and evaluate ROI
  7. Feasibility study
    Professional conclusion on AI applicability and benefits for your business

Time: from 2 weeks

Development

Suppose, in the discovery phase, we conclude that the data is sufficient, properly labeled, and the business goal is feasible.

In that case, we roll out the full-scale AI product development. We move according to the designed earlier roadmap. The process is managed as Scrum or Kanban with weekly demo sessions so that you track the progress.

Deliverables

  1. Standalone operable AI module
  2. Source code and documentation
  3. Trained ML model metrics report
  4. Knowledge transfer

Time: from 1 month

MLOps

Automation of machine learning operations, integration, and deployment workflows.

MLOps eliminates manual toil and mistakes working with multiple versions of data preprocessing and feature engineering pipelines, datasets, respective train/validation/inference source code, and models by automation and high-level orchestration.

Time: from 1 month