Generative AI Development Services

Use generative AI models to stay ahead of your competition.

Become a generative AI company with skilled experts

Generative AI and large language models (LLMs) are spearheading the AI revolution and transforming industries and businesses. Partnering with an experienced team like Software Mind is essential to implementing LLMs solutions successfully – our specialists are ready to help with your company’s generative AI needs.

Our generative AI development and large language model services

Implementing LLMs in business

Utilize generative AI models to enhance decision-making, discover valuable insights, achieve greater efficiency and increase competitive advantages. 

Vector semantic search

Use the power of vector search and natural language processing (NLP) to quickly and accurately comprehend users’ intents without an exact keyword match. 

Generative AI development

Take advantage of AI tools like GitHub Pilot and Microsoft Copilot to boost developers' productivity by streamlining their workflows and helping them code faster. 

Autonomous agent development

Deploy autonomous AI agents like AutoGPT or BabyAGI that independently work, learn and adapt to empower your organization in everyday tasks. 

Text generation

Incorporate generative AI into your creation process to develop in-depth content that can be used to improve efficiency.

Fine-tuning AI models

Customize the outputs of your AI models to match your specific needs. With fine-tuning, you can adapt pre-trained AI models to specific tasks, while enhancing their performance and reducing training times. 

Image generation

Tap into a virtually infinite resource pool of creative and imaginative graphics generated in an instance to save time and money.  

Reinforcement learning from human feedback (RLHF)

Combine reinforcement learning with human feedback to improve your AI models. Enable AI systems to learn optimal behaviors through iterative corrections to produce more accurate, efficient and robust outputs.

Real-world benefits of tailored generative AI solutions

Enhanced task automation

Automate and speed up tasks to reduce the amount of time and effort needed to complete them. Improve accuracy, reduce the number of errors and revamp overall quality. 

Improved data analysis 

Elevate data analysis by leveraging AI-generated insights to collect, organize and visualize vast data sets. Develop products and grow your business faster than your competition. 

Boosted software development

Increase the efficiency of software development by helping programmers be more productive, less frustrated when coding and more focused on their jobs.

Reduced costs

Drive down the costs of your company's daily operations by implementing LLM-based solutions to support daily operations. 

Advanced personalization

Gain a competitive edge through tailored content, customized solutions and bespoke development. Empower your team’s work through the help of a generative AI assistant.  

Upgraded process automation

Minimize or eliminate human input by automating complex processes using generative AI. Maximize your company's existing procedures and operations.   

Large language models: our proven system building process 

01

Consulting Phase 

Consulting Phase 

Objective: Understand a client's needs and determine the potential of LLMs in meeting those needs.
Initial client meeting: Ascertain specific use-cases (e.g., chatbots, content generation, data analysis).
Evaluation: Analyze current content and data infrastructure. Determine potential areas where LLMs can add value for a client.

03

MVP (Minimum Viable Product)

MVP (Minimum Viable Product)

Objective: Implement a basic version of the LLM solution to validate its efficacy.
Setup: Integrate the LLM into a client's environment, possibly using cloud platforms or APIs.
Prototype Development: Create a simple implementation based on the defined use-case.
Initial Testing: Test the LLM's outputs, accuracy, and relevance.
Feedback Collection: Understand how end-users or stakeholders perceive its performance.

05

Scaling

Scaling

Objective: Expand the LLM's implementation to cater to broader applications and more users.
Infrastructure Enhancement: Ensure that the infrastructure can handle more queries and data as usage grows.
Parallel Processing: Utilize techniques to handle multiple simultaneous requests to the LLM.
Deployment Strategies: Decide on cloud vs on-premises deployments based on scale requirements.

02

Identifying Scope

Identifying Scope

Objective: Assess the suitability of LLMs for a client's specific problems.
Data & Content Assessment: Understand the kind of data/content a client deals with and its volume.
Problem Definition: Define what a client wants to achieve through LLMs.
Approach Selection: Decide how to use the chosen LLM.
Ethical & Bias Evaluation: Ensure understanding of potential pitfalls, biases, and risks.

04

End-to-end Development

End-to-end Development

Objective: Fully develop the LLM-based solution, incorporating MVP feedback.
Fine-tuning (if permitted by the model): Train the LLM on specific datasets to better suit a client’s needs.
Integration: Ensure that the LLM works seamlessly within a client's existing digital infrastructure.
UI/UX Development: Optimize the user experience if the LLM interfaces directly with users.
Comprehensive Testing: Check for potential issues, especially those related to incorrect outputs or biases.

06

Maintenance 

Maintenance 

Objective: Offer ongoing support and improvements for the LLM solution.
Continuous Monitoring: Track the LLM's performance and outputs.
Regular Updates: Implement updates or adjustments based on new model releases and specific client needs.
User Feedback Loop: Continuously gather feedback to identify areas for improvement.

01

Consulting Phase 

Consulting Phase 

Objective: Understand a client's needs and determine the potential of LLMs in meeting those needs.
Initial client meeting: Ascertain specific use-cases (e.g., chatbots, content generation, data analysis).
Evaluation: Analyze current content and data infrastructure. Determine potential areas where LLMs can add value for a client.

02

Identifying Scope

Identifying Scope

Objective: Assess the suitability of LLMs for a client's specific problems.
Data & Content Assessment: Understand the kind of data/content a client deals with and its volume.
Problem Definition: Define what a client wants to achieve through LLMs.
Approach Selection: Decide how to use the chosen LLM.
Ethical & Bias Evaluation: Ensure understanding of potential pitfalls, biases, and risks.

03

MVP (Minimum Viable Product)

MVP (Minimum Viable Product)

Objective: Implement a basic version of the LLM solution to validate its efficacy.
Setup: Integrate the LLM into a client's environment, possibly using cloud platforms or APIs.
Prototype Development: Create a simple implementation based on the defined use-case.
Initial Testing: Test the LLM's outputs, accuracy, and relevance.
Feedback Collection: Understand how end-users or stakeholders perceive its performance.

AI models we use

Advanced AI models, known as large language models (LLMs), are trained on extensive amounts of data, which enable them to complete various tasks. Here are the most pivotal AI models.

GPT-4

An industry changing LLM released by OpenAI and used by Chat GPT, GPT-4 delivers improved performance, reliability and creativity.

PaLM 2

A next-generation LLM built by Google and used in chatbots like BARD, PaLM excels at understanding tasks, classification, multilingual proficiency and NLP.

LLaMa 2

Large Language Model Meta AI (LLaMa), designed by Meta to help researchers, requires less computing power and resources than other generative AIs. 

Falcon 40b

A family of language models developed by the Technology Innovation Institute and released under the Apache 2.0 license, it acts as an open model and enables custom modifications.   

Dolly v2

A large language model trained on the Databricks machine learning platform and known for its advanced ability to follow instructions.

Client reviews

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Industry-based generative AI services 

Finance and accounting 

Enhance financial forecasting and analysis by leveraging AI-generated insights. Automate the generation of financial reports and audits, as well as streamline accounting processes. 

Language translation

Facilitate communication by translating content across languages quickly and accurately. Bridge language barriers to expand business reach and improve collaboration across international teams. 

Software development  

Assist developers in writing code more efficiently by providing suggestions, identifying bugs and analyzing their final work for a faster time to market. Test scripts and automatically generate documentation and comments, while enhancing code quality and maintainability.   

Content creation

Summarize long documents, extract key insights and generate high-level overviews for faster decision-making. Draft marketing materials, blog posts and social media content.

Legal and compliance

Analyze legal documents and contracts for potential risks and ensure compliance with relevant regulations. Automate the drafting of legal texts and contracts to save time and resources.

Research and development 

Accelerate the ideation process and generate innovative solutions to business problems. Review and summarize academic literature to stay updated on industry trends and developments.

Data analytics

Extract insights from datasets and uncover patterns and trends that inform business decisions. Examine customer behavior and preferences to create personalized product recommendations.

Healthcare 

Design AI-supported tools that can interpret large data sets and advise medical staff. Provide patients with 24/7 access to trained generative AI chatbots. 

Manufacturing and product design 

Generate concept designs for new products and research the latest trends in specific industries. Utilize AI to conduct a series of simulations to run tests and optimize final products.

Human resources 

Automate initial candidate screening, identify top talent and streamline the recruitment process. Assist in employee training and development by providing personalized learning materials and resources.

Technology Stack

Other AI services

Generative AI solutions are not the only AI and ML-related services Software Mind provides. Here are additional AI-related fields we specialize in.

01

Cloud-based AI/ML

Use machine learning and artificial intelligence to effectively address different challenges, such as cost reduction and process automation.

Cloud-based AI/ML

01

Cloud-based AI/ML

Use machine learning and artificial intelligence to effectively address different challenges, such as cost reduction and process automation.

Insights about AI and LLMs

Learn from our experts’ insightful content regarding generative AI solutions:

Tailored generative AI solutions

1500

+ experts

25

+ years of innovation

10

+ R&D labs around the world

Frequently asked questions (FAQ)

What is generative AI?

Generative AI is an area of AI that concentrates on generating new content like images, text, music, or videos. It uses advanced algorithms that are often based on deep learning models to analyze existing data and create original, realistic outputs. Generative AI enables machines to create innovative and imaginative content that is often difficult to distinguish from human-made work. In the coming years, it will have a significant impact on numerous industries and fields.

Looking for other services?

For over two decades we’ve been helping companies across markets and sectors develop disruptive solutions. Proven ways of working, domain knowledge and an open culture that prioritizes ownership mean we contribute from day one.

Engineering and consultancy that deliver value

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Engineer customized solutions that increase personalization and user conversion across channels.

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Work with experienced engineering teams to create evolutive solutions for your customers.

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