Generative AI · GenAI · LLMs · Flutter · Mobile
Generative AI integrated in mobile apps.
LLMs, image and voice at the core of your app.
+300
Projects delivered
15+
Years of experience
100%
Senior team
LLMs, image and voice at the core of your app.
+300
Projects delivered
15+
Years of experience
100%
Senior team
Generative AI has moved beyond a chatbot on a website — it now sits at the core of the world's most downloaded apps. Cameras that describe what they see with computer vision, apps that generate personalised content with real-time streaming LLMs, assistants that know the user's context through RAG systems connected to your data, and semantic search engines that understand intent rather than matching exact words. All of this is possible today — the differentiator is the architecture that controls inference costs.
Dribba integrates generative AI in mobile apps built with Flutter, iOS and Android: calls to LLMs (GPT-4o, Claude, Gemini) with real-time streaming, on-demand image generation with Stable Diffusion and DALL·E, voice recognition with Whisper and contextual transcription, semantic search with embeddings, and on-device models with llama.cpp and Core ML for offline operation. With the right architecture to ensure inference costs do not eat into the product margin — see our AI integration service for the full approach.
Related services
Frequently asked questions
The highest-ROI features depend on the use case, but consistently strong performers are: conversational assistants contextualised with the user's own data (not generic chatbots), semantic search that finds content by meaning rather than keywords, AI-driven content generation (personalised descriptions, summaries, reports) and voice interfaces for hands-free use cases. The Product Discovery identifies which ones apply to your specific product.
Through four techniques: intelligent context caching (avoid sending the same context with every API call), smart model routing (use smaller, cheaper models for simple tasks and reserve GPT-4o/Claude Opus for complex ones), on-device models for frequent low-complexity operations, and pre-generation of predictable responses. Dribba projects and monitors inference costs from architecture design, not after launch.
Yes. We implement on-device models using llama.cpp (Android/iOS), Core ML (Apple Silicon) and TensorFlow Lite for specific tasks: local text classification, intent detection, summarisation and speech recognition. On-device AI has no inference cost, works with zero latency and does not send user data to external servers — ideal for privacy-sensitive use cases.
A basic integration (LLM with streaming + semantic search on existing content) starts at €20,000–35,000. A full AI feature set with multimodal (text + image + voice), RAG system and on-device models ranges from €50,000 to €120,000 depending on complexity. Monthly inference costs (LLM APIs) vary by usage volume — we project these during the Product Discovery.
Have a project in mind?
No commitment, no small print. An honest assessment of your idea with the team that will build it.