Most modern mobile apps can be launched in 8–12 weeks for an MVP using agile development and modern frameworks. More complex platforms with advanced features, integrations, or AI capabilities may take 3–6 months.

Yes. We build AI directly into iOS, Android, and cross-platform apps — an in-app chatbot that answers using your own documents (retrieval-augmented generation, or RAG), computer vision that reads a photo of a document or barcode, and recommendation engines that personalize what each user sees. Most projects use a cloud model API (GPT-5 via Azure OpenAI, or Claude via Amazon Bedrock) rather than training a custom model from scratch, which keeps cost and timeline predictable.

We build mobile applications for iOS, Android, and cross-platform environments using modern frameworks such as React Native and Flutter. Cross-platform development allows companies to reach users on both major mobile ecosystems while reducing development time and cost compared to building separate native applications.

Yes. Our clients in the United States work with US-based account managers and product strategists who oversee communication, planning, and delivery. This ensures clear collaboration, aligned time zones, and faster decision-making throughout the development lifecycle.

Yes. If your current mobile application is outdated or experiencing performance issues, we can redesign the user experience, rebuild the application architecture, or migrate it to modern frameworks to improve scalability, security, and overall performance.

Yes. Mobile applications can be integrated with AI models, cloud infrastructure, ERP systems, CRM platforms, APIs. These integrations allow businesses to automate processes, unlock real-time data insights, and deliver more intelligent digital experiences to users.

Ask which LLM provider they use and whether it runs inside your own cloud account or a shared public endpoint — that answer determines whether your data is exposed to a third party. Ask if they’ll sign a Business Associate Agreement (BAA) for healthcare data, and whether prompts and AI responses are logged with PHI or PII redacted first. A vendor who can name the specific architecture (on-device machine learning vs. a cloud model API, retrieval-augmented generation vs. fine-tuning) and walk through the data flow end-to-end has done this before in a regulated environment. Sunflower Lab scopes these answers with your compliance team in the first project conversation, not after a security review flags a problem.

Yes — we scope AI features so protected health information (PHI) and other sensitive data never leaves an environment you control. For regulated clients, that means running the AI model inside your own cloud tenant (Azure OpenAI, AWS Bedrock, or Google Vertex AI) under a signed Business Associate Agreement (BAA), rather than sending data to a public consumer AI tool. Every AI interaction — the prompt sent and the response received — is logged with a unique user ID and timestamp, so your compliance team has an audit trail if an auditor or internal reviewer ever asks. We scope this at project start, not as a retrofit before launch.

A custom-built app is written around your specific workflow, data model, and integrations — your CRM, ERP, or internal APIs — rather than fitting your business into a pre-built template’s constraints. Template builders are faster for a simple app with no unique logic; custom development is the right call once you need AI features, a specific compliance posture, or integration with systems you already run.