Artificial Intelligence Services

Reinforce Your Digital Ecosystem with AI

As the leading AI development company, we rely on five interoperable, operational pillars to drive AI transformation at scale.

Reinforce Your Digital Ecosystem with AI

Our Artificial Intelligence Services

As a leading AI software company, we help companies implement AI into business processes strategically and effectively.

Artificial Intelligence Consulting

Get expert guidance to implement tailored AI-driven initiatives.

AI Development

Unlock the power of AI-infused development and supercharge productivity.

AI Design

Deliver stunning experiences with ultra-realistic visuals and interfaces.

AI Support & Maintenance

Ensure optimal functioning and performance of your custom AI systems.

AIOps / MLOps

Disrupt the DevOps pipeline with AI and improve agility, efficiency, and reliability.

AI Security & Compliance

Design interpretable AI systems that prioritize data protection, privacy, and ethics.

COMPUTER VISION

Image recognition 

Object tracking

Face recognition

Video analytics

Video recognition

Object detection

AI CONSULTATION

Enterprise AI Development

AI as a Service

Infrastructure Support

Natural Language Processing

Research & Development

MACHINE LEARNING CONSULTING

Deep Learning

Supervised/Unsupervised Learning

Model Fine Tuning

Feature Engineering

Model Engineering

CONVERSATIONAL AI

Chat GPT-4 integrations

Prompt Engineers

AI Chatbot Development

Voice Interfaces and Speech Recognition

Contextual Understanding

Model Training and Optimization

ANALYTICS

Multimedia Analytics

Predictive analytics

Pattern recognition

Cluster analysis

Information retrieval

Strategy Development

GEN AI

Generative AI integrations

Generative AI development

Generative AI consulting

Cognitive Computing

Emotional Intelligence

Industry-Specific Gen AI Solutions

Trusted by Key Industry Players to Drive AI Transformation

Our AI Expertise

At Mobisoft, we recognize unique AI transformation needs and persistent silos of diverse business areas. Our mission is to assist you in accelerating your smart growth journey.

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AI Strategy Consulting

Implement AI in your core business functions with expert advice.

Computer Vision Solution

Detect and identify objects, and classify images for quick decision-making.

Neural Networks for Custom Model

Train AI models on your diverse business data and customize LLMs.

Intelligent Automation Solutions

Incorporate automated processes and workflows using RPA and AI technologies.

Predictive Modeling

Use AI to forecast trends, behaviors, and outcomes based on data analysis.

Generative AI Integrations

Use AI-generated content or data for innovative applications.

Custom AI Development

Build custom AI solutions aligned with your objectives and operational requirements.

Machine LearningModels

Develop custom ML models for predictive analytics and pattern recognition.

Natural Language Processing (NLP)

Maximize the use of your data with NLP and deliver human-centric experiences.

AI Chatbots and Virtual Assistants

Create smart conversational AI interfaces for improved service and support.

Recommendation Engines

Deliver personalized content or product suggestions based on user behavior.

AI Domains We Excel In

Helping you upgrade core operations with greater flexibility by leveraging advanced AI tools.

Data Science

  • Python
  • Spark
  • Glue
  • EMR
  • BigQuery

Business Intelligence

  • Data Studio
  • Power BI
  • Tableau
  • Google Analytics

Natural Lang Processing

  • Dialog Flow
  • Lex
  • PyTorch
  • spaCy

AI/ML Frameworks

  • PyTorch
  • Tensorflow
  • SageMaker
  • Scikit-learn

Data Сapture / OCR

  • Tesseract
  • Amazon Textract
  • Google Cloud Vision API
  • Microsoft Azure OCR

Algorithms

  • Clustering
  • Metric Learning
  • Supervised/Unsupervised Learning

Cloud Services

  • Amazon Web
  • Services
  • Microsoft Azure
  • Google Cloud
  • Platform

Database

  • PostgreSQL
  • MongoDB
  • MySQL

DevOps

  • Git
  • Docker
  • CI/CD tools

Front End

  • TS
  • Angular
  • JavaScript
  • Next

Back End

  • Node
  • Python (Django Flask)

Embedding Models

  • OpenAI

Large Language Models

  • GPT-4
  • GPT-3.5 (OpenAI)
  • PaLM 2 (Google)
  • Claude 2 (Anthropic)
  • Llama 2 (Meta)
  • DALL.E
  • Whisper
  • Embeddings
  • Moderation
  • Stable Diffusion
  • Google Bard
  • VIcuna
  • Generative Adversarial Networks

Neural Networks

  • CNN
  • RNN
  • Representation Learning
  • Manifold Learning
  • Variational Autoencoders
  • Bayesian Network
  • Autoregressive Networks

Image Classification Models

  • Yolo

Augment Your Team Capabilities with AI Experts

Bring diverse tech expertise, efficiency, and innovation to your development operations with the help of our AI professionals.

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Success Stories

Learn how we helped conglomerates, enterprises, and visionary startups bring the power of AI into their workflows, products, and services.

eCommerce App - Recommendation Systems

Enhanced app experience of a major retail chain with AI-powered personalized suggestions.

eCommerce App - Description Generation using GenAI

Helped a large retail store network in the USA to unleash the power of AI-generated product descriptions.

Self-Serve Analytics

Empowered the team of a global brand to simplify data queries using LLMs to Query for quick decisions.

Vehicle Damage Detection

Redefined vehicle maintenance and damage detection with precision using an AI-enabled smart system.

See AI in Action Here

Add AI Capabilities to Your Operations Like Never Before

Being a trusted Artificial Intelligence company, we follow a precise process to make your business operations smart and secure.

STEP 01

Ideate

Set your AI goals and address obstacles to leverage its potential.

STEP 02

Assess

Use a framework to assess “achievability” and ROI of AI use cases.

STEP 03

Risk Identification

Evaluate the robustness of AI models and perform a risk analysis.

STEP 04

Select

Pick your process-related use cases and select carefully.

STEP 05

Implement

Identify the resources, timelines, and estimated costs associated with AI implementation.

Simplify AI Adoption with Mobisoft’s AI Services

Schedule A Free Consultation Today!

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Frequently Asked Questions

Around Our Artificial Intelligence Services

Being a leading AI development company, we are proficient in a variety of programming languages and frameworks commonly used in our AI development services such as Python, Tensorflow, PyTorch, and more. Please visit "AI Techstack" for more info.

Sure. Our process typically involves the following to ensure the quality and relevance of the data used in data science and AI solutions:

  • Data cleaning
  • Normalization and scaling
  • Encoding categorical variables
  • Feature engineering

This meticulous process ensures that the data fed into our AI models is well-prepared for effective training and prediction.

Our approach to model selection and optimization is tailored to the unique characteristics of each use case. However, we follow a systematic process as mentioned hereunder:

  • Understand specific use case needs
  • Choose the appropriate algorithm or model architecture
  • Iteratively adjust model parameters
  • Cross-validation of AI models on various subsets of data

Yes, we have successfully integrated AI into similar systems, overcoming challenges such as data compatibility, system integration complexities, and ensuring seamless collaboration with existing software infrastructure. As a leading Artificial Intelligence development company, we are well-equipped to overcome these challenges and provide end-to-end AI integration services for various use cases.

The techniques we use depend on the use case and include distributed processing, data partitioning, and sharding, as well as cloud-based storage. By employing these techniques, we ensure that our AI applications can efficiently handle and process large volumes of data, facilitating scalable and high-performance solutions.

At Mobisoft, our approach to building and training neural networks is characterized by a combination of AI expertise and best practices. This includes careful selection or customization of neural network architectures, data augmentation, transfer learning, regularization techniques, and optimization algorithms. This comprehensive strategy ensures the effectiveness and optimal performance of our neural networks.

We achieve seamless integration by developing APIs, adopting a microservices architecture, utilizing containerization technologies, and conducting thorough compatibility testing. This approach ensures the smooth incorporation of AI-driven business solutions into existing workflows and infrastructure, minimizing disruptions.

To overcome challenges related to data quality and availability, we implement robust data preprocessing techniques, conduct thorough exploratory data analysis, and, when necessary, leverage data imputation methods. This ensures that our models are trained on high-quality, reliable datasets.

Being a trusted Artificial Intelligence development company in the USA and India, we prioritize compliance by adhering to regulatory standards and industry best practices in AI development. Our technique involves thorough documentation, regular audits, and the implementation of ethical guidelines. We also ensure that our models align with data privacy regulations and industry-specific standards, fostering trust and accountability in our AI solutions.

Yes, testing Generative AI applications requires specialized considerations. We focus on assessing the diversity and quality of generated outputs, conducting stress testing to evaluate model robustness, and implementing validation techniques specific to the generative nature of the models. Additionally, we employ domain-specific evaluation metrics to ensure the effectiveness and reliability of Generative AI applications in diverse scenarios.

We prioritize ethical considerations and bias mitigation in Generative AI models by implementing fairness-aware training, diversifying training datasets, and conducting thorough bias assessments. Additionally, we follow ethical guidelines and stay informed about emerging best practices to ensure responsible and unbiased AI development.

In previous projects, we have implemented strategies like balanced dataset sampling, fairness-aware training, and bias assessments using metrics like disparate impact analysis. These measures ensure a proactive approach to mitigating bias in AI models, promoting fairness and inclusivity.

Generative AI models thrive on diverse datasets, including but not limited to images, text, audio, and video. The suitability depends on the specific task; for instance, images for GANs (Generative Adversarial Networks), text for language models, and sequences for sequence-to-sequence generative models.

Yes, Generative AI models can be seamlessly integrated into existing software systems. We achieve this through APIs, microservices, or containerization, ensuring compatibility with various software architectures. This facilitates the incorporation of Generative AI capabilities without significant disruption to existing systems.

Privacy is a crucial consideration, and we address concerns by implementing privacy-preserving techniques such as differential privacy, anonymization, and secure data handling. Additionally, we adhere to data protection regulations and industry-specific privacy standards to ensure the responsible and secure use of Generative AI models.

The expected cost varies based on project complexity, data requirements, and deployment scale. We provide detailed cost estimates that encompass development, deployment, and ongoing maintenance, ensuring transparency and alignment with client budget considerations. Contact us to know your AI implementation cost.

The 10-20-70 rule in AI refers to a general guideline for allocating resources during the development of AI models. The rule suggests that in a typical AI project:

  • 10% of effort is dedicated to Problem Definition and Data Collection
  • 20% of effort is allocated to AI Model Development and Training
  • 70% of effort is focused on Deployment, Monitoring, and Iterative Improvement