Artificial Intelligence Services
Expand your digital horizon with AI/ML technology
Let’s talk technology!Artificial Intelligence has transformed the way we work, turning into a trend that is here to stay. Companies can easily integrate AI/ML into their products either for personalized recommendations or product modernization, or even enhancing customer experience.
Nitor Infotech helps companies derive value by leveraging Artificial Intelligence and machine learning to continuously improve software products, reinvent, and accelerate innovation.
Our Offerings
Companies adopting machine learning technology and AI are set to achieve tremendous growth. Nitor Infotech is excited to leverage the massive potential of AI/ML for your products, solutions, and services that will transform the way your business works. Here’s a list of some of our key offerings in the realm of AI/ML:
Generative AI
Leverage LLMs
Use deep learning techniques to seamlessly integrate into product development.
AI/ML Chatbots
Utilize Conversational AI
Utilize advanced AI for human-like interactions with a virtual assistant.
Data Engineering
Discover patterns
Ingest, process, and analyze vast quantities of data through data extraction, management, cleansing, transformation, and visualization.
MLOps
Streamline model lifecycle
Simplify development and deployment through experiment tracking and optimization, model re-training, and performance monitoring tools.
Computer Vision
Derive meaningful information
Enhance the AI edge of products through functions like object detection and object recognition.
Recommender systems
Target recommendations
Use ML techniques for targeting recommendations and personalizing user experiences.
ML Model Development
Build faster
Create and deploy models easily to empower data scientists and developers.
Natural Language Processing
Understand and Analyze
Apply ML to process text and speech for business use.
Our Approach
Our Artificial Intelligence and Machine Learning services, help build game changing products in the fastest possible time with the best available AI developers and data scientists. Product teams thus can drive maximum value from AI at every step of their AI/ML adoption journey. This is how we approach each project and deliver more intelligent apps:
Idea and Scope
We focus on defining the problem, researching on end-user identification, and outlining scenarios.
MVP Design
We identify essential MVP features, allocate teams with specialized roles and responsibilities, and define critical milestones to plan and map out the project.
Technology Decision
Depending on the product we research on the models best suited for it (Service-based language model vs Open Source) and then finalize the tech stack.
Maintenance & Evaluation
We monitor tool implementation and continuously optimize language models. We gather and deliver usage and benefit matrices to help our clients quantify both the tangible and intangible benefits of the project.
Deployment
We levarage IaaS tools like Terraform, validate through validation tools like AWS config, launch the MVP, and continuously monitor through tools like AWS Cloudwatch or similar tools.
Development
Here we fine tune the LLM model, validate it, iteratively test and refine prompts through Prompt engineering, implement model APIs, and test the integration points
Ready to build intelligent products? Leverage AI to airlift your products!
Let's Talk AI!Insights
Questions on your mind
What is Artificial Intelligence technology?
Artificial Intelligence technology is leading the way for the next industrial revolution. With the ever-increasing amount of data being produced by IT systems, IoT devices as well as user devices, traditional methods of managing structured and unstructured data are proving to be inadequate. To replace it by leveraging the immense computational abilities of a machine, a widespread, Artificial Intelligence (AI)-driven boom in growth is restructuring businesses as we know it today.
While early adopters have already reaped the benefits of AI and ML technology, the rest of the economy is not far behind; according to The Economist, 75% of executives believe that organizations will actively implement AI and machine learning (ML) within the next 3 years.
According to a forecast, the global AI market is expected to grow to about a $126 Billion industry by 2025. The mushrooming of artificial intelligence companies all around the world makes this paradigm shift in the business world fairly evident.
How we do it?
Our teams continuously collaborate to ensure that the core data science and data engineering work is conducted smoothly and completed in parallel. With this coordinated, iterative approach, we ensure that all the important project tasks are completed laterally resulting in shorter development cycles, reduced time to market, and lower production costs.
Our sophisticated cognitive engineering techniques fuse AI and ML with specific business needs to deliver clear, concise, and measurable results. Our Agile approach saves costs with the concurrent deployment of four core teams.
Why Nitor Infotech for Artificial Intelligence services?
At Nitor Infotech we craft our offerings by applying the immense power of Artificial Intelligence (AI) for the growth of your business. We use the latest advances in AI to meticulously plan, strategize, and execute customized business solutions. Nitor Infotech’s experts have expansive skillsets comprising of IoT, NLP, Predictive Analytics, Big Data, and more that can springboard your business results.
- Agile approach : Non-sequential approach that saves time and costs with the concurrent deployment and continuous interaction of data engineers, data scientists, and domain SMEs
- Leading talent: Extensive expertise of the Data Science team reinforced by specialists in computer science and mathematics
- Comprehensive data expertise: Assured ability to work with varied data types, including relational, text, hypertext, as well as semi-structured audio and images for audio-signal processing, computer vision, and deep learning
- Progressive skillset: Hands-on experience with frameworks such as TensorFlow and scikit-learn