Generative AI Development Company in Bangalore and USA

Generative AI development costs vary based on the complexity of LLM integration, fine-tuning requirements, number of use cases, and deployment environment. A simple GPT-powered feature integration may start from $10,000, while enterprise-grade generative AI platforms with custom fine-tuned models, multi-modal capabilities, and production guardrails can range between $25,000 and $150,000+. Contact Cipher Trivia for a detailed, no-obligation proposal.

We work with all major LLMs including OpenAI GPT-4o, GPT-4 Turbo, Anthropic Claude 3.5, Google Gemini Pro, Meta LLaMA 3, Mistral, Cohere Command, and open-source models from Hugging Face. We also integrate image generation models like DALL-E 3, Stable Diffusion, and Midjourney APIs. We select the optimal model based on your accuracy needs, latency requirements, cost budget, and data privacy constraints.

Yes. We fine-tune foundation models on your proprietary datasets using techniques including LoRA, QLoRA, full fine-tuning, DPO, and RLHF. This ensures the model understands your domain terminology, writing style, business rules, and delivers highly accurate, contextually relevant outputs. We handle the full pipeline from dataset preparation and training to evaluation and production deployment.

Prompt engineering is the systematic design and optimization of instructions given to LLMs to produce desired outputs consistently. It includes techniques like few-shot prompting, chain-of-thought reasoning, system prompts, output formatting, and guardrails. Expert prompt engineering can improve LLM output quality by 40-60% without any model fine-tuning, making it the most cost-effective way to improve generative AI performance.

We implement multiple anti-hallucination strategies including Retrieval-Augmented Generation (RAG) to ground responses in factual data, output validation layers, confidence scoring, source citation requirements, structured output schemas, and human-in-the-loop verification workflows. For enterprise deployments, we combine these with guardrail frameworks like NeMo Guardrails and custom validation pipelines.

Absolutely. We specialize in embedding generative AI capabilities into existing web and mobile applications via REST APIs, SDKs, and microservices. This includes adding AI-powered content generation, chatbots, code assistants, document summarization, semantic search, and intelligent automation features without disrupting your existing architecture or user experience.

A generative AI feature integration using pre-trained models (e.g., GPT-4o API) can be delivered in 4 to 8 weeks. A comprehensive generative AI platform with custom fine-tuned models, multi-modal capabilities, RAG pipelines, and enterprise guardrails typically takes 3 to 6 months. We follow agile methodology with iterative prompt optimization and model evaluation cycles.

We implement strict data privacy measures including Azure OpenAI and AWS Bedrock for enterprise-grade data isolation, on-premise model deployment for sensitive industries, PII redaction before LLM processing, data encryption at rest and in transit, role-based access controls, and compliance with GDPR, HIPAA, and SOC 2 frameworks. Your data is never used for model training without explicit consent.

Yes. We provide comprehensive post-deployment support including prompt optimization, model version upgrades (e.g., migrating from GPT-4 to GPT-4o), cost monitoring and optimization, output quality dashboards, guardrail tuning, new use case development, and A/B testing of different model configurations. Our generative AI systems are designed for continuous improvement as models and your business needs evolve.