AI App Development Company in Bangalore and USA

An AI-powered MVP with pre-trained model integration (e.g., OpenAI GPT API) can be delivered in 6 to 10 weeks. A fully custom AI application with proprietary ML models, data pipelines, and enterprise integrations typically takes 3 to 8 months. We follow agile methodology with iterative model training and validation cycles to ensure continuous improvement.

Yes. We specialize in integrating OpenAI GPT-4, GPT-4o, Azure OpenAI, and other LLM APIs into existing mobile and web applications. This includes conversational AI chatbots, content generation, semantic search, document summarization, and intelligent assistants with custom prompt engineering, guardrails, and fine-tuning on your domain data.

Yes. Our data science team builds custom ML models for classification, regression, anomaly detection, recommendation systems, demand forecasting, image recognition, and more using TensorFlow, PyTorch, and scikit-learn. We handle the full ML lifecycle from data preparation and feature engineering to model training, validation, and production deployment with MLOps automation.

We serve 30+ industries including healthcare (medical imaging, diagnostics), fintech (fraud detection, risk scoring), retail and eCommerce (recommendations, demand forecasting), logistics (route optimization, predictive maintenance), education (adaptive learning, auto-grading), manufacturing, real estate, insurance, and professional services.

We implement strict data privacy measures including data anonymization, encryption at rest and in transit, role-based access controls, GDPR and HIPAA compliance frameworks, secure model serving, and regular security audits. For sensitive industries, we deploy on-premise or private cloud AI infrastructure to ensure complete data sovereignty and regulatory compliance.

Absolutely. We fine-tune foundation models like GPT, LLaMA, Mistral, and domain-specific models on your proprietary datasets using techniques including LoRA, QLoRA, and RLHF. This ensures the AI understands your domain terminology, business rules, and delivers highly accurate, contextually relevant outputs while maintaining data security.

Yes. We set up comprehensive MLOps pipelines including automated model retraining, A/B testing, performance monitoring, data drift detection, model versioning, feature stores, and alerting systems using MLflow, Kubeflow, and cloud-native ML platforms. Our post-deployment support ensures your AI models maintain accuracy and performance as data patterns evolve.

We work with TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LangChain, LlamaIndex, OpenAI API, Azure AI Services, AWS SageMaker, Google Vertex AI, Pinecone, ChromaDB, FastAPI, and MLflow. Our team selects the optimal stack based on your specific AI use case, scalability needs, and deployment environment.

Yes. While headquartered in Bangalore, India, we actively serve AI clients across the United States, United Kingdom, Middle East, and Australia. We offer flexible engagement models including dedicated AI teams, fixed-price projects, and time-and-materials contracts. Our teams work with 4 to 6 hours of overlapping time zones and communicate seamlessly via Slack, Jira, and daily video standups.