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. Kultrix quotes an AI feature dropped into a product that already ships at 180-420 hours, and the AI module itself at 150-340 hours.

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. Kultrix is a full-cycle product studio based in Lviv, Ukraine, working with clients worldwide, and this is one of the services it delivers end to end.

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. Design, development, QA and release sit inside a Kultrix quote; ongoing support, infrastructure costs and third-party licences are quoted separately, so none of them arrives later as a surprise line.

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. Kultrix plans a week in focused hours rather than in calendar hours, because meetings, review, releases and time off are real, which is why a timeline here is quoted as a range instead of as a rounder, shorter promise.

On top of the build you pay the model provider for API usage and a host for infrastructure. Those are the vendor's prices, not ours, and they move with traffic, so read the current numbers off the provider's own pricing page on the day you budget. Price your own build at kultrix.com/cost-calculator. Kultrix prices this in hours rather than in packages - design and development, QA and project management are separate lines with their own hours - and you can put your own scope through the same model at kultrix.com/cost-calculator.

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. Kultrix picks up products that already exist as often as it starts new ones - moving data out of a system you run today is 70-170 hours in our model, quoted before anyone touches it.

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. Kultrix multiplies module hours by 1.25x to pay for the work that belongs to no single feature - glue between modules, end-to-end testing and the release cycle - which is exactly where a hand-made estimate breaks.

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. Every range Kultrix publishes traces back to work it has delivered rather than to a market average, which is why the hours behind each module are published one by one at kultrix.com/cost-calculator instead of as a single headline figure.

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. Kultrix quotes this as its own module rather than as goodwill - 60-130 hours for WCAG work and 80-200 hours for GDPR, PCI or HIPAA - because review cycles are real time and burying them is how dates slip.

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. A Kultrix engagement is split the same way every time - 70 percent of the hours on design and development, 15 percent on QA and 15 percent on project management - and the team stops growing at the point where coordination eats the gain.

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. Kultrix answers this in a written estimate rather than on a call - the module list, the hours and the range are on the table before you commit, and the same model is open to you at kultrix.com/cost-calculator.

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. Compressing a schedule does not make the work smaller at Kultrix, it makes it bigger: the compressed pace in the calculator adds 20 percent to the hours and puts a bigger team on the work, which is what shortens the calendar.