Finance infrastructure for AI companies building in the U.S.
AI spend needs to be tracked before tax season
Engineering, model development, data, experimentation, cloud infrastructure, and contractor costs can all affect tax and reporting. We help AI companies organize development spend so it can support accounting, tax, and investor review.
AI margins depend on more than revenue growth
Compute, hosting, data, model usage, and tooling costs can move quickly. We help build reporting that shows gross margin, burn, customer economics, and cost trends with more clarity.
AI teams scale through talent before processes catch up
U.S. hires, contractors, advisors, and equity compensation can create payroll, tax, accounting, and reporting needs. We help companies build the finance setup around how the team is actually growing.
AI companies need reporting that explains the business model
Whether you are pre-revenue, enterprise SaaS, API-based, usage-based, or AI-enabled services, your reporting needs to connect product spend, revenue model, runway, payroll, and R&D activity.
The finance questions AI companies need to answer early
AI companies burn cash in places generic reports do not explain.
AI companies can spend heavily on engineering, cloud, data, contractors, model development, experimentation, and go-to-market before revenue becomes predictable. Standard accounting categories often hide the details leadership and investors actually need.
For international AI companies entering the U.S., the finance system needs to support R&D tracking, U.S. hiring, state registrations, investor reporting, and cross-border cost allocation. Orbiss helps build that structure around the way AI companies actually operate.
Frequently asked questions
AI companies entering the U.S. need finance systems that can keep pace with R&D, hiring, cloud spend, and investor expectations.
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AI companies should prioritize R&D cost tracking, cloud and compute cost visibility, payroll setup, contractor classification, revenue model reporting, cash runway, intercompany activity, and investor-ready financials.
The right setup depends on whether the company is pre-revenue, SaaS, API-based, usage-based, enterprise, services-led, or a mix of models.
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Potentially. AI companies may have activities that qualify for the U.S. research credit, but eligibility depends on the specific work performed, technical uncertainty, experimentation, documentation, and wage or contractor costs.
The credit is claimed on Form 6765, and qualified small businesses may be able to apply a portion of the credit against payroll tax, subject to IRS rules and limits.
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Cloud and compute costs should be tracked in a way that separates product development, customer delivery, internal tooling, experimentation, and general operations where possible.
This helps leadership understand gross margin, burn, product investment, pricing, and which costs support tax or R&D analysis.
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Software development and research-related costs can have specific U.S. tax treatment. The rules have changed in recent years, and companies should review whether costs are deductible, capitalized, amortized, or relevant to R&D credit analysis.
AI companies should not wait until year-end to identify development costs. The records are easier to support when the categories are built into the accounting process.
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The right structure depends on where the team works, where intellectual property is held, how customers are contracted, where investors expect the entity to sit, and how costs are shared between the U.S. entity and the foreign parent.
Accounting, tax, payroll, reporting, and intercompany flows should be considered before the structure is finalized.
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Investors often want to understand burn rate, runway, revenue model, customer traction, gross margin, cloud costs, R&D spend, headcount, pipeline, and budget vs. actuals.
For AI companies, reporting should explain both financial performance and the cost of building the technology.
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