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AI Development for US Startups and Enterprises

LLM features, RAG search and AI agents built on your data in your US cloud accounts, with the privacy, accuracy and audit controls American buyers ask about.

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AI Development for the United States

AI Development, built around how the United States works

US companies tend to move from AI experiments to production quickly, and the questions change just as fast: where does customer data go, which model provider offers a BAA or zero data retention, how are hallucinations measured, and who is accountable when an automated decision affects a person. Nexzem builds generative AI, RAG and agent systems for US clients from India, with evaluation and logging built in from the first prototype.

There is no single federal AI law in the US. Sector regulators, the FTC and a growing set of state laws apply existing rules to AI, while the voluntary NIST AI Risk Management Framework gives enterprise buyers a shared vocabulary for risk. We design so you can answer their questions with evidence, such as evaluation reports and data flow diagrams, rather than assurances.

US rules that shape AI products

These are the rules that most often change the design of an AI feature for American users. This is general information, not legal advice; we document data flows, model versions and evaluation results so your counsel can assess each one.

  • FTC Act: claims about what your AI does must be truthful and supported, and the FTC has acted against deceptive AI marketing
  • HIPAA: PHI sent to a model provider needs a BAA and HIPAA-eligible services, and de-identification follows Safe Harbor or Expert Determination
  • NYC Local Law 144: automated employment decision tools used on New York City candidates need a bias audit and candidate notice
  • State privacy and AI laws: California, Colorado and other states regulate automated decision-making and profiling, and the rules are still evolving
  • Illinois BIPA: collecting face or voice biometrics requires written consent, which matters for computer vision and voice AI
  • Copyright: the US Copyright Office does not register purely AI-generated material, which affects content products

Model hosting, data retention and cost

For most US clients we deploy models through Microsoft Foundry (including Azure OpenAI), Amazon Bedrock or Google Vertex AI inside the client's own cloud account and US regions, so prompts and outputs stay under existing data agreements. Where a direct API from OpenAI or Anthropic fits better, we use enterprise terms and zero data retention options where available, and send PHI only to services covered by a BAA.

Token costs at US user volumes can surprise a finance team, so we add per-tenant spending limits, response caching and routing between larger and smaller language models. Our free LLM token estimator gives a quick sense of how prompt size drives cost.

What our AI development covers

Custom AI software built on your data, from a first working prototype to a monitored production system.

  1. 01

    AI Product Development

    New AI-first products built from scratch, covering model selection, backend APIs, web or mobile front ends and the admin tools your team needs to run them.

  2. 02

    AI Feature Integration

    Add prediction, search, summarization or classification to an existing application without a rewrite, using APIs that fit your current architecture and release process.

  3. 03

    Predictive Models

    Demand forecasts, churn scores, lead scoring and risk models trained on your historical records and shown where planners and sales teams already work.

  4. 04

    Intelligent Process Automation

    AI agents combined with workflow tools to read documents, route tickets, fill forms and flag exceptions, so staff spend their time on cases that need judgement.

  5. 05

    Generative AI Features

    Drafting, summarization and question answering powered by large language models, grounded in your content and wrapped with guardrails, logging and spending limits.

  6. 06

    Proof of Concept Sprints

    A time-boxed build on a sample of your real data that answers one question clearly: is this use case accurate and valuable enough to fund?

  7. 07

    Monitoring and Support

    Tracking of accuracy, drift, latency and spend after launch, with retraining, bug fixes and small improvements handled under a clear support agreement.

How a project runs

Clear stages with a review at the end of each, so you always know what happens next and what it costs.

  1. 01

    Discovery

    Map the decision to support, the users, the data available and the metric that defines success.

  2. 02

    Data audit

    Check data quality, volume, labels and access, and close gaps before any modeling starts.

  3. 03

    Prototype

    Build a working model on real data and measure it against the agreed metric.

  4. 04

    Production build

    Wrap the model in APIs, screens, tests, security controls and monitoring, then release.

  5. 05

    Run and improve

    Track accuracy and cost in production and retrain as your data changes.

The stack behind it

Proven, well-supported tools chosen for your scale, budget and team, never for novelty.

  • Python
  • PyTorch
  • TensorFlow
  • Hugging Face
  • LangChain
  • Node.js
  • React
  • PostgreSQL
  • Docker
  • AWS

More services in the United States

AI Development in other markets

Anywhere else

We deliver AI development for clients worldwide. This page covers what changes in the United States; the team, process and contracts are the same wherever you are based.

AI Development overviewWhere else we work

AI Development in USA, answered

Something else on your mind? Ask a consultant and get a reply within one business day.

Can you build a HIPAA-compliant AI chatbot?

We can build a chatbot whose architecture supports HIPAA compliance: PHI processed only by BAA-covered model services, encryption, access controls, audit logs and no PHI in analytics. Compliance also depends on your policies and agreements, which your team owns. See our AI chatbot development service for typical scope.

How do you keep hallucinations out of production answers?

Answers are grounded in retrieved documents with citations, prompts restrict the model to that context, and an evaluation set of real questions is scored before every release. Guardrails block unsupported topics and route low-confidence cases to a person. Our glossary entry on AI hallucination explains why it happens.

Should we use RAG or fine-tuning?

Start with RAG when answers depend on your documents, change often or need citations. Fine-tuning helps when you need a consistent style, format or narrow task behavior that prompting cannot reach. Many products use both. Our RAG vs fine-tuning comparison walks through the decision.

Do we own the prompts, models and pipelines?

Yes. Prompts, evaluation sets, fine-tuned weights, pipelines and application code are assigned to you in the contract and live in your repositories and cloud accounts. Model provider terms still apply to the base models themselves, so we note any license limits before you commit.

Planning AI development for the United States?

Share your scope and market. A consultant replies within one business day with next steps, a rough estimate and a suggested team.

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