ISO 27001 & SOC2 Certified Company SINCE 2012

AI software development costs depend on the scope of your project, the complexity of the AI models involved, and whether you need a full product build or AI integration into existing systems. Simple AI features like a chatbot or recommendation engine can start in the range of $25,000–$75,000. Full AI-powered platforms with custom model training, data pipelines, and production infrastructure typically range from $100,000 to $500,000 or more.

At Dreamztech, we start every engagement with a free consultation to understand your goals and provide a transparent estimate. We offer flexible engagement models — fixed-price, time-and-materials, or dedicated teams — so you can choose the structure that fits your budget and timeline.

Traditional software development follows rules you define. The software does exactly what you program it to do. AI development is different because the software learns from data and makes predictions or decisions on its own.

AI development requires additional steps that traditional projects do not: data collection and preparation, model training and evaluation, bias testing, and ongoing model monitoring after launch. It also requires specialized talent — data scientists, ML engineers, and MLOps specialists — alongside your standard development team. We bring both disciplines together under one roof so you do not need to manage multiple vendors.

A working AI prototype or MVP typically takes 4 to 8 weeks. A production-ready AI application with full integration, testing, and deployment usually takes 3 to 6 months. Highly complex projects — like enterprise-wide AI platforms with multiple models and deep system integrations — can take 6 to 12 months.

The biggest factor in timeline is data readiness. If your data is clean, accessible, and well-structured, we move fast. If data preparation is needed, we build that into the project plan upfront so there are no surprises.

AI delivers strong results across many industries, but we see the highest impact in these sectors:

• Financial Services: Fraud detection, risk scoring, automated underwriting, and claims processing.
• Healthcare: Clinical decision support, patient triage, medical image analysis, and drug discovery.
• E-commerce & Retail: Personalized recommendations, demand forecasting, dynamic pricing, and visual search.
• SaaS: Intelligent automation, natural language interfaces, predictive analytics, and churn prediction.
• Logistics & Supply Chain: Route optimization, warehouse automation, demand planning, and predictive maintenance.

If your business runs on data, AI can make it faster, smarter, and more profitable.

Quality and security are built into every phase of our process, not bolted on at the end.

• Certifications: We are ISO 9001:2015 and SOC 2 Type II certified. Enterprise security is standard, not optional.
• Development practices: Code reviews, automated testing, CI/CD pipelines, and static code analysis on every project.
• AI-specific quality: Model validation, bias testing, explainability audits, and performance benchmarking before any model goes to production.
• Data security: Encryption at rest and in transit, role-based access control, and compliance with GDPR, HIPAA, and other regulatory frameworks as needed.
• Post-launch: Model monitoring, drift detection, and automated retraining pipelines to keep your AI accurate over time.

Yes. In fact, most of our projects involve adding AI capabilities to systems that are already in production. You do not need to rebuild your entire stack to benefit from AI.

We specialize in integrating AI into existing ERPs (SAP, Oracle), CRMs (Salesforce, HubSpot), data platforms, and custom-built software. Our approach uses APIs and microservices so the AI layer works alongside your current systems without disrupting daily operations.

Common integration projects include adding predictive analytics to dashboards, building intelligent document processing into workflows, connecting AI chatbots to customer service platforms, and embedding recommendation engines into e-commerce sites.

We work across the full AI technology stack, including:

• Large Language Models (LLMs): OpenAI GPT, Anthropic Claude, Meta LLaMA, Google Gemini, Mistral, and open-source models.
• Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, and Hugging Face Transformers.
• Cloud AI Platforms: AWS SageMaker, Azure AI, Google Vertex AI, and Databricks.
• GenAI Tools: LangChain, LlamaIndex, vector databases (Pinecone, Weaviate, Milvus), and RAG architectures.
• MLOps: MLflow, Kubeflow, Weights & Biases, and custom CI/CD pipelines for model deployment.
• Computer Vision: OpenCV, YOLO, and custom CNN architectures.
• NLP: Named entity recognition, sentiment analysis, text classification, and conversational AI.

We are technology-agnostic. We choose the tools that best fit your use case, not the ones that are trending.