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Large Language Models (LLMs) Development Services

LLM Development Services


Improve your products and enhance your processes with the cutting-edge capabilities of large language models (LLMs). We integrate, fine-tune, and customize general-purpose LLMs to meet your unique business requirements.


Business benefits

LLM Development Services with Addepto


LLM Development Services. What you can expect?
Common Use Cases for LLM Applications
What are general-purpose LLMs?
What are domain-specific LLMs?
What are the techniques of customization LLMs?
Why Choose Addepto for Building Your LLM Applications?
Addepto's LLM-powered Knowledge Assistant: ContextClue

Addepto LLM development services offerings include:


Our LLM development services are designed to help you leverage the full potential of large language models, tailored specifically to your industry and objectives.

From enhancing model performance with your proprietary data to seamlessly integrating LLMs into your existing systems, we provide end-to-end solutions that ensure your AI initiatives deliver impactful, reliable results.

Here’s a closer look at what we offer:

Fine-tuning
We refine pre-trained Large Language Models (LLMs) using your proprietary data, enhancing performance for your specific domain and tasks.

Prompt engineering
Our experts craft precise prompts to maximize LLM effectiveness for your applications without modifying the base model.

Domain-specific Large Language Models (LLMs) creation
We develop specialized LLMs from the ground up for industries requiring deep, focused expertise.

Integration services
We seamlessly incorporate Large Language Models (LLMs) into your existing systems and workflows, ensuring smooth operation and maximum efficiency.

Performance optimization
Using techniques like quantization and pruning, we optimize Large Language Models (LLMs) for deployment on your target hardware.

Ethical AI implementation
We prioritize responsible AI development, implementing safeguards against biases and ensuring data privacy.

Scalability solutions
Our team designs solutions that grow with your business, from proof-of-concept to enterprise-wide deployment.

Ongoing support and maintenance
We provide continuous model updates, performance monitoring, and technical support to keep your



Large Language Models (LLMs) are used across various industries because they can understand and generate human language and analyze data.

Here are some of the most common use cases for LLM applications:

  • Chatbots and virtual assistants
    LLMs power chatbots that provide 24/7 customer service. These bots handle routine inquiries, use sentiment analysis, and escalate complex issues as needed, improving customer satisfaction and reducing operational costs.
  • Document analysis: Law firms employ LLMs to sift through extensive legal documents, conduct research, and streamline contract analysis, significantly reducing time spent on these tasks.
  • Market research: Businesses utilize sentiment analysis powered by LLMs to gauge public opinion about products or brands by contextual information and sentiment analysis in social media posts, reviews, and other user-generated content. This helps them understand customer preferences and improve offerings.
  • Targeted campaigns: LLMs analyze consumer data to create personalized marketing messages, improving engagement and conversion rates.
  • Content creation: Companies utilize LLMs to generate engaging ad copy and social media posts, enhancing their marketing strategies.
  • Content moderation: Social media or eCommerce platforms can use LLMs to detect inappropriate content in posts and reviews, ensuring a safer user environment by identifying threats or harassment.
  • Information retrieval and summarization: By quickly summarizing lengthy documents or reports, LLMs enhance the efficiency of knowledge management systems. They can sift through vast datasets to retrieve relevant information rapidly, making it easier for organizations to access the knowledge they need.
  • Knowledge managing: LLMs can transcribe meetings and discussions in real time, preserving critical insights that might otherwise be lost. This capability allows teams to focus on collaboration rather than note-taking.
  • Training and onboarding: They can personalize training materials based on individual progress and queries, creating tailored learning experiences that enhance onboarding processes.

General-purpose Large Language Models (LLMs) Explained


General-purpose LLMs are large language models trained on a vast and diverse range of text data, enabling them to perform a wide variety of language tasks without being specifically optimized for any single application.

These models can understand and generate human-like text across multiple domains and can be adapted to various tasks through fine-tuning or prompt engineering.

Examples of general-purpose LLMs include:

  1. GPT (Generative Pre-trained Transformer) series by OpenAI
  2. BERT (Bidirectional Encoder Representations from Transformers) by Google
  3. LLaMA (Large Language Model Meta AI) by Meta (Facebook)
  4. Claude (the AI I’m based on) by Anthropic

These models can be used for various tasks such as text generation, translation, summarization, question-answering, and more. They serve as a foundation that can be customized or fine-tuned for specific applications, which is likely what your company offers as a service.


General-purpose LLMs vs domain-specific LLMs


General-purpose LLMs, such as GPT-3, BERT, and Claude, are trained on diverse data across multiple domains, enabling them to perform a wide range of language tasks with broad knowledge.

Domain-specific LLMs are trained or fine-tuned on data from a particular field, making them highly specialized in understanding and generating text within that domain.

General-purpose LLMs offer broad applicability but may require fine-tuning for specialized tasks, while domain-specific LLMs provide greater accuracy and relevance within their field but have limited versatility outside it. Domain-specific models excel at understanding specialized terminology and can be more efficient for targeted applications.


The most popular techniques of customizing LLMs include:


  • Fine-tuning
    This involves further training the pre-trained model on a specific dataset relevant to the target domain or task. Fine-tuning can be done on the entire model or just on certain layers.
  • Prompt engineering
    Crafting specific prompts to guide the model’s output for particular tasks without changing the model itself.
  • Retrieval-augmented generation (RAG)
    Combining LLMs with external knowledge bases to enhance their capabilities and accuracy in specific domains.

These techniques allow for customizing LLMs to specific use cases while balancing factors such as performance, efficiency, and resource requirements.


Addepto - your partner in developing AI solutions


Addepto brings a unique blend of expertise and flexibility to building LLM-powered applications, making us an ideal partner for businesses looking to harness the full potential of Generative AI.

Here’s why partnering with Addepto sets you up for success:

  • Expertise with all state-of-the-art LLMs
    We have deep experience working with a wide range of leading LLMs, from API-first commercial solutions to advanced open-source models. This flexibility allows us to select and customize the best model for your needs, whether you’re looking for established industry leaders or cutting-edge open-source options.
  • Advanced customization techniques
    Our team excels at adapting LLMs to fit your unique business requirements using various customization methods, including fine-tuning with proprietary data, advanced prompt engineering, and domain-specific model development. We ensure your AI solution aligns with your specific goals and delivers optimized, impactful performance.
  • Seamless integration with your infrastructure
    We understand the importance of a smooth integration process. With in-depth knowledge of IT systems and workflows, we ensure that your LLM application integrates seamlessly with your existing infrastructure, minimizing disruption while maximizing efficiency and compatibility with current operations.
  • Comprehensive, multi-technology AI systems
    At Addepto, we go beyond LLMs to build comprehensive AI ecosystems that combine a range of technologies, including machine learning, computer vision, and natural language processing. Our holistic approach allows us to create a cohesive system tailored to the unique needs of your business, delivering end-to-end AI solutions that drive results.

By choosing Addepto, you’re selecting a partner with the flexibility, expertise, and innovation to create a robust LLM application that evolves with your business, leverages the best AI technology available, and supports your long-term goals.

With a proven track record across diverse industries and a focus on cost-effective innovation, we don’t just provide LLM solutions—we become your dedicated ally in harnessing AI’s full potential for your business.

Check out our Case Studies


ContextClue: Advanced knowledge managing and data analysis through Generative AI solutions


ContextClue, developed by Addepto, transforms enterprise knowledge management by going beyond traditional data analysis.

Going beyond traditional text analysis, this versatile tool seamlessly handles multiple data formats – from PDF documents and images to SQL databases, BI dashboards, and spreadsheets.

What sets ContextClue apart is its ability to serve as a comprehensive knowledge base assistant, understanding context and relationships across all your data sources.

Through advanced Generative AI solutions, it doesn’t just analyze data – it becomes your organization’s memory, capable of answering complex queries, identifying patterns, and delivering insights from your entire information landscape.

Organizations can maintain complete control over their data by deploying ContextClue on their private infrastructure. This secure large language model deployment model ensures sensitive information remains protected while delivering powerful analytical capabilities.

The platform’s extensive customization features make it an ideal foundation for building specialized enterprise applications. By utilizing ContextClue as a development accelerator, organizations can rapidly deploy tailored Generative AI solutions, significantly reducing both development time and resource investment compared to building from scratch.

Ready to transform your data analysis capabilities? Contact Addepto to explore how ContextClue can drive innovation in your organization.

Discover more: ContextClue



Our clients



Large Language Model (LLM) development process








Discovery and requirements gathering


  • Consultation to understand your business nuances
  • Identifying specific use cases and challenges
  • Defining key performance indicators (KPIs)

Data assessment


  • Evaluating available data sources

Model selection and architecture design


  • Choosing the appropriate foundational LLM
  • Designing custom architecture for your application
  • Planning for scalability and performance optimization

LLM Development and customization


  • Implementing the chosen LLM architecture
  • Customization of the model on your domain-specific data
  • Developing necessary APIs and integrationsIdentify gaps, risks, and areas requiring additional investment or preparation for successful Gen AI adoption.
  • Develop mitigation strategies and contingency plans to address potential roadblocks or challenges.

Testing and deployment


  • Testing for accuracy and performance
  • Bias detection and mitigation
  • Integration with your existing systems
  • Implementing security measures and access controls
  • Providing documentation and user training

LLM Industry expertise



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LLM in Healthcare


The most popular applications of LLM in Healthcare:

  • Clinical Diagnosis and Treatment Suggestions – LLMs can rapidly analyze vast amounts of medical literature, offer potential diagnoses based on symptoms, and suggest treatment options.
  • Personalized Medicine and Health Plans -LLMs can create tailored health plans by understanding patients’ medical history, lifestyle, and genetic factors.
  • Medical Research and Drug Discovery – LLMs accelerate research by analyzing large datasets, highlighting potential links between studies, and speeding up drug discovery processes.
  • Health Data Analysis and Predictive Healthcare – LLMs can predict disease outbreaks, identify at-risk patients, and suggest preventive measures.

LLM in Finance and Insurance


The most popular applications of LLM in Finance and Insurance:

  • Trading and Market Analysis – LLMs analyze news, market data, and financial indicators to make predictions about asset performance.
  • Fraud Detection – LLMs use pattern recognition to flag anomalies in financial transactions and schemes.
  • Personalized Financial Advice – LLMs can provide customized financial recommendations based on individual user data and market trends.
  • Regulatory Compliance – LLMs assist in ensuring compliance with financial regulations by analyzing and interpreting complex legal texts.”

LLM in Legal


The most popular applications of LLM in Legal:

  • Contract Analysis – LLMs can review and analyze legal contracts, identifying key clauses and potential issues.
  • Legal Research – LLMs can quickly search through vast legal databases to find relevant case law and precedents.
  • Document Generation – LLMs can assist in drafting legal documents, such as contracts or briefs, based on specific requirements.
  • Compliance Monitoring – LLMs can help organizations stay compliant with changing regulations by continuously analyzing legal updates.

LLM in Retail


The most popular applications of LLM in Retail:

  • Customer Service Chatbots – LLMs power advanced chatbots that provide personalized responses to customer inquiries and offer product recommendations.
  • Sentiment Analysis – LLMs analyze customer feedback from various sources to identify pain points and opportunities for improvement.
  • Inventory Management – LLMs can optimize inventory levels by analyzing sales trends and predicting future demand.
  • Voice Commerce – LLMs can enable natural language processing for voice-activated shopping assistants, enhancing the ease of online shopping.


Healthcare
Finance & Insurance
Legal
Retail

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Technologies that we use



Open Source LLM

Commercial LLM

Programming languages

Database

Frameworks


LLAMA


LLAMA – A family of large language models released by Meta AI, ranging from 7B to 65B parameters, aimed at advancing research in areas like instruction following and multi-task learning.

BLOOM (Hugging Face/BigScience)


BLOOM (Hugging Face/BigScience) – Model trained on a large multilingual dataset, developed by the BigScience workshop and Hugging Face.

Falcon (Anthropic)


Falcon (Anthropic) – Large Language Model released by Anthropic, focused on being safe and truthful.

Stable Diffusion


Stable Diffusion – An open-source text-to-image generative AI model capable of creating highly detailed images from text prompts, developed by Stability AI
Mistral


Mistral – A large open-source language model trained by LAION, comparable in size to GPT-3 but with a focus on safety and truthfulness.

Claude (Anthropic)


Claude (Anthropic) – A constitutional AI assistant from Anthropic that aims to be honest, harmless, and have stable long-term preferences aligned with human values.

Gemini (Google)


Gemini (Google) – Another large language model from Google, focused on open-ended conversation and question-answering

GPT-3.5 (OpenAI)


GPT-3.5 (OpenAI) – The predecessor to GPT-4, known for its strong language generation abilities but with some limitations in areas like math and commonsense reasoning

GPT-4 (OpenAI)


GPT-4 (OpenAI) – The latest and most advanced language model from OpenAI, succeeding GPT-3.5. It has improved capabilities across various tasks like question-answering, writing, and coding.
Python


Python – Python is a versatile, high-level programming language known for its simplicity and readability, making it an ideal choice for beginners and experienced developers alike in fields ranging from web development to data analysis and machine learning.
MongoDB


MongoDB – a document-oriented database that stores data in JSON-like documents with the dynamic schema. It is great for transactional stores where performance is a concern. Its schema-less operations allow you to update the data on the fly.

PySpark


PySpark – used to transform data. It enables you to run AI applications on billions of data on distributed clusters 100 times faster than the traditional python applications.
PyTorch


PyTorch – PyTorch: Developed by Facebook, PyTorch is an open-source ML library that allows data scientists to easily prototype and deploy models for applications in computer vision and natural language processing.

TensorFlow


TensorFlow – TensorFlow: An open-source machine learning library from Google, TensorFlow simplifies building and training ML models with high-level APIs like Keras and offers visualization tools.

Hadoop


Hadoop – Hadoop – is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs.

Key benefits

What business benefits you can get with
custom LLM application?



Improved accuracy and relevance


Domain-specific LLMs are trained on industry-relevant data, enabling them to understand specialized terminology and context better. This leads to more accurate and contextually aware outputs, especially in fields with unique jargon or complex concepts.


Enhanced operational efficiency


By automating routine tasks and providing more accurate insights, domain-specific LLMs can significantly improve operational efficiency. This allows professionals to focus on more complex decision-making processes, ultimately saving time and resources.


Cost-effectiveness and scalability


While there’s an initial investment in developing custom LLMs, they can be more cost-effective in the long run. These models can handle increased workloads without a proportional rise in expenses.


Competitive advantage


Domain-specific LLMs allow businesses to offer more sophisticated, tailored services that address the unique challenges of their industry. This customization can set companies apart from competitors who rely on general-purpose AI solutions, potentially leading to improved customer experiences and increased market share.


LLM Development - FAQ


What is an LLM, and how can it benefit my business?
How does a custom LLM differ from off-the-shelf AI solutions?
How long does it typically take to develop and implement a custom LLM solution?
What kind of ROI can I expect from implementing an LLM in my business processes?
Can LLMs integrate with our existing software and systems?
What are Large Language Models (LLMs)?
How LLMs integrate with machine learning


What is an LLM, and how can it benefit my business?


An LLM (Large Language Model) is an advanced AI system trained on vast amounts of text data, capable of understanding and generating human-like text. It can benefit your business by automating complex tasks, enhancing customer service, improving decision-making processes, and streamlining operations. LLMs can handle everything from content creation and data analysis to personalized recommendations and intelligent automation, potentially increasing efficiency and unlocking new capabilities across various departments.

How does a custom LLM differ from off-the-shelf AI solutions?


A custom LLM is tailored specifically to your business needs, industry, and data, unlike off-the-shelf solutions that offer general capabilities. Custom LLMs are trained on your proprietary data and industry-specific information, allowing them to understand your unique terminology, processes, and context. This results in more accurate, relevant, and actionable outputs. Custom LLMs can be integrated seamlessly into your existing workflows and systems, providing a competitive edge by addressing your specific challenges and opportunities in ways that generic AI solutions cannot.

How long does it typically take to develop and implement a custom LLM solution?


The development and implementation timeline for a custom LLM solution can vary depending on the complexity of your requirements and the scale of implementation. With an experienced partner, the development process can be significantly accelerated thanks to their fluency in implementing AI according to best practices and their ability to combine state-of-the-art AI models with custom techniques and approaches.

What kind of ROI can I expect from implementing an LLM in my business processes?


The ROI from implementing an LLM can be substantial, though it varies based on the specific application and your business context. Typically, businesses see returns in the form of increased efficiency, cost savings from automation of routine tasks, and improved decision-making leading to better business outcomes. Additionally, LLMs can open up new revenue streams through enhanced products or services.

Can LLMs integrate with our existing software and systems?


Yes, LLMs can be seamlessly integrated with your existing software and systems. We design our LLM solutions with interoperability in mind, using standard APIs and protocols to ensure smooth integration with your current tech stack. Whether you’re using CRM systems, ERP software, data analytics platforms, or custom in-house solutions, we can develop connectors and interfaces to allow the LLM to interact with these systems. This integration enables the LLM to access relevant data, enhance existing processes, and provide insights within your familiar work environments. Our team will work closely with your IT department to ensure a smooth and secure integration process.

What are Large Language Models (LLMs)?


Large Language Models (LLMs) are advanced AI systems that excel in Generative AI and Natural Language Processing (NLP), enabling them to understand and generate human-like text. These models are trained on extensive datasets, allowing them to learn the nuances of language and context.

Key Features of Large Language Models (LLMs):

(more…)

How LLMs integrate with machine learning


Large Language Models (LLMs) can be effectively combined with machine learning (ML) to enhance various applications and improve overall performance.

This integration leverages the strengths of both technologies, allowing for more sophisticated data processing and decision-making capabilities:

  • Enhanced data processing
    LLMs excel at understanding and generating human language, making them ideal for preprocessing tasks. They can automate data cleaning, standardize formats, and transform unstructured text into structured data, streamlining the preparation for ML models.
  • Improved predictive analytics
    By analyzing textual data (like customer feedback), LLMs can provide insights that enhance the predictive capabilities of ML models. This integration allows for more accurate predictions by considering a wider range of factors.
  • Automation of complex tasks
    LLMs can simplify complex tasks that typically require extensive ML pipelines. For example, they can classify emails or generate responses without needing custom ML models, saving time and resources.
  • Continuous learning
    LLMs can adapt to new data in real-time, allowing them to stay relevant in fast-changing environments. Techniques like Retrieval Augmented Generation (RAG) enable them to incorporate up-to-date information into their responses.


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