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United States

en-US / New York

AI development company in New York for agents, LLM apps and automation

MLChronix is an AI agency building AI agents, LLM integrations, workflow automation and custom AI software for companies across New York City, from Midtown Manhattan, the Financial District and Hudson Yards to Flatiron, Brooklyn and Long Island City. We work live with your East Coast mornings, deploy into your own cloud account, and start every engagement with a fixed-price pilot.

fixed-price pilot · live overlap with eastern mornings · your aws, azure or gcp account · you own the code

01 / Areas we serve in New York

From Midtown Manhattan to Roosevelt Island and the wider tri-state area.

We work remotely with companies across New York, in your hours. These are the business districts and hubs our New York work typically comes from.

Midtown Manhattan

Corporate headquarters, banks, law firms, consultancies and media companies around Park Avenue and Sixth Avenue.

Financial District and Lower Manhattan

Trading, insurance, fintech and the back offices of global financial institutions.

Hudson Yards and Chelsea

Technology, media and investment firms in the newer towers on Manhattan’s West Side.

Flatiron and Union Square

The historic heart of Silicon Alley: SaaS, adtech, e-commerce and venture-backed startups.

SoHo and Hudson Square

Digital agencies, fashion and DTC brands, publishers and creative technology studios.

Brooklyn: DUMBO, Navy Yard and Downtown Brooklyn

Startups, design studios, hardware and manufacturing companies at the Brooklyn Navy Yard.

Long Island City, Queens

Film and media production, logistics, life sciences and growing technology offices.

Roosevelt Island and the wider tri-state area

Cornell Tech’s campus, plus Jersey City, Westchester and Stamford firms that work on New York hours.

02 / AI in New York

Where AI agents pay off in New York.

New York buyers tend to arrive with a specific workflow and a compliance team already asking questions. Banks, insurers, asset managers, media groups and agencies want agents that read documents, prepare reports, answer client questions and move data between systems, and they want to know exactly what the agent did and who approved it. That is how we build: scoped credentials, a full action log, evaluation sets agreed before launch, and a person signing off on anything that changes money or a customer record.

Several New York rules shape how AI is used here. NYC Local Law 144 requires a bias audit, a published summary and candidate notice before an employer uses an automated employment decision tool to screen candidates for hiring or promotion. Firms regulated by the New York State Department of Financial Services must meet its cybersecurity regulation, 23 NYCRR Part 500, and every company holding New York residents’ data falls under the SHIELD Act. We design agents so the logs, access controls and documentation those rules expect already exist.

we design for

  • NYC Local Law 144 on automated employment decision tools (bias audit, published summary and candidate notice)
  • NYDFS Cybersecurity Regulation (23 NYCRR Part 500) for regulated financial services firms, and the New York SHIELD Act
  • NIST AI Risk Management Framework, with HIPAA-aware design and SOC 2 alignment where required

We are engineers, not lawyers. We build to these frameworks and work with your legal and compliance teams on the final review.

03 / Use cases

What we build for New York companies.

AI for banking and asset management in New York

Research and reporting agents that pull from filings, internal notes and market data and draft a summary an analyst reviews, with every source cited and every run logged.

AI for insurance in New York

Claims intake and triage agents that read submissions, check them against policy wording, request missing documents and route each claim to the right adjuster.

AI for law firms and professional services in New York

Knowledge assistants over precedents, engagement letters and past matters that answer with citations, plus intake automation that prepares conflict checks for a person to clear.

AI for media and publishing in New York

Tagging, metadata, summarisation and archive search across large content libraries, connected to your CMS, with editors approving what is published.

AI for fashion, retail and dtc brands in New York

Customer service agents for order status, returns and sizing questions, and product content generation from your catalogue, integrated with Shopify or your commerce stack.

AI for real estate and property management in New York

Leasing enquiry handling, tenant request triage and lease abstraction that extracts key dates and terms into your property system.

04 / Services

AI, web, app and software services in New York.

05 / FAQ

Questions from New York teams.

Do you have an office in New York?

No. We are a remote team based in Pakistan and we are clear about that from the first call. We work live with New York from about 8am to 12pm Eastern every weekday, run workshops and demos on video, and send written updates and recorded walkthroughs for everything that happens outside that window.

We use AI to screen job applicants. Does Local Law 144 affect what you build?

It can. If a tool substantially assists or replaces the decision on hiring or promotion for New York City roles, the employer needs an independent bias audit, a published summary and advance notice to candidates. We design screening tools so their inputs, outputs and selection rates can be audited, and we keep a person making the final decision. The audit itself must come from an independent auditor, not from us.

Can you work within our bank’s vendor and security review?

Yes. We deploy inside your own AWS, Azure or Google Cloud account, use the access you grant and nothing more, and provide architecture diagrams, data flows and logging details for your security team. We sign an NDA before scoping and complete your vendor questionnaires.

What does a first project with a New York team usually look like?

One workflow, one fixed price, two to six weeks. We agree the success measure with you up front, build against your real data in a test environment, and finish with a working agent, its evaluation results and a recommendation on whether to scale it.

Put an AI agent to work in New York.

Thirty minutes, one workflow, an honest view of whether an agent will pay for itself.