Next Steps to AI Transformation

We help enterprise companies develop and implement Artificial Intelligence strategies that achieve results.  Our proven technology-enabled process includes use case definition, feasibility study, prototypes, and end-user-testing. We work with your team as colleagues and strategic consultants to lead project initiatives and provide AI adoption in your organization.


Useful AI initaitives start with a clear vision of the goals, opportunities, and the ideal use case. Through collaboration, research and workshops, we are able to identify oue client’s goals, users, and requirements.

(2-6 weeks)


Next, we ensure the feasibility of the defined use case. We identify data sources and quality, develop simulations and develop a comprehensive understanding of the project components.  This results in a product roadmap and development plan.

(2-6  weeks)


Through agile sprints, we develop high-quality models and networks that display a running version of the AI. Our cross-functional team becomes part of our client’s organization – guiding a use case vision to an ultimate solution.

(1-2 weeks per sprint)


After a successful model prototype, our team provides the necessary resources to help our clients complete a fully functioning AI solution and support or lead any necessary engineering solutions.

(4-8 weeks)

Launch & Post Launch

We finalize documentation and perform rigorous testing before deploying to production. A specialist support team monitors model performance and system integrity to provide ongoing maintenance and operational support.

(Ongoing from Launch)

Transition & Enablement

We perform capability training and transition planning, hands over technical assets and documentation, and develops maintenance and support plans.

(4-16 weeks)

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AI Services


  • Data Preparation – We give your data the works and do cleanup, labeling, augmentation, embedding, and pre-filtering to create a feasibility roadmap.

  • Data Cleanup
    We detect the data that is not properly formatted, has results out of possible range, etc

    Data Set Labeling
    For supervised learning, models need labeled data in order to be trained

    Data Augmentation
    We create additional synthetic data from the existing data set, for the network to train on

    Data Embeddings
    We convert non-numerical data to numerical appropriately, for the network to train on such (i.e. image annotations)

    Data Pre-filtering
    We pre-process your images for the network to train on via our growing filter library

  • Feature Selection & Application: After data prep, we assess what can be derived from your data and see how we can optimize it.

  • Feature Extraction
    Unsupervised analysis of images (and metadata, if applicable) to extract/ learn useful features; standard and custom methods available

    Feature Engineering
    Human-in-the-loop generation of useful features from images and metadata

    Data Visualization
    Visualizing the training data in terms of its features

    Image Data Manipulation
    Alignment, stitching, warping/de-warping, etc. some use-cases are general (e.g. alignment), some very specific (e.g. highlight veins in a leaf)

    Dimensionality Reduction
    Reducing the list of features to only include the useful ones for the network to train on; standard and Dynam.AI proprietary algorithms.

  • AI Model Development & Training: This is where the fun begins, and we add our secret sauce to prepare for the Physics guided AI magic.

  • AI Implementation Strategy
    Creating a pipeline or pipeline for data manipulation and solution training provides an end-to-end solution for our clients

    Custom multistage ML/AI pipelines
    We can address problems that require complex, multi-stage data processing and inference pipelines.

    AI Model Development
    Combining unsupervised, semisupervised, supervised ML models, data augmentation approaches, custom objectives, etc. to provide best solution for the client

    AI Model Training
    Using standard and custom techniques to optimize model performance

    AI Model Wrapping
    Implementing services around AI models so that they can be accessed by the rest of the software solution

  • Model Deployment & Solution Integration: We add a second batch of secret sauce to custom-tailor your application. This includes APIs, front-end interfaces, and packaging.

  • AI Model Deployment and Serving
    Operational integration of the models into the delivered solution; solution QA.

    Services Development
    Writing services and APIs around the AI we develop

    Interface Development
    Create front-end interfaces to the services we write

    Service Integration
    Work associated with versioning, packaging, and tying together everything

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