Retrieval Augmented Generation Development Services

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Benefits of
Retrieval Augmented Generation

Maximize the efficiency and performance of your Generative AI LLM with Retrieval-Augmented Generation (RAG).

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Cost-effective Implementation

Reduce costs by avoiding retraining large language models. Leverage existing data sources to enhance model performance without extensive reworking.

Current Information

Keep your responses up-to-date by connecting to live data sources like social media feeds or news sites, ensuring your model provides the latest information.

Enhanced User Trust

Improve user confidence by providing accurate information with source attribution, allowing users to verify and trust the data presented.

More Developer Control

Gain flexibility in managing information sources, adapting to changing requirements, and ensuring secure, relevant responses through controlled data retrieval.

Improved Accuracy

Reduce the risk of inaccuracies by retrieving information from authoritative sources, minimizing errors due to outdated or incorrect training data.

Efficient Troubleshooting

Easily identify and correct issues in model responses by tracing information back to its source, enhancing the overall reliability of your AI solutions.

Selected Industries Where We Have Special Expertise

Leverage our deep industry expertise in RAG to transform your AI capabilities. We help you deploy sophisticated, scalable solutions that meet the unique demands of your business.

Blockchain

CRM

Education

Enterprise Software

Fintech

Game Development

Healthcare Services

IoT

Schedule A Call

Book a time for a free consultation with one of our AI development experts to explore your Retrieval Augmented Generation requirements and goals.

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How Retrieval Augmented Generation Works

RAG enhances the capabilities of large language models by integrating external data sources, leading to more accurate and contextually relevant responses.

Create Data

Collect and convert external data into a format compatible with your model, using APIs, databases, or document repositories to build a comprehensive knowledge base.

Retrieve Relevant Information

Perform a relevancy search by converting user queries into vector representations and matching them with the vector database, ensuring highly relevant information retrieval.

Improve the LLM Prompt

Enhance user prompts by adding retrieved data in context, using prompt engineering techniques to communicate effectively with the large language model.

Refresh External Data

Maintain data relevancy by regularly updating your knowledge base through automated processes, ensuring your model always has access to the latest information.

Where We Can Help
in Retrieval Augmented Generation

Customized Data Integration

We assist in integrating your unique data sources, ensuring seamless compatibility with your large language models for optimal performance.

Relevancy Search Optimization

We fine-tune relevancy search algorithms, ensuring the most relevant information is retrieved and used by your models.

Prompt Engineering

We provide advanced prompt engineering techniques to enhance the effectiveness of your large language models, ensuring accurate and contextually relevant responses.

Data Updating Strategies

Implement robust strategies for keeping your data sources up-to-date, ensuring your models always provide the latest and most accurate information.

Security and Compliance

Ensure your data retrieval processes adhere to the highest security standards and regulatory requirements, protecting sensitive information and maintaining user trust.

Monitoring

Continuous monitoring and optimization of your RAG implementations, ensuring consistent performance and reliability of your AI-driven solutions.

Our Retrieval Augmented Generation Software Development Services

We have worked with many of the most popular tools, frameworks and technologies for building AI and Machine Learning based solutions.

Natural Language Processing

Reinforcement Learning

Chatbots

Semantic Search

AI Software Development Process

We develop custom enterprise-grade AI and Machine Learning solutions. From model selection, to data labeling, to deployment, we can help you design and develop custom AI solutions platforms that are tailored specifically to your needs.

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Illustration of the Generative AI software development process

STEP 1

Model Selection and implementation

Our data scientists will work with you to either build a machine learning model from scratch or select a pre-trained model that is suitable for your project. We will handle the implementation of the model using a programming language like Python.

STEP 2

Data Labeling

Data preparation is a fundamental aspect of every AI model. Prior to training the machine learning model, we will meticulously label your data. This pivotal step entails assigning a class or label to a subset of your dataset and makes it ready for data analysis

STEP 3

Model Trainng

Our machine learning engineers will train your ML model using your labeled dataset. We will adjust the model's parameters to minimize the error between the predicted labels and the true labels.

STEP 4

Model Optimizaton

After the initial training is complete, our data scientist will work with you to iterate on the training process and try different techniques to improve the model's accuracy. This may include adjusting the model's hyper parameters or using different AI techniques for preprocessing the data.

STEP 5

Deployment to Production

Once the model is performing to your satisfaction, our team will assist with deploying machine learning models to production. This may involve integrating the model into an existing application or building a new application specifically designed to use the model.

How We Can Work Together to Develop Your Retrieval Augmented Generation Software Solution

The best software solutions enhance and enable business. That is why we focus on developing cost-effective nearshore software solutions and apply a delivery model that will achieve your goals and timeline.