AI App Development Services

AI app development is the process of designing, building, and deploying applications that use artificial intelligence, such as machine learning, large language models, and automation, to handle work that once needed human judgment.

The benefit depends on your goal. Most companies use AI apps to cut costs by automating manual work, make better data-driven decisions, speed up customer response, and ship features competitors cannot match

AI app development takes 8 to 12 weeks for a focused integration, 12 to 24 weeks for a custom AI mobile app, and 16 to 32 weeks for an end-to-end generative AI product. Enterprise AI programs run 6 to 18 months, depending on scope.

The main reason projects slip is data that is not ready, which is why we start with a discovery and data check.

AI app development cost ranges from about $25,000 for a simple LLM assistant to $2 million or more for a full enterprise AI program. The main drivers are system complexity, data readiness, integration scope, and compliance needs.

The table in the cost section above shows typical ranges by project type, and a scoping call gives you an accurate estimate for your use case.

Yes. We build AI-powered mobile apps for iOS and Android that go beyond bolt-on features, using on-device AI for faster response times, offline use, and stronger data privacy, plus cloud processing where it makes sense.

We also add AI personalization that adapts content to each user, and AIoT builds that combine mobile engineering with real-time sensor data and anomaly detection across manufacturing, logistics, and healthcare.

Building in-house means you own the AI development team and control the IP long-term, which is right when AI is core to your product and you can support a full engineering team at roughly $900,000 to $1.4M per year. Hiring in-house also adds a three- to five-month wait for a senior AI engineer.

Hiring Cypherox compresses time to production from months to weeks, removes recruiting risk, and gives you engineers who have already shipped production AI. It is the right choice when speed is the constraint, when specific AI skills are missing, or when you want to validate an initiative before adding permanent headcount.

We set security and compliance rules at the start, not after launch. For regulated industries, we design AI to comply with GDPR, HIPAA, SOC 2, and ISO 27001 from the outset, covering data handling, logging, access controls, and audit trails.

We sign NDAs at the start of every engagement, and data shared during scoping and development is kept confidential and is never used to train shared models.

Yes, and we check integration compatibility before any design decision. Our discovery phase includes a technical review of your data, APIs, authentication, and performance needs, so the AI fits your current setup rather than forcing a rebuild.

The common challenges are pipeline latency, inconsistent data schemas, and enterprise authentication, and we identify each in discovery so it does not become a production problem.

We offer three models: fixed-scope projects for defined AI features and integrations, dedicated AI teams for multi-month product builds, and team augmentation to add specific AI skills to your existing team.

Each starts with a scoping call, and we send a detailed estimate within five business days.

Every production launch includes post-launch monitoring of output quality, speed, uptime, and data flow. AI models can lose accuracy over time as data changes, a problem called model drift, so we schedule regular updates, usually quarterly, using user feedback.

Support terms are set per engagement, and long-term optimisation partnerships are available for clients who want ongoing model governance without managing it in-house.