Adding AI to existing software, such as an assistant that answers from your documents, document analysis or smart search, has become one of the most requested software projects. This guide explains what AI integration costs, what you pay every month after launch, and how to start small.
Short answer: adding AI features to an existing app typically costs around $15,000–$80,000 to build, depending on the use case. An LLM assistant that answers from your own documents usually costs $20,000–$60,000, and a custom AI assistant that connects to your systems and takes actions $60,000–$150,000+. On top of the build, budget for monthly AI model usage, hosting and maintenance. Three Circle rates start from $35 per hour, and a first AI feature can start as an MVP.
AI integration cost by type
| Type of AI integration | What it does | Typical build cost | Typical timeline |
|---|---|---|---|
| AI feature in an existing app | Summaries, smart search, classification or recommendations added to your product | $15,000–$80,000 | 1–3 months |
| Assistant on your own documents (RAG) | Answers questions from your documents, website and policies | $20,000–$60,000 | 1–3 months |
| Custom AI assistant with integrations | Looks up data in your CRM, ERP or order systems and takes actions | $60,000–$150,000+ | 3–6 months |
These ranges match our AI chatbot development cost guide and are planning estimates, not fixed prices. After a short discovery call we give you an estimate for your exact use case.
Monthly costs after launch
| Cost | What it covers | Typical range |
|---|---|---|
| AI model usage | Charges from the model provider (OpenAI, Anthropic, Google and others) based on volume | From tens to thousands of dollars per month |
| Hosting and vector database | Servers, storage and search for your knowledge base | $50–$1,000+ per month |
| Maintenance and improvement | Monitoring quality, updating prompts, models and data | Depends on how often you add features |
What drives the cost of AI integration
- Data preparation: connecting, cleaning and organising the documents and databases the AI will use is often the largest part.
- Integrations: every system the AI reads from or writes to adds work and testing.
- Accuracy requirements: customer-facing or regulated uses need more testing, guardrails and human review.
- Model choice: larger models cost more per request; smaller or open-source models can cut monthly costs.
- Security and access control: deciding exactly what each user and the AI may see.
- User interface: a simple chat box costs less than AI built into several screens and workflows.
How to start small
- Pick one task where AI saves measurable time, such as answering support questions or extracting data from forms
- Build it as an API your existing app calls, so you do not rebuild your product
- Test with real examples before launch and keep a person in the loop for uncertain cases
- Track cost, speed and answer quality from day one
- Expand to the next use case once the first one pays for itself
A starter MVP with Three Circle costs $3,000, or $1,500 when you claim our free clickable design of 5–6 key screens.
AI products we have built
Our AI work includes Grant Intel, an AI grant discovery and application SaaS; CannAvance, an enterprise SaaS with AI workflows; Sport Betting IQ, a sports app with LLM-powered insights; Careerails, an AI recruitment platform; and Optimized Strength, a fitness app with personalised AI coaching. See our AI integration services.
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Frequently asked questions
Typically around $15,000-$80,000 for AI features inside an existing app, $20,000-$60,000 for an assistant that answers from your documents, and $60,000-$150,000+ for a custom assistant that connects to your systems.
AI model usage fees based on volume (from tens to thousands of dollars a month), hosting and a vector database ($50-$1,000+ a month), and maintenance.
A single AI feature or document assistant usually takes 1-3 months; a custom assistant with several integrations 3-6 months.
No. AI is usually added as a separate service your existing app calls through an API.
Start with one measurable use case, choose the smallest model that gives good answers, cache common answers and track usage from day one.
