ISO 27001 & SOC2 Certified Company SINCE 2012

Choosing the right generative AI development partner is critical because GenAI projects require specialized expertise that goes beyond traditional software development. Here are the key factors to evaluate:

• LLM expertise: Do they have hands-on experience with multiple foundation models (GPT, Claude, LLaMA, Gemini, Mistral)? Model-agnostic partners avoid vendor lock-in.
• Production track record: Ask for case studies of GenAI applications running in production — not just prototypes. Proof-of-concepts are easy; production deployment is where most fail.
• Data security: Ensure they have ISO 27001 or SOC2 certifications. Your business data will flow through their systems during model training.
• RAG and fine-tuning capabilities: The best GenAI apps are grounded in your data. Your partner should have deep experience building RAG pipelines and fine-tuning models.
• Post-launch support: GenAI models degrade over time. Your partner should offer MLOps, monitoring, and continuous optimization — not just build-and-walk-away.

At DreamzTech, we check every box: 200+ AI projects, ISO 27001 + SOC2 certified, and SLA-based support with dedicated GenAI teams.

Generative AI has gone from a research curiosity to the fastest-adopted technology in history. ChatGPT reached 100 million users in just 2 months — faster than any product before it. The enterprise generative AI market is projected to reach $137 billion by 2028.

What is driving this growth? Three converging factors:

• Foundation model maturity: GPT-4, Claude 3.5, LLaMA 3, and Gemini deliver enterprise-grade accuracy for text, code, and multimodal tasks.
• RAG architecture: Retrieval-augmented generation solves the hallucination problem by grounding AI outputs in verified enterprise data.
• Cost economics: Model inference costs have dropped 90% since 2023, making production GenAI economically viable for businesses of all sizes.

Companies that delay GenAI adoption risk falling behind competitors who are already automating content creation, customer support, code generation, and data analysis with generative AI.

Generative AI development costs depend on the complexity of your application, the models used, and whether you need custom fine-tuning or RAG architecture.

Typical ranges:

• GenAI chatbot or assistant: $30,000–$80,000 for a production-ready conversational AI with RAG pipeline and enterprise integration.
• Content generation platform: $50,000–$150,000 depending on multi-format support, brand voice training, and workflow automation.
• Enterprise GenAI platform: $150,000–$500,000+ for multi-agent systems, custom model training, and full MLOps infrastructure.

At DreamzTech, we start with a free consultation to scope your project and provide a transparent estimate. We offer fixed-price, T&M, and dedicated team models.

Traditional AI focuses on analyzing data and making predictions — classification, regression, anomaly detection. It answers “what is this?” or “what will happen next?”

Generative AI creates new content — text, images, code, music, video. It answers “what should I write?” or “how do I respond?” This creative capability makes GenAI uniquely suited for:

• Customer support automation (conversational, context-aware responses)
• Content creation at scale (marketing, documentation, reports)
• Code generation and developer productivity tools
• Document analysis, summarization, and extraction
• Personalized recommendations and communications

In practice, the best enterprise AI solutions combine both: traditional ML for prediction and classification, and generative AI for creation and interaction.

Timeline depends on the type of GenAI application:

• GenAI chatbot with RAG: 4–8 weeks for a production-ready assistant with knowledge base integration and enterprise deployment.
• Content generation platform: 6–12 weeks including brand voice training, multi-format support, and workflow integration.
• Custom LLM fine-tuning: 8–16 weeks including data preparation, training, evaluation, and production deployment.
• Enterprise GenAI platform: 3–6 months for multi-agent systems with full MLOps, monitoring, and organizational rollout.

The biggest variable is data readiness. If your data is accessible and well-structured, we move fast. We include data preparation in our project plans so there are no surprises.

Yes — and this is where most of our projects start. You do not need to rebuild your stack to benefit from generative AI.

Common integration scenarios we deliver:

• Adding a GenAI assistant to your CRM (Salesforce, HubSpot) for automated responses
• Building document processing into existing workflows (insurance, legal, finance)
• Embedding AI-powered search across internal knowledge bases and documentation
• Adding code generation tools to your development pipeline
• Connecting GenAI to ERP systems for automated reporting and analysis

We use APIs and microservices so the GenAI layer works alongside your current systems without disrupting daily operations.

We work across the full generative AI stack:

• Foundation Models: OpenAI GPT-4o, Anthropic Claude, Meta LLaMA 3, Google Gemini, Mistral, and open-source alternatives.
• Orchestration: LangChain, LlamaIndex, LangGraph, CrewAI, and custom frameworks.
• Vector Databases: Pinecone, Weaviate, Milvus, ChromaDB, and pgvector.
• Cloud Platforms: AWS Bedrock, Azure OpenAI, Google Vertex AI, and Databricks.
• Image Generation: Stable Diffusion, DALL-E, Midjourney API, and custom diffusion models.
• MLOps: MLflow, Weights & Biases, and custom CI/CD for LLM deployment.

We are model-agnostic. We select the technology that best fits your use case, budget, and data privacy requirements — not the one that is trending.