Large Language Model Development

Our experienced LLM company provides strategic counseling to organizations interested in taking advantage of LLMs for sustainable business growth. Through our LLM consulting, companies receive tailored implementation strategies and learn how to employ LLMs both ethically and securely for their unique business applications.

If you’re seeking LLM customization services, you’ve come to the right place. Using prompt engineering, retrieval augmented generation (RAG), and fine-tuning, we customize pre-built foundation models (FMs) based on domain-specific data and knowledge. As a result, companies can set their offerings apart, improve decision-making, and increase profits.

Our specialists can adjust the parameters and weights of pre-trained LLMs to suit your industry-related applications. To ensure a frictionless process, we train LLMs on limited labeled data, follow strict compliance and security guidelines, and implement diverse fine-tuning methods like reinforcement learning from human feedback (RLHF).

We help organizations design custom prompts that align with their business objectives and enable the LLMs they use to generate more accurate, unbiased, and context-aware responses. Through prompt engineering techniques like zero- and few-shot learning, we create powerful LLMs that deliver reliable outputs and improve user experience (UX).

Do you want to benefit from pre-built LLMs, such as PaLM 2 or Claude, without additional customization and fine-tuning? Our LLM development company is up to the task. We use proven implementation methods like API-based integration to embed LLMs in your systems and infrastructure, prioritizing model performance, reliance, and security.

If you need to build AI-powered software solutions like chatbots, speech recognition tools, or content creation apps, EffectiveSoft is ready to support you. We deliver high-performance, scalable, and secure systems that integrate advanced LLMs for a wide range of text-related tasks, from text editing and proofreading to personalized customer support.

For maximum effectiveness, you need an LLM development company to take full care of your models after deployment. To ensure the long-term operation of your LLMs, we monitor them for performance, security, and compliance; troubleshoot all issues as they arise; and update the models to accommodate the latest language trends and evolving user needs.

Do you want to ensure your LLMs are inherently secure, scalable, and bias-free? Tap into our end-to-end LLM operations (LLMOps) services. Drawing on our deep expertise in LLMOps techniques, tools, and best practices, we guide businesses in effectively managing the full life cycle of their LLMs, from model training to ongoing improvement.

Do persistent hallucinations in your LLMs cause factually inaccurate, nonsensical, or biased outputs, pushing customers away from your products and services? EffectiveSoft can help. We leverage efficient hallucination reduction methods like advanced prompting and RAG to help businesses minimize hallucinations and make the most of their LLMs.

We combine supervised, semi-supervised, unsupervised, and reinforcement learning with scikit-learn, Keras, PyTorch, TensorFlow, and other frameworks to create powerful machine learning (ML) models for use cases like predictive analytics.

Our AI engineers use spaCy, NLTK, and TextBlob to build bespoke natural language processing (NLP) models with advanced generation and understanding capabilities for key phrase extraction and document categorization.

We incorporate NLP-based sentiment analysis into virtual assistants, chatbots, and other AI solutions, allowing businesses to enhance their products and services based on the collected customer feedback and evaluated text and voice sentiment.

For clients who want to avoid building an LLM from scratch, we employ a transfer learning (TL) method. We apply knowledge from a pre-trained LLM like GPT to address a new but similar language task by using fine-tuning or feature extraction.

Our specialists support our clients in tailoring the parameters of pre-trained LLMs for their domain-specific applications. We further train these existing LLMs on smaller and more focused datasets and implement techniques like hyperparameter tuning.

EffectiveSoft’s team helps businesses precisely adjust existing LLMs to new activities by implementing few-shot learning. We train a model using only 3–5 labeled datasets, after which it effectively applies the learned patterns to complete novel tasks.

Our LLM developers excel in using a zero-shot learning method, where a model performs tasks without receiving extra training on labeled data. In this case, the LLM relies only on its knowledge from previous training sessions and its general grasp of language.

EffectiveSoft helps organizations in various sectors enhance the performance and accuracy of their existing LLMs with in-context learning (ICL). Through prompts, we train models to adapt to new contexts and solve novel tasks without the need for any fine-tuning.

Our team of top-tier data specialists helps companies establish resilient and scalable data infrastructures to gather, process, and organize the massive amounts of text information required to train, fine-tune, and deploy high-quality and bias-free LLMs.

With our strong proficiency in cloud computing, we expertly guide our clients in deploying LLMs on suitable cloud platforms like OCI, AWS, Azure, and GCP. We ensure your models perfectly fit in the cloud and scale securely as user demand grows.