LLM product development services

We develop advanced products with Large Language Models (LLMs). Our services guide you from initial concept to deployment, providing the support needed at each stage.

LLM product development
We lead the development of Bielik – an open LLM
As founders of the SpeakLeash /ˈspix.lɛʂ/ project, we gather and share language data to drive LLM product development.
We co-develop Bielik, an open Large Language Model, using advanced techniques to meet diverse (language) needs.
Our work with top experts ensures the AI meets local language needs while upholding ethical standards.
LLM product development for Credit Agricole bank
We developed and deployed a fully operational AI Agent using LLMs in Credit Agricole’s customer service workflows.
This LLM-based AI Agent manages basic inquiries and directs complex ones to the appropriate teams.
Our deep understanding of LLM product development in regulated sectors ensures the AI meets strict (financial) regulations.

services for developing, integrating, and optimizing LLM

LLM product development services

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End-to-end LLM product development

We manage every stage of the LLM product development process. From concept and prototyping to testing and deployment. Each step is tailored to meet your goals and deliver value.
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Custom LLM development and deployment

We build and deploy custom Large Language Models (LLMs) tailored to your specific needs. We fine-tune models to address your business challenges and deploy them into your environment.
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LLM integration

We integrate LLM capabilities directly into your existing systems. We enhance how you process language data while keeping your current workflows intact. We ensure full compatibility with your tools and processes.
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Interactive Proof of Concept (PoC)/Proof of Vlaue (PoV)

We help you validate your ideas before committing to full-scale implementation. Our interactive PoC lets you test LLMs in your real-world environment, providing the data and insights needed for confident decision-making.

Our Generative AI development expertise


330
IT experts on board
11
awards and recognitions
for our GenAI solutions
236
clients served in custom development

We develop and deploy LLM solutions in your private infrastructure

What we cover in LLM product development


  • RAG development services - Consultation

    1. Discovery & analysis for LLM product development

    We start by understanding your business objectives and evaluating existing workflows. Through workshops and internal hackathons, we identify the best ways LLM product development can address your needs.

    We cover:

    • Defining project goals and understanding specific challenges.
    • Hosting workshops and internal hackathons to explore potential solutions.
    • Assessing data readiness and infrastructure for LLM implementation.
    • Creating a detailed plan with timelines, resources, and milestones.
  • Self-hosted LLM development - Custom LLM selection & fine-tuning

    2. Model selection and design for your LLM product

    We select and design the right LLM for your use case. Our approach ensures the model is a perfect fit for your needs and integrates into your environment.

    This step includes:

    • Choosing between pre-trained models or custom-built LLMs.
    • Designing models for compatibility with your existing software.
    • Creating a prototype to validate the chosen LLM’s performance.
  • Self-hosted LLM development - Training and optimizing

    3. LLM product development, training, and integration

    We build, train, and integrate the LLM into your systems. Our team manages the technical complexities, allowing you to focus on your core business operations.

    We provide:

    • Choosing the right LLM, then fine-tuning and training it with curated data for optimal performance.
    • Designing user interfaces, defining user flows, wireframes, and data formats for efficient LLM interactions.
    • Designing APIs to ensure smooth interactions between the LLM and your existing systems
  • Self-hosted LLM development - Monitoring & maintenance

    4. LLM testing and deployment into your environment

    We rigorously test the LLM to ensure it performs well under real-world conditions. Once tested, we deploy it into your infrastructure for immediate use.

    This step includes:

    • Testing the LLM across different scenarios, adjusting parameters for precise outputs, and ensuring data input and output are aligned with your standards.
    • Deploying the LLM in local environment.
    • Setting up configurations for optimal performance in your setup.

Comparison

Traditional ML development vs. LLM applications


Using LLMs instead of multiple task-specific models saves time and resources. A single LLM can be adapted for different tasks, making it easier to deploy and manage. This approach means quicker implementation and the ability to use the model across different applications.

  • Traditional ML Models

    Machine Learning (ML) models

    Traditional machine learning (ML) models require a dedicated model for each task. Each model demands its own dataset and extensive training, leading to a resource-intensive and time-consuming development process.

    For example:

    • Sentiment analysis: Requires labeled datasets of customer reviews (e.g., positive, negative, neutral).
    • Customer support chatbots: Uses datasets of customer-service interactions.

  • LLM Applications

    Large Language Models

    Large Language Models (LLMs) streamline this process. Pre-trained on extensive text datasets, LLMs are capable of handling diverse tasks without building new models for each use case. This flexibility enables businesses to quickly integrate LLMs into their operations, supporting applications such as:

    • Text summarization
    • Content generation
    • Multilingual translation
    • Information extraction
    • Customer support automation
    • Sales support and sentiment analysis

the complete stack to build, run, and optimize LLM Product

LLM application stack


Foundation model
Model selection: The foundation of an LLM application can be based on proprietary, open-source or custom-built models. Each option offers different advantages in terms of cost, flexibility, and control.
Customization: Customization is often necessary to align models with specific industry needs. This can involve fine-tuning pre-existing models or developing custom LLMs using proprietary data.
Domain-specific optimization: Specialized datasets enable models to better understand and process industry-specific language, enhancing their ability to perform tasks in sectors like finance, healthcare, or legal services.
ML infrastructure
Type of deployment: LLMs can be deployed on cloud platforms, or within a company’s own hardware setup. The choice depends on factors like data security needs and infrastructure control.
Compute resources: Running LLMs requires significant computing power. The infrastructure must accommodate these computational needs for consistent model performance.
Data management systems: Effective data management is crucial for storing and processing the large datasets used in training and deploying LLMs, ensuring smooth data flow throughout the model lifecycle.
Additional tools
Data pipelines: These are essential for processing and transforming data, enabling efficient integration with LLMs. Data pipelines ensure that both structured and unstructured data is ready for analysis and model training.
Vector databases: Vector databases are used to store embeddings. They facilitate efficient data retrieval, allowing LLMs to access relevant information quickly during interactions.
Orchestration tools: Tools like LangChain or LlamaIndex manage the flow of prompts and data between the LLM and external systems. They help automate interactions and ensure consistent outputs.

They trusted our expertise


cresit agricole logo
Dekra
Carefleet

Our featured Generative AI projects


  • AI Agent

    AI-powered assistant for customer service interactions

    CLIENT: CREDIT AGRICOLE

    • Message understanding: The system extracts key information from incoming messages and generates a summary containing the purpose and emotional tone. It helps eliminate human errors and ensures clear and uniform language
    • Intelligent routing: Simple requests are handled automatically for faster resolution, freeing up agents for more complex and personal interactions. More complicated messages are passed to the right teams.
    • Generating resources: The system creates customized draft replies and snippets. It can format them into PDFs for sending. It helps improve customer satisfaction scores, and meet service-level agreements.
  • AI assistant

    Intelligent sales assistant for credit card recommendations

    CLIENT: BANK • UAE

    • Meeting preparation assistance: The assistant helps sales representatives prepare for customer meetings. It provides detailed reminders about product terms and benefits for accurate and personalized recommendations.
    • Real-time data analysis: The assistant analyzes input from the salesperson in real-time and compares it against the conditions of over 20 different credit card products. Then, it issues accurate recommendations that meet both client expectations and bank requirements.
    • Integration with up-to-date product data: Direct integration with the bank’s product database ensures recommendations are based on the latest offer conditions.

Testimonial

What our clients say

By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.

Katarzyna Tomczyk – Czykier
Director of the Innovation and Digitization Division – Retail Banking

Why choose us

LLM product development experts

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LLM product development

We specialize in developing custom LLM solutions that fit your specific needs. Our expertise spans from fine-tuning pre-trained models to building custom LLMs, ensuring optimal performance for various use cases.
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Compliance with industry standards

We prioritize data security and privacy, ensuring that our LLM solutions comply with industry regulations such as GDPR, or CCPA. We understand the unique challenges of working in regulated sectors.
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Domain expertise

With experience in implementing LLMs in complex environments, we deliver solutions that address industry-specific challenges. Our work with clients like Credit Agricole shows our ability to meet strict regulatory and performance requirements.

Get in touch

Let’s talk


Book 1-on-1 consultation 

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Grzegorz Motriuk

Head of Sales | Application Development

Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.

Frequently asked questions

LLM product development FAQs