GenAI integration

We provide GenAI integration solutions, offering specialized or general-purpose AI models to automate tasks, synthesize data insights, and deliver personalized customer experiences.

    • Generative AI – a type of AI that can create new data, like text, code, images, or music. It does this by learning patterns from existing data and then using those patterns to generate new, but similar, content.

    • LLM (Large Language Model) – a type of generative AI that is trained on a massive amount of text data. This allows LLMs to communicate and generate human-like text in response to various prompts and questions. Examples: Gemini, ChatGPT, etc.

    • Chatbot – a computer program/interface that simulates conversation with human users. Chatbots are often used for customer service applications or to provide information on a website.

    • API – a set of instructions and standards that allows applications to talk to each other. Imagine it like a waiter taking your order in a restaurant - the waiter doesn't cook the food, but they facilitate communication between you and the kitchen. Similarly, an API facilitates communication between different software components. Chatbots can use APIs to access information or services from other applications.

    • Docker – a platform for developing, deploying, and running applications. It allows developers to package their code and all its dependencies into standardized units called containers. These containers can then be run on any machine with Docker installed, ensuring consistent behaviour regardless of the underlying environment. This is useful for deploying LLMs and chatbots because it makes them easier to share and run on different systems.

What is our offering?

LLM-based task-specific trained models (TSMs)

Built to execute only one task successfully (e.g., provide answers to customer support requests). For example, a model for drafting customised sales emails, or a model for retailers to generate product descriptions for their marketplaces.

LLM-based general-purpose trained models (GPMs)

Built to provide a wide range of GenAI offerings for the company, accessible via one interface. For example, a comprehensive GenAI model for the postal services group, which includes an Agile coach, has a module for client service, commercial assistants for bank and postal services, a conversational agent for those that deliver parcels at home, an HR module.

Business problems addressed

In both cases, GenAI allows for:

Automation of repetitive, mechanical tasks → reduction of manual workload

Aggregation of company’s knowledge (either in general or in a specific field) to easily transmit it to other employees via a simple interface (instructions, FAQs, databases, help, support functions, etc.) → addressing a lack of specialized expertise or lack of institutional experience (e.g., for new employees)

Synthesised answers about complex documentation/data → rapid decision-making

Customisation when providing services → in all B2C interactions of the company (from customer support to messaging)

Example use cases

  • ‍Q&A engine to receive synthesized answers about complex (e.g., financial) documents‍

  • Term sheet generator to output term sheets from short summaries of client calls, with numbers already filled in‍

  • Customer chatbot to provide instant, personalised answers to any request‍

  • Product description generator to write about products that are being sold by the company, using brand voice guidelines‍

  • Research assistant to access the most relevant, cited material, curated from trusted scholarly sources‍

  • Virtual health assistant to respond to patient questions with accurate medical knowledge‍

  • Internal agents' assistant for customer support representatives or other employees to instantly access accurate answers‍

  • Shopping assistant to accurately answer customer questions about any product‍

  • Insurance policy expert to receive synthesized answers about complex policies and documents‍

  • Copywriting assistant to transform notes into structured articles that are ready to publish‍

  • Google Ads copywriting assistant to generate conversion-optimized Google Ads that capture brand voice‍

  • Product/service FAQ generator to automatically build tailored FAQ sections

Making it work

Tasks for the client:

  • Provides us with training datasets (company material that is aimed to be outsourced to the assistant/model, chats and chat history (if chat service is already in-use))

  • Generates more datasets upon request

  • Generates interaction examples (example questions to the model, expected outputs)

  • Delineates any safeguards that are needed (e.g., for a model to say that any medical recommendation is only loose guidance and the person has to consult a real medical professional)

Our deliverables:

Integrated (pre-trained on given data and tested) TSM/GPM, with:

  • Secure API endpoints for model integration

  • Containerized application for easy deployment

  • Python scripts for model interaction and customization

  • Technical and user documentation

  • Recommendations for setting up necessary infrastructure for model deployment

This, in turn, allows for:

  • Automatisation of repetitive tasks, freeing up employee time

  • Synthesised insights from complex data provided to the model

  • Customised, tailored employee or user experiences

  • Reduction in operational costs, minimisation of human error

Project timeline

  • ‍Free consultation & recommendations, within 2 weeks of initial contact.‍

  • Analysis of processes & roadmap what to do, within 2 weeks of signing the contract.‍

  • Model training and integration, within 4 weeks of the day when the client provides the data.‍

  • New functionalities, retraining, upon separate agreements/invoicing.

Overall duration from around 3 months (TSMs) to 6-12 months (GPMs and multi-layered chatbots).

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