Question 1

What are generative AI solutions?

Accepted Answer

Generative AI solutions are systems that use large language models (LLMs) and other generative models to produce text, code, images, or structured data. Business applications include intelligent assistants, automated content generation, document summarization, code generation, and conversational interfaces built on models like GPT-4, Claude, Gemini, or custom fine-tuned LLMs.

Question 2

What is the difference between RAG and fine-tuning an LLM?

Accepted Answer

RAG (Retrieval-Augmented Generation) connects a language model to your own data at query time, ideal for knowledge bases, documentation search, and up-to-date information. Fine-tuning trains the model on your data to adjust its behavior, tone, or domain expertise. We help you choose the right approach or combine both based on your use case and data.

Question 3

Can you integrate ChatGPT or OpenAI into our existing product?

Accepted Answer

Yes. We integrate OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and other LLM APIs into existing web, mobile, and enterprise applications, adding features like chat interfaces, document Q&A, automated workflows, and intelligent search without rebuilding your product.

Question 4

Do you build custom AI assistants and chatbots?

Accepted Answer

Yes. We design and build domain-specific AI assistants grounded in your business data, with custom personas, tool use, memory, and multi-turn conversation, deployed as web widgets, API services, or embedded in your existing platforms.

Question 5

How do you ensure the AI only uses our company data?

Accepted Answer

We implement RAG pipelines with your private knowledge base, apply strict retrieval filters, and configure the LLM to stay grounded in retrieved context. We also add guardrails, output validation, and logging to prevent hallucinations and ensure responses stay within your defined scope.

Question 6

What industries do you build generative AI solutions for?

Accepted Answer

We have built generative AI solutions for healthcare, legal, education, e-commerce, SaaS, fintech, and enterprise, covering use cases like clinical documentation, contract analysis, personalized learning, product descriptions, customer support automation, and internal knowledge management.

Question 7

How long does a generative AI project take to build?

Accepted Answer

A focused generative AI integration, such as a RAG-powered assistant or ChatGPT-based feature, typically takes 4-10 weeks from discovery to production. More complex systems with custom fine-tuning, multi-agent workflows, or deep integrations can take 3-6 months. We provide a clear timeline after scoping.

Generative AI Solutions

Unlock Innovation With Custom Generative AI Solutions for Your Business