Services · Mobile app development · AI apps
Codeeo delivers AI app development in New York for financial firms, law practices, property managers, publishers, healthcare providers and fast growing startups across the five boroughs. We build the mobile and web apps people use, plus the model services behind them: research assistants, document review, multilingual support and screening tools. New York's own rules, from DFS cybersecurity requirements to the city's law on automated hiring tools, are designed in from the start.
Codeeo has no New York office. Our 80+ people are based in Dubai, Riyadh and Karachi and collaborate remotely with clients in New York, running discovery, reviews and demos by video call with a written record after each one.
200+ companies across the US and the UAEFrom a first store to a listed developer
What a New York AI app can do
New York concentrates industries that cannot afford a confident wrong answer. Asset managers and banks around the Financial District and Midtown answer to the Department of Financial Services. Law firms bill by the hour and guard privilege closely. Publishers near Times Square and in the Flatiron District care about attribution. Property managers run thousands of apartments where a tenant's request might carry legal weight, and startups from DUMBO to Long Island City sell into all of them.
The city's users add their own demands. Many commute underground with patchy connectivity, many speak Spanish, Chinese, Russian, Bengali or Haitian Creole at home, and most expect an app to be fast and to the point. These are the capabilities New York clients most often ask us to combine, each with the logging and human checkpoints that a regulator, a client or opposing counsel might later want to see.
An analyst tool that reads earnings transcripts, filings, broker notes and your internal research, answers questions with citations to the exact page, and compares periods side by side. Material nonpublic information stays walled off by permission, and every query is logged for compliance review. Answers that cannot be supported by a source are refused rather than invented.
Analysts get to the relevant paragraph without opening twenty PDFs.
Clause extraction, playbook comparison and first pass review of document productions for law firms and in house teams. The model marks deviations from your standard positions, a lawyer accepts or rejects each one, and privileged material never leaves your tenancy.
Associates spend their hours on judgment, not on reading boilerplate.
Screening and ranking features for employers hiring in New York City are covered by Local Law 144, which calls for an independent bias audit and candidate notices. We build the data exports an auditor needs, the notice flows and the option for alternative assessment where you offer one.
Your AI hiring tool can face an audit without a scramble.
A resident app that logs repair requests from a photo, answers lease and amenity questions from your own documents, and routes urgent issues such as no heat or a leak straight to staff. Requests carry timestamps that superintendents and managers can rely on. Building staff can reply from their phones, and residents see progress without calling the management office.
Repairs are logged properly and nothing urgent waits in a queue.
Support and intake assistants that work in the languages New Yorkers actually use, with native speaker review of tone and terminology. Records keep both the original message and the English version, so staff and auditors read the same thing.
More customers are served without waiting for an interpreter.
AI mobile app development for New York users has to assume dead zones between stations. We cache recent content, queue requests and run small models on the device for drafting, search and summaries, then sync when signal returns.
The app keeps working between Union Square and Atlantic Avenue.
Summaries, headline options, tagging, transcription and archive search for publishers, with sources linked and every suggestion clearly marked as machine drafted. Editors stay the final authority, and nothing publishes without a named person approving it.
Editors move faster while keeping their standards intact.
For practices and care networks, intake, triage questions and scheduling assistance under HIPAA, with protected health information processed only in covered services. Clinical decisions remain with licensed staff, and the assistant says so to patients plainly.
Front desks handle more patients with fewer callbacks.
If your app adjusts prices or offers using personal data, New York now expects that to be disclosed to the consumer. We build the disclosure into checkout and keep a record of which signals informed each price, so marketing can personalize while legal can see exactly what happened.
Personalized pricing that holds up to scrutiny.
Every model, prompt version, data source and output is recorded in a register your risk team can search. DFS has published guidance on AI cybersecurity risks, and the register supports the access controls, monitoring and vendor oversight that guidance discusses.
Risk and compliance can answer what the AI did, when and why.
Who this is for
Our custom AI app development for New York clients tends to begin from one of the situations below. They differ in regulator, data sensitivity and pace, so the first release, the hosting decision and the people we need in discovery differ too. Speed expectations also vary across the city. A trading desk wants a tool next quarter, while a hospital network may need months of committee review first. We plan around the slower approvals instead of pretending they will not happen, and we use that time to prepare data and test sets so the build itself moves quickly once the green light arrives.
Banks, insurers and money transmitters licensed in New York operate under the DFS cybersecurity regulation, Part 500, which covers access control, monitoring and third party risk. We start with an internal use case such as research or operations support, deploy inside your cloud, and hand your CISO the documentation needed for vendor and risk assessments before anything reaches customers.
Your security team reviews architecture before the first sprint.
Firms in Midtown and Lower Manhattan want review and drafting help without risking privilege or client confidentiality. We run models in an environment your IT controls, mirror ethical walls from your document management system and keep a lawyer approving each output that leaves the firm.
Client data is excluded from any vendor training by contract.
Owners with buildings across the boroughs need faster repair handling, clearer tenant communication and better records. A resident app with an AI assistant and a staff console can cut response times, but tenant screening or pricing features also raise fair housing questions, so we keep those decisions with people and log the criteria used. Superintendents get a simple mobile view of open work orders with photos and history attached.
Automated decisions about tenancy stay under human review.
Founders in Brooklyn and Queens building for banks, hospitals or media buyers need their product to survive procurement. We build with SSO, role based access, audit logs and data residency options from the beginning, so the first enterprise security questionnaire is answered from documentation rather than promises. We also prepare architecture notes and model documentation that buyers' risk teams increasingly request during vendor onboarding.
Enterprise readiness costs less when it is built in early.
How we work together
A fixed scope first release is the usual starting point, followed by a retainer that covers monitoring, model updates and new features. Firms with internal engineering often add a dedicated Codeeo team that works inside their repositories, ticketing and code review rules. As an AI app development company serving New York remotely, we treat written records as part of the deliverable: decisions, test results and model changes are documented so an auditor or a new hire can follow them without us in the room. Pricing for each route is set after discovery.
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
Native Swift and Kotlin or React Native and Flutter, Python and TypeScript services, vector search on Postgres or a managed store, and hosting on AWS, Azure or Google Cloud inside your own accounts. Language models sit behind one interface so you can change provider. Identity runs through Okta, Microsoft Entra ID or your existing single sign on provider.
01Mobile
02Data and cloud
03Marketing and tracking
Internal use first, inside your cloud, with DFS expectations mapped early.
We usually begin with a staff facing tool where errors are caught by a professional before they reach a customer. The architecture is reviewed against Part 500 requirements on access, monitoring and third party service providers, and the vendor documentation your risk team needs is produced alongside the code. Customer facing features follow once the internal tool has a track record.
Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.
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