Understanding large language models (LLMs)

LLMs augment human creativity and decision-making with insights and efficiency at scale. Such models as ChatGPT can understand and generate text in a human-like way. They can perform tasks such as:

  1. Use statistical probability to “guess” the right answer

    LLMs work by predicting the most likely sequence of words. For example, if an LLM trains on data with many instances of the phrase “grass is green,” when the user writes “grass,” the LLM will assume it should reply with “is green.”

  2. Accurately mimic human speech and reasoning

    LLMs train on datasets, which contain trillions of words and can adapt to infinite topics, understand intent and creatively solve tasks they’re assigned — just like a human.

  3. Remember and recall information

    LLMs have memory. They retain information from previous interactions and can reference data. For example, an AI-chatbot might “read” internal documents, find a solution to a problem, and resolve a simple ticket — all without engaging a human agent.

How companies use LLM and AI

LLMs are versatile — their ability to read and write like a human makes them ideal assistants in:

  1. Controlling communication quality

    LLMs analyzes the communication of sales and support agents, rate their responses, and provide constructive feedback to improve the quality of business services or flag violations.

  2. Searching across datasets

    Retrieval-Augmented Generation (RAG) grasps the user’s intent, turns any text into a precise search query and retrieves information from the company’s knowledge base or database.

  3. Finding insights in unstructured data

    LLMs can analyze multiform data directly, even if emails, reviews, and analytics reports are parts of one dataset; identify patterns, and organize everything into a consistent format.

  4. Automating various routine tasks

    They automate routine: take notes from meetings, write out action points, attach to a call, email to participants — initiate all this with a single text command.

  5. Generating copy and documents

    LLMs accurately translate texts, videos, audios into any language, write marketing copies that take into account the company’s brand voice.

  6. Automating corporate processes

    LLMs analyze datasets from corporate branches across regions and languages to understand and address turnover causes.

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Why business consider AI consulting services

AI consulting will help businesses streamline processes, cut costs through automation, or break through plateaued performance. This service is for companies that:

  1. Want to implement an LLM and need a defined strategy for growth


    Company aims to structure the communication with users across all channels: emails, support tickets, app reviews in stores. Then, LLM finds patterns, highlights trends, and helps identify growth points.

  2. Already have a specific LLM use-case, but lack technical know-how


    Company wants to create a solution based on artificial intelligence and seamlessly integrate it into its existing infrastructure, technological stack, and business processes.

  3. Encountered plateaued performance and want to solve it with an LLM


    Businesses want to pinpoint areas where automation can bring benefits, craft an AI implementation strategy, and collaborate on launching a pilot program to address performance plateaus.

We find and implement AI solutions in six steps

We learn all about business processes, technology, and goals, and collaborate closely to find the right solution for business context

  1. Define pain points and business objectives

    Learning about the client’s operations, challenges, goals, sorest points and discovering which of them they tried and want to solve with AI, we define the work plan.

  2. Audit processes and identify use cases

    We dive into operations, services, knowledge bases, looking for time-consuming tasks, inefficient processes, and potential starting points for integration.

  3. Propose and select AI implementation solutions

    We research and select LLM platforms that align with specific business and infrastructure, like GPT-4, LLaMa, Tune AI, Google Cloud Natural Language API, etc.

  4. Develop the MVP and pilot small projects

    Our engineer develops a straightforward algorithm, test-drives functionality, measures impact, and demonstrates its efficiency on defined use cases.

  5. Build the final version and expand across the company

    We transform the solution into an interface, gradually expand its use across your organization and train your team to maximize the value of AI.

  6. Analyze performance and refine

    We monitor the performance of LLM implementations, measure outcomes against the client’s objectives, and iterate based on feedback and results.

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Vladi Makeew

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