AI built into your product, not just used to build it
Two focused services — custom AI agents that take real action, and LLM integration with retrieval-augmented generation grounded in your own data — each a standalone engagement with its own page, run separately or together depending on what you are actually building.
Two services, one engineering standard
Each of these is a real, separately-run engagement with its own scope and its own page — not a bundle you are forced to buy as one. Pick the one your product needs, or run both if it needs both.
Common questions about AI agent and LLM/RAG development
Depends on what the product has to do. If it needs to take action — call APIs, update records, trigger workflows — that is an AI agent. If it needs to answer questions grounded in your own documents or data accurately, that is LLM integration with RAG. Many real products need both: an agent that calls a RAG-grounded lookup as one of its tools. Tell us what the product does on the call and we scope it accordingly, not as two disconnected quotes.
No — different thing. AI-Assisted MVPs with Claude is about how we build your product (using Claude to accelerate our own engineering). AI agent development and LLM/RAG integration are about what we build: AI capability as a feature inside your product, for your end users, regardless of what tooling we use to build it.
The same three tiers as any Applefy project: $20K-$40K for a simple integration (3-6 weeks), $40K-$100K for a mid-complexity build (2-4 months), and $100K-$250K+ for a complex, multi-system build (4-8 months). Running an agent and a RAG integration together is scoped and quoted as one engagement, not two separate projects added up.
Either. Most of this work is adding AI capability to a product that already exists — integrating with your existing backend, data, and auth rather than starting from nothing.
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Book a call with our CEO
Denys Havryliak
Founder & CEO
- 10+ years in Software Engineering
- Master’s in Cybersecurity
- Deep, current knowledge of AI tooling
You’ll talk to the person who builds. Denys works hands-on across product, architecture, and delivery — and keeps a close watch on what today’s AI tooling can genuinely do in production, not just in a demo.