Finding and
Funding AI

Intersog can help you find the value prop for AI.


Key Factors for AI Initiatives

Data Readiness

Data readiness is one of the prime aspects of an AI initiative’s success. If you have a pile of expected inputs and outputs ready to train your ML engine, you have an excellent starting point. Honing in on one function, one problem, one set of data is the easiest way to get started. See what Forbes has selected as the top AI trends for 2023.

Organizational Readiness

AI requires change management and process realignment. The ‘business as usual’ mentality will require some disruption. We recommend ensuring the business, technology and human capital strategies all align with any AI initiatives. Moreover, Intersog can help you define the best place to begin.

Identify the Right Problems

Don’t think 'end to end' but identify small areas of immediate impact. What is common, often repeated, easy to discern, has good data available and makes bottom line or top line improvements. Also, considering the advancements in ChatGPT, a sentient service bot can add tremendous value.

Right Folks on the Bus

Generally, the team needs to include the business, tech and operational SME’s, BI, data science, strategy, transformation, and innovation team members. Also, along with AI strategy, impact advisory, change management and program execution services expertise. Thus, outside services are often selected to accelerate the process.

The RIGHT Project


benefits, helps
organization and users,
readily adoptable


good data to start, risks assessed,
clear ripple effect

Intersog’s AI Discovery Workshop helps identify the right place to get started - or continue. The discovery process drills into the organization. Identifies the available data, readiness indicators and several other factors which are documented. Additionally, we help to align the initiative with specific business goals. We want to create ​​well-defined organizational and user benefits, with projections for revenue increases and/or cost effectiveness. Intersog is recognized as a top provider in AI solutions. So, read on to find examples of our AI solution services along with their impact and applicability.
AI Defined
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AI Solution Services

We can help if you are set with your direction and need particular skills or if you need AI strategy, discovery and/or execution services.

AI Solution Services

We can help if you are set with your direction and need particular skills or if you need AI strategy, discovery and/or execution services.

AI Solution Services

We can help if you are set with your direction and need particular skills or if you need AI strategy, discovery and/or execution services.

    Enter needed skills

    How many consultants?

    When needed?

    Or add a date:

    How long?

    Team Ready!


      Are you interested in our AI Discovery Workshop?

      Do you have AI goals defined?

      Have you identified the data needed for your ML engine?

      Do you need help preparing the data?

      Do you have a team dedicated to this effort?

      When do you want to start the project?

      All Ready!


        All Ready!


        AI Potential




        Computer Vision

        Intelligent Document Processing (IDP) AI solution services use a machine learning engine to automate the workflow and/or analysis of large volumes of documents and other data sets. IDP facilitates robotics process automation, task automation, marketing automation, fraud detection, predictive analytics and so much more. Think of the volume and pervasiveness of document and systems data that is available. AI combines almost any data set to create business insights and execute repetitive tasks. Quality, learning and work-life balance are all improved.

        IDP Drivers

        • Compliance and regulatory reporting
        • Document classification and advanced search
        • Claims, invoice, forms processing
        • Contracts management
        • Employee and customer onboarding
        • Data extraction and workflow Automation

        ChatGPT and similar alternatives are the drivers behind chatbots, conversational AI, sentiment analysis, buyer intent, empathetic responses, language translation and data set training. ChatGPT has great potential when combined with IDP and other neural network initiatives. Consider ChatGPT as another foundational building block for AI. Undeniably, AI considerations can be instrumental in helping to define strategic initiatives.

        ChatGPT Drivers

        • Conversational query
        • Workflow automation
        • Language translation
        • Education and training
        • Market research
        • Intent recognition
        • Content generation

        NLP is an AI and ML solution that understands, processes and generates human language responses. Large Language Models (LLM) have become the predominant NLP function. ChatGPT being the most popular. With NLP, computers can read text or hear speech, interpret it, measure the sentiment and determine actionable insights based on the content and the context. Indeed, NLP is the basis of chatbots, voice assistants, language translators and advanced data classification.

        NLP Drivers

        • Document insights
        • Data definition from unstructured sources
        • Named Entity Recognition (NER)
        • Forms parsing and workflow automation
        • Key‐value pairs extraction
        • Chatbot, voice assistant etc.

        Computer Vision (CV) is object recognition based AI. It trains machines to perform human vision functions. With the capability of high end video and image technology, CV quickly surpasses the human eye in regards to capturing quality control issues on a high volume output production line. CV is the base of face recognition, self-driving cars and AI healthcare diagnostics. Basically, CV is here to stay and will continue to evolve to help operational and service output.

        CV Drivers

        • Anomaly detection
        • Medical imaging diagnosis
        • Tracking and classification tasks
        • Object and facial recognition
        • Autonomous vehicles
        • Surveillance and security

        Top AI Use Cases


        Process Automation

        • workflow
        • onboarding
        • training IDP for forms
        • invoices
        • contracts
        • media data
        • diagnostic imaging
        • compliance reports

        AI Assistant

        • personal assistant
        • chatbot
        • customer service
        • task automation
        • content creation
        • search automation
        • scheduling

        Personalized Marketing

        • customized responses
        • lead generation
        • email
        • text
        • drip campaigns
        • online prompts
        • ID buyer intent

        Trends, Detection and Forecasting

        • data insights
        • anomaly detection
        • security
        • decision support

        Feed the Machine

        The Inputs and Outputs of AI

        HOT ITEMS

        Input Examples

        Data Examples:
        structured data
        json files
        sensor data
        unstructured data
        editable controls for behavior and
        performance of the AI model

        HOT ITEMS

        Output Examples

        Business Insights:
        forecasting, predictions, decisions
        label, categorize, classify
        Connected Content:
        personalized, helpful, knowledgeable,
        translation, colloquial
        automated operations, alerts, notifications,
        transactions, campaigns, diagnosis

        AI Breakdown

        inputs and outputs

        AI is based on expected inputs and outputs. Additionally, machine learning can take these expectations and predict outcomes. Automated actions can be taken and insights can be derived from setting up AI models focused on particular. Also, the healthier the onset data inputs and outputs are, the healthier the AI system will grow. This is a critical aspect to any AI initiatives. Basically, data readiness will be one of the early aspects of the transformation.

        machine learning

        Machine Learning (ML) an AI system constantly collecting data and learning. This data could be documents, forms, machines, websites, system interactions, etc. The ML engine will learn from this data without being explicitly instructed to do so. Thus, ML is used for insights and actions. It is prepared with adjustable parameters and is inclusive of several other AI functions including supervised and unsupervised learning.

        data science

        Data Science aims to extract meaningful insights from disparate data sources. As an AI augmentation discipline, data science combines data visualization, complex algorithms, statistical analysis, predictive modeling, trends forecasting, etc.; working with structured and unstructured data of almost any source; scada systems, databases, spreadsheets, text documents, images, videos, sensor data, etc. Also, Intersog specializes in custom AI Solution Services.

        neural network

        The basics of the neural network including the following steps: Define the problem, gather and prepare the data, design network architecture which includes deciding the number and types of layers, the number of neurons in each layer, the connections between layers, the activation functions, initialization methods, forward and back-propagation, defined loss function, editable parameters and the training process itself. Therefore, upon launch and evaluation, adjustments to the parameters can be made.

        ai infra

        The AI Infrastructure requires several key components for success. These include high-performance servers (GPUs & TPUs), robust and scalable storage, a secure, fast and highly available network infrastructure, a data management strategy with strong execution, AI frameworks and tools, a model training and inference platform, and IDE for devs. In addition, secured and private AI environment, UI for AI management and an AI executive dashboard are typically preferred.

        Lessons Learned

        Take time to understand the problem, goals and solution

        Identify every day time sucks

        Get the data in order (volume of historical inputs and outputs are ideal)

        Develop short and long term strategies and plans

        Get people onboard early, change management is required

        Set small goals and realistic expectations

        Leverage existing tools, platforms and outside expertise

        Stay fluid; learn, adjust and innovate

        Our Clients Say

        “Communication with Intersog was great. Since I had some specific
        requirements, the offshore team was able to fully meet them.
        Also, we have now thousands of customers using our upgraded system."
        Eitan Weisbeker