AI Application Development Services

AI application development is building an actual application (web, mobile, or enterprise) with AI at its core, and taking it all the way to production. Our AI-powered app development services cover more than the model: product strategy, model selection, RAG and data grounding, the interface people use, guardrails, integration, and the MLOps needed to run it reliably for real users.

They're three different jobs:

• AI application development builds a new application end-to-end.

• AI Development focuses on custom models: training, customization, and fine-tuning.

• AI Integration adds AI into systems you already run.

Many projects use two or three of these together, and we'll tell you which you actually need during scoping.

Per industry averages, a focused AI feature or proof of concept typically runs from around $20,000–$80,000, while a production-grade AI application usually lands between roughly $70,000 and $300,000+, depending on data readiness, model choice, integrations, and compliance scope.

Custom work doesn't fit a fixed price list, so rather than quote a misleading number, we scope your project and provide a tailored estimate after an initial consultation, and our scope generator can give you a quick ballpark in the meantime. We favor an MVP-first approach to control AI app development cost.

It depends on scope, but our architect-led, AI-accelerated delivery gets you a working proof of concept in about 1 week and a functional MVP in roughly a month, then iterates to production from there. The first step is a Scope & Design Document that turns your idea into a build-ready plan, which is what our Product Discovery engagement produces.

Yes. We build native and cross-platform AI mobile applications, and we handle the parts that make mobile AI hard: latency, battery, offline behavior, and deciding what runs on-device versus in the cloud. We built the React Native app for SafeMode's AI fleet-safety platform, delivering real-time driver feedback that lifted downloads 35% and improved driver safety 25%.

Yes, and we have shipped one for ourselves. Our own AI assistant runs in production on the Model Context Protocol with 16 real-time tools, lead qualification, and a multi-layer security stack, built in 4 weeks with 1 developer. For your app, we cover conversation design, grounding in your own content with RAG, tool access, guardrails, and the monitoring that keeps answer quality from drifting after launch.

Yes. Enterprise AI applications carry requirements consumer apps don't:

• SSO and role-based access

• Audit trails

• Data residency

• Integration with systems you already run

We build for those constraints, including fully private deployments. For Sylo, we shipped an AI agent platform as a single binary that runs on the user's own hardware, where memory and credentials never leave the machine.

Yes. We've shipped AI and software for:

• Healthcare (DispatchHealth, Doctify, RetinaRisk)

• PropTech (Plaace), legaltech (LegalFly)

• Retail (Renovai)

This included handling the compliance and data-handling that those domains require. For AI healthcare app development specifically, PHI handling, HIPAA-aware architecture, and audit logging are designed in from the first architecture decision, not retrofitted. See our industries for domain-specific expertise.

By treating it as engineering, not a demo. A senior architect designs the system, data flow, and guardrails before any code; we ground outputs in your data with RAG, measure quality with evaluation suites, and wire in monitoring from the first sprint. That's the gap between the ~95% of pilots that stall and an application that ships, and it's how we build our own AI products.

We build model-agnostically. Your application works across OpenAI, Anthropic, Google, Llama, and Mistral, with the provider abstracted behind our architecture, so switching models is a configuration change, not a rebuild. We run our own products this way, routing across multiple providers in production.

Yes. We add AI features (copilots, RAG assistants, generation, search) into an existing app through documented, secure APIs, without disrupting what already works. Check our AI Integration services if the goal is to connect AI across your wider systems rather than build a new application.

Yes, completely. You own all the source code, models, prompts, design assets, and intellectual property, with full rights transferred to you. There's no vendor lock-in: you're free to host, extend, or move the application to another team whenever you want.

Security and compliance are built in, not bolted on. We follow SOC 2, HIPAA, and GDPR practices, design for EU AI Act requirements, and can deploy open models in your own cloud or on-prem so sensitive data never leaves your environment. Every AI application ships with prompt-injection defenses, output validation, and audit logging, the same discipline behind the multi-layer security stack on our own AI product.

By not defaulting to the biggest model for everything. We right-size models, using Small Language Models where they win, which can be 10–30x cheaper to serve, and add caching, batching, and RAG so you pay for accuracy, not an oversized model or an unnecessary training run. Cost predictability is part of the architecture, not an afterthought.

Each has trade-offs:

• In-house gives you control but is slow and expensive to staff

• Big consultancies bring brand and strategy but often stop at the pilot

• Freelancers are cheap but rarely own the whole build

A product partner like DBB gives you a vetted senior team that ships a live application, owns the architecture, stays model-agnostic, and transfers full IP to you, available now, at a fixed scope.

If you would rather grow your own team than outsource the build, you can also hire AI engineers to work as an extension of your in-house staff.