AI App Development — Intelligent Products That Scale

We build AI-powered chatbots and virtual assistants, intelligent document processing systems, recommendation engines, content generation tools, RAG (Retrieval-Augmented Generation) systems, semantic search, automated classification and tagging, and custom AI features integrated into existing web and mobile products.

We work with OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), open-source models (Llama, Mistral, Mixtral), and embedding models (OpenAI Ada, Cohere). We choose the optimal model based on your accuracy requirements, latency constraints, cost budget, and data privacy needs.

RAG (Retrieval-Augmented Generation) is a technique where an AI model answers questions by first retrieving relevant information from your documents and data, then generating a response based on that context. Use RAG when you need an AI that knows about your specific business — internal knowledge bases, product documentation, legal documents, or customer data.

A simple AI chatbot or feature integration takes 4–6 weeks. A RAG system with custom data pipeline takes 6–10 weeks. A complex AI product with custom model training takes 3–6 months. We recommend starting with a focused proof-of-concept to validate the AI approach before scaling.

A basic AI integration (chatbot, simple classification) costs $10,000–$25,000. A custom RAG system ranges from $25,000–$60,000. Complex AI products with fine-tuning and custom pipelines cost $50,000+. Ongoing costs include API usage fees ($50–$5,000/month depending on model and volume) and infrastructure.

Yes. We frequently integrate AI capabilities into existing web and mobile applications. Common additions include intelligent search, automated customer support, content summarization, personalized recommendations, document extraction, and predictive analytics. We design the integration to feel native to your existing product experience.

We use multiple techniques: RAG to ground responses in real data, structured output schemas to enforce format consistency, prompt engineering with guardrails and system instructions, temperature tuning for deterministic outputs, fact-checking pipelines for critical information, and human-in-the-loop review for high-stakes use cases.

GPT-4 and Claude offer the best quality and are easiest to deploy (API-based, no infrastructure needed). Open-source models (Llama, Mistral) provide full data sovereignty and lower cost at very high volume, but require self-hosting GPU infrastructure. For most products, we recommend starting with GPT-4 or Claude and exploring open-source only if you have strict data privacy requirements or very high volume.

We take data privacy seriously. Options include: using API providers with data processing agreements (DPAs) that guarantee no training on your data, self-hosting open-source models for complete data sovereignty, implementing PII redaction before sending data to AI models, and on-premise deployment for regulated industries. We help you choose the right approach for your compliance requirements.

AI can handle 60–80% of routine customer inquiries (FAQs, order status, basic troubleshooting), reducing support costs significantly. However, complex issues, emotional situations, and edge cases still benefit from human agents. We recommend a hybrid approach: AI handles tier-1 support with seamless escalation to humans when needed.

We track metrics including response accuracy (human evaluation and automated scoring), response latency, user satisfaction ratings, task completion rates, escalation rates (for support bots), cost per query, and retrieval precision/recall for RAG systems. We set up monitoring dashboards and continuous evaluation pipelines to catch quality degradation early.

For API-based models (GPT-4, Claude): minimal — just your application server and a vector database (Pinecone, Weaviate) for RAG. For self-hosted models: GPU servers (A100 or H100), model serving infrastructure (vLLM, TGI), and MLOps tools. We handle the complete infrastructure setup and can advise on the most cost-effective architecture for your scale.