AI Development · United States

AI Development Company USA

A remote team that builds AI features for US companies, from an internal document assistant to a customer-facing tool, around data handling the client controls.

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How we work

Most US AI projects begin with a narrow, concrete problem: a support queue swamped by repetitive questions, a contract review that takes a paralegal a full day, or a sales team that can't find the right case study quickly, with no general ambition to "add AI" behind it. An ai development company working with US businesses spends the first phase shrinking that problem to something an LLM or a smaller specialized model can do reliably, because the distance between a promising demo and a system a team trusts in production is almost always edge cases.

Where the project touches customer data, employment decisions or regulated industries like insurance or lending, the regulatory picture in the US is shifting from federal silence toward a growing set of state-level rules, which changes how the system is designed and logged from the start rather than left for a legal review after launch.

Market

What the USA buyers expect from this work

Data handling comes before model selection
US enterprise buyers care more about where their data goes than which model answers the prompt. We design the data flow so customer and business data isn't used to train a third-party provider's foundation model, using enterprise API terms and, where warranted, a private or self-hosted model, before deciding which vendor or model fits.
Emerging state rules on automated decisions
New York City's Local Law 144 requires an independent bias audit for automated employment decision tools used on NYC-based candidates, and Colorado's AI Act introduces risk-management obligations for high-risk AI systems used in consequential decisions like employment, lending and insurance. We build the audit trail and documentation these rules expect into the system rather than treating them as paperwork bolted on afterward.
Integrating AI into systems that already run the business
The more valuable US AI projects tend to sit inside an existing workflow, drafting a response inside the CRM, summarizing a document inside the case management tool, rather than a standalone chatbot nobody opens. That means the integration work with Salesforce, a support desk or an internal tool is often a larger share of the project than the model logic itself.
Evaluation before rollout
We build a test set from real examples the client provides and measure the system against it before it reaches customers or employees, since a demo that looks convincing on five hand-picked examples can fail in ways that only show up across a few hundred real ones.

Compliance we plan for

State rules on automated decisions, including NYC Local Law 144 and Colorado's AI Act
CCPA/CPRA provisions on automated decision-making and profiling
HIPAA where an AI system touches protected health information
SOC 2-aligned data handling for enterprise procurement

How the engagement runs

AI projects typically start as a fixed-scope proof of concept against a narrow, well-defined task, scoped tightly enough to validate accuracy against real examples before a larger investment is made. Once the proof of concept clears that bar, work shifts to a sprint-based build integrating the feature into the client's existing systems.

IP, prompts, evaluation datasets and any fine-tuned model assets assign to the client on payment, and API keys and infrastructure run under the client's own accounts rather than ours.

Industries

Industries we often work with here

FAQ

Frequently asked questions

Will our data be used to train someone else's AI model?

Not by default. We use enterprise API terms from providers like OpenAI or Anthropic, which contractually exclude submitted data from training, and where that guarantee isn't strong enough for the use case, we look at a private or self-hosted model instead.

Does our hiring tool need a bias audit under NYC Local Law 144?

If it's used to make or substantially assist employment decisions about candidates in New York City, yes, an independent bias audit is required before use, along with public disclosure of the results. We build the logging and evaluation data the audit needs as part of the system rather than as a separate retrofit.

How long does an AI proof of concept take to build?

Typically four to eight weeks for a narrow, well-defined task, most of it spent gathering real examples to test against rather than writing the integration itself. A proof of concept that skips this evaluation step usually looks better in a demo than it performs in production.

Can you build this to run on our own infrastructure instead of a public API?

Yes, for teams with stricter data-residency or security requirements, we can deploy an open-weight model on your own cloud account instead of calling a third-party API, trading some model capability for full control over where the data goes.

How do you measure whether the AI feature is actually accurate enough to ship?

We build an evaluation set from real examples the client provides, run the system against it before rollout, and track accuracy on that set as the system changes, rather than relying on how convincing a handful of demo examples looked.

Does Colorado's AI Act apply to a system we're building for a different state?

It applies based on where the affected consumer is located, not where your company is based, so a system used nationally can fall under it for Colorado-based users specifically. We build the risk-management documentation the law expects (impact assessments, notice to consumers) regardless of which state triggers it first.