Services · Mobile app development · AI apps
Codeeo handles AI app development in the USA for companies that want language, vision, voice and prediction features inside apps people use every day, and that need those features to hold up across many states at once. We design and build iOS and Android apps, web consoles and the model services behind them, with federal sector rules, state privacy laws and FTC expectations on AI claims treated as design inputs rather than afterthoughts.
We have no American office. Codeeo's team of 80+ in Dubai, Riyadh and Karachi works remotely with clients across the United States, and all discovery, design reviews and acceptance testing run by video call, with written decisions after every session.
200+ companies across the US and the UAEFrom a first store to a listed developer
What a national AI app needs
Building an AI app for the whole American market is a different exercise from building for a single city. There is no single federal privacy law, so obligations depend on where users live and what sector you serve. HIPAA governs health data held by covered entities and their vendors, GLBA applies to financial institutions, COPPA protects children under thirteen, and the FTC can act against any company whose AI claims or data practices are unfair or deceptive. On top of that, a growing list of states has its own consumer privacy statute, and several have passed laws aimed at AI itself.
National products also meet more variety in users: different accents and languages, different devices and connection quality, and very different expectations between enterprise buyers and consumers. The capabilities below are the building blocks we use most for US wide products. We choose and order them per project, and each comes with the controls needed to roll out state by state without rebuilding.
Retrieval augmented generation over help centers, policies, product data and knowledge bases, with citations on every answer. Content can be tagged by state or product line, so a user in Illinois sees rules for Illinois and a user in Florida sees rules for Florida.
Answers stay accurate as your content and your footprint change.
Assistants that do more than talk: reschedule an appointment, file a claim, change a plan or create a ticket by calling your APIs. Every action has a permission check, a confirmation step for anything consequential and a log entry tied to the user.
Customers finish tasks in one conversation instead of three channels.
Inbound voice assistants and outbound reminders that sound natural and disclose they are automated. The FCC has ruled that AI generated voices count as artificial voices under the Telephone Consumer Protection Act, so outbound calls are designed around prior express consent and easy opt out.
Phone automation that does not invite TCPA claims.
Architectures for health, financial and student data: HIPAA eligible cloud services with business associate agreements, GLBA safeguards for customer financial information and FERPA aware designs for schools. Sensitive fields are minimized, encrypted and kept away from model providers unless contractually protected.
Security reviews pass because the controls were designed in.
AI mobile app development across the USA often means users in rural areas with weak coverage. Running speech, vision and small language models on the phone through Core ML or ML Kit keeps features usable offline and keeps personal data on the device.
Features work in a farm town as well as in a downtown office.
Where a model influences credit, insurance, housing or employment outcomes, users may be owed specific reasons. Federal lending rules require specific adverse action reasons even when a complex model is involved, so we build reason codes, human review queues and decision logs into the product.
You can explain any automated outcome to a customer or regulator.
Screen reader support, captioned voice features, adjustable text and plain language output, tested against WCAG. Accessibility lawsuits under the ADA are common in the US, and public sector buyers expect Section 508 conformance, so we test with assistive technology before release.
More Americans can use the app, and procurement teams can buy it.
Apple Pay, Google Pay, cards through Stripe or Braintree, ACH for larger amounts and in-app subscriptions for AI tiers. Card data stays with PCI DSS compliant processors, and usage based plans meter model consumption per account so pricing matches cost. Rules on linking to outside payment options in US apps have shifted after recent court rulings, so we confirm the current position before designing checkout.
Revenue follows usage without exposing card data.
Feature flags by state, jurisdiction aware consent screens, privacy request workflows that handle each state's deadlines, and configuration for disclosures that differ by law. Launching in a new state becomes a configuration change and a legal sign off, not a new release.
Expansion follows legal approval rather than engineering backlog.
Evaluation pipelines, drift monitoring, cost dashboards and routing between large and small models by task. At national scale small inefficiencies multiply, so we track cost per conversation and per feature from the first day of production.
Running costs stay visible and predictable as users grow.
Who this is for
As an AI app development company working with US clients, we see four common starting points. Each brings a different federal regulator, a different state footprint and a different first release, so we plan them in different ways. Custom AI app development in the USA also has to respect how American buyers purchase software. Enterprise customers run security reviews and ask for SOC 2 reports from their vendors, health systems ask for business associate agreements, and public agencies may need FedRAMP authorized hosting. We find out early which of these your buyers will require, because each one changes hosting, logging and vendor choices.
Telehealth, care navigation and wellness businesses need intake, triage support, documentation help and member chat, often across many states with different licensing for clinicians. We build with HIPAA eligible infrastructure, keep clinical decisions with licensed providers and configure features by state so expansion follows your licensing footprint.
Clinical claims in marketing are reviewed against FTC expectations.
Lenders, neobanks and insurers want faster onboarding, servicing and claims, but face fair lending, adverse action and state insurance rules. We start with servicing and document work where AI saves time without making the regulated decision, then add decision support with explanations and human review once your compliance team is satisfied. State insurance departments and banking regulators may ask how models were tested, so evaluation results are kept in a form you can hand over.
Regulated decisions keep a documented human in the loop.
Retail, travel, fitness and education apps want assistants, personalization and creation tools. The questions here are user trust, age gating where children may be present, honest marketing claims and running cost per user. We design features that are clearly labeled, easy to turn off and measured against retention and revenue, not novelty. App store review guidelines for generated content and user reporting are planned in, so approval does not stall the launch.
Anything marketed as AI must actually do what the ad says.
SaaS teams adding AI need features that enterprise buyers can approve: tenant isolation, admin controls over which models are used, data retention settings and documentation for security questionnaires. We build these alongside the visible features, so sales cycles are not stalled by a missing control.
Enterprise controls ship with the first AI feature, not after.
How we work together
Most clients start with a fixed scope first release that proves one AI feature with real users, then move to a monthly retainer for model operations, compliance updates and new features. Companies building AI as their core product often take a dedicated Codeeo team that works in their repositories, tools and ceremonies. Code and accounts belong to you from the start in every model. Codeeo has delivered 200+ projects across the UAE and the US, and fees for each option are set after discovery, once data work, integrations and launch states are known.
A defined build with a deadline
We agree what the product has to do, write it down with a price and a date, and build exactly that. Changes are priced before they are made, not discovered on the invoice.
A product that keeps evolving
Named engineers who work your hours, join your standups and stay on the codebase. You direct the backlog; we own the quality of what ships.
Something live that needs looking after
A monthly block of hours for improvements, fixes and the small requests that otherwise wait months. Unused hours carry for a quarter.
Technology
Swift, Kotlin, React Native or Flutter on the client, Python and TypeScript services, Postgres with vector search, and AWS, Azure or Google Cloud in US regions. We work with hosted models from major providers and open models you can run yourself, behind one swappable interface.
01Mobile
02Data and cloud
03Marketing and tracking
Sector laws, state privacy laws, FTC rules and a small set of AI specific state laws.
HIPAA, GLBA, COPPA and FERPA apply by sector and user type. State consumer privacy laws such as California's CCPA and Texas's TDPSA govern personal data and profiling. The FTC polices deceptive AI claims and unfair data use. A few states regulate AI directly; Colorado, for example, replaced its 2024 AI Act in 2026 with a narrower disclosure focused law due to start in 2027. We map these per project with your counsel. Illinois adds a biometric privacy statute with a private right of action and rules on AI in employment decisions, which matters for any face, voice or hiring feature used there.
Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.
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