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.
Our Generative AI development expertise
We develop and deploy LLM solutions in your private infrastructure
What we cover in LLM product development
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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.
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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.
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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
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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.
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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.
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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
They trusted our expertise
Our featured Generative AI projects
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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.
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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.
Why choose us
LLM product development experts
LLM product development
Compliance with industry standards
Domain expertise
Get in touch
Let’s talk
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Frequently asked questions
LLM product development FAQs
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How long does it take to develop an LLM-based product?
The timeline varies based on the complexity of your needs and the level of customization required, typically ranging from a few weeks to several months.
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What kind of data is needed for LLM fine-tuning?
We use domain-specific data that reflects your business context, such as customer interactions, industry-specific terminology, or internal documents, to fine-tune the model.
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How do you ensure data security during LLM development?
We prioritize data security through encryption, secure storage, and adherence to regulations like GDPR. All sensitive data remains protected throughout the training and integration process.
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Can you develop LLM products for regulated industries?
Yes, we have extensive experience working with regulated sectors, including finance and banking. We ensure that our solutions comply with industry-specific regulations and standards.
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What are the costs involved in LLM product development?
Costs depend on the scope of the project, including model selection, customization, and integration. We provide a tailored quote based on an initial assessment of your needs.