AI Development and Automation forAmerican Businesses.

AI development services in the USA range from useful to pointless, and the difference is usually the job you point the AI at. We build AI and automation for US businesses from Greater Noida, India: systems that read documents, answer routine questions, draft replies, route work and update your records. This page explains what we build, what we advise against, the US rules that affect AI projects, and how to separate a working proposal from a demo.

What we build for US businesses

The projects that pay back share a pattern: a repetitive task, a clear definition of a good result, and a person who can check the output.

• Document processing. Extract fields from invoices, purchase orders, bills of lading, insurance forms or contracts, validate them against rules, and post them to your ERP or accounting system. A model extracts; a rules engine checks; a person reviews the exceptions.
• Customer support assistants. Chatbots that answer from your own documentation and order data, hand off to a human with the full conversation, and say plainly when they do not know.
• Sales agents. Lead qualification, follow-up drafting and meeting scheduling that write into your CRM. See our AI sales agents page.
• Back-office automation. Approvals, reconciliations, reminders and reports across finance and HR. See HR and finance automation.
• Search over your own knowledge. Staff ask questions of manuals, policies and past tickets and get answers with the source passages attached.
• Messaging assistants. Chat flows on web, SMS-capable platforms or WhatsApp, including our WhatsApp AI chatbot work.

We have built products with automated flagging, scoring and lead discovery, mostly for Indian businesses, such as the NidaanLab pathology lab system, which flags abnormal results, and the Marketing OS lead CRM, which scores and routes leads. Those are rules-based automations and data workflows rather than US deployments. The main AI and automation page lists the full range.

What we advise against

Be careful with proposals that promise to replace a whole department. We will say no, or propose a smaller first step, when:

• There is no way to measure whether the output is right.
• A wrong answer is expensive and nobody checks it (medical, legal or credit decisions).
• Your data is too scattered or low quality to use. A cleanup project comes first.
• A fixed rule or a simple script would do the job. It is cheaper and easier to audit.

US requirements for AI projects

Privacy and automated decisions

AI projects process personal data, so state privacy laws apply. California's CCPA, as amended by the CPRA, gives residents rights to know, delete, correct and opt out of the sale or sharing of data; the California Attorney General explains who is covered. The California Privacy Protection Agency's regulations, approved in September 2025, address risk assessments, cybersecurity audits and automated decision-making technology, including consumer rights to access and opt out of its use; the CPPA's page has the text and dates. Other states, such as Virginia, Colorado, Connecticut, Utah, Indiana, Kentucky and Rhode Island, have their own privacy laws, and state AI-specific rules are changing quickly, so ask your counsel about hiring, lending, housing and insurance uses in particular. We design an AI system so a person can see why a decision was made, override it and export the record.

Truthful claims and data security

The FTC expects businesses to keep their privacy and security promises, and to collect only what they need and dispose of it securely; its privacy and security guidance is a useful checklist. Practically, do not tell customers an AI assistant is a human, do not claim it is more accurate than you have measured, and do not feed customer data to a service whose terms you have not read.

Health data

If an AI feature processes protected health information, HIPAA applies, and each vendor in the chain, including the model provider, needs the right agreement. HHS publishes sample BAA provisions. HIPAA has no certification for developers. We raise this at discovery and, where possible, build and test with synthetic data; whether a given model provider will sign a BAA for your use must be checked before you design around it.

Model providers, hosting and SOC 2

Most AI systems call a model API (from OpenAI, Anthropic, Google or others) or run an open model on your own cloud. Where your data goes is a design choice we make with you: a hosted API is quicker, and a model in your own cloud account keeps data inside your boundary. We do not hold a SOC 2 report. Our controls are access limits, audit logs of every prompt and action, encrypted backups, secrets management and hosting in your accounts, so your own review covers the system. If an enterprise customer needs SOC 2 evidence for the AI vendor, check the model provider's reports as well, and ask us early.

Working across time zones

India is UTC+5:30 all year. US Eastern is 9 hours 30 minutes behind until November 1, 2026, then 10 hours 30 minutes behind; Pacific is 13 hours 30 minutes behind. Expect one to three hours of live overlap, your morning and our early evening, and asynchronous handover. AI projects need a domain expert on your side to review sample outputs; we schedule that review in your morning and track accuracy on a shared board. Every two weeks we show working software on a staging server. More on the model is on the USA hub.

Engagement models and pricing

• Fixed quote. For a defined pilot, such as one document type or one support channel, after discovery and a written scope.
• Dedicated team. A monthly team when AI is a core product feature or you have a long list of automations.
• Support retainer. Monitoring, accuracy reviews, prompt and rule updates, and model changes after launch.

We do not publish dollar prices. API usage costs from model providers are a separate, variable line that we estimate with you during discovery, so you are not surprised later.

Process and timeline

We start with a pilot rather than a platform. A typical path: one to two weeks defining the task and collecting 50 to 200 real examples, a prototype tested against those examples, then a working pilot that is integrated with your system and reviewed by staff, often within 10 to 14 weeks in total. After that you decide whether to widen it. You receive the source code, evaluation set and accuracy report, prompts and rules, documentation, and cloud accounts in your name.

Risks of AI projects, and how we reduce them

• Wrong answers. An evaluation set, human review of uncertain cases, and fallbacks to a person.
• Data leakage. Minimal data sent to models, redaction of identifiers where possible, and logs.
• Vendor dependence. Model calls sit behind our code so a provider can be swapped.
• Drift. Accuracy checks after launch, because documents and customers change.
• Ownership. Code, prompts and evaluation data belong to you, with IP assignment in the contract.

How to evaluate an AI vendor

• Ask for the accuracy measure and the test set, not a demo.
• Ask what happens when the AI is unsure or wrong.
• Ask where your data goes, who can read it, and how long it is kept.
• Ask who owns the prompts, models and evaluation data.
• Ask what they would not automate, and which certifications they do not have.

Next step

Describe one task you would like to automate, with a few real examples and the system where the result should land. We will reply with questions and a written scope for a pilot. See also our US custom software and US CRM pages, or contact us.