AI Agent Development Company in Detroit

Scope drives everything, so we quote ranges and then a fixed fee against a signed statement of work. A scoped proof of concept, meaning one agent, three to five tools, an evaluation suite, an action log dashboard, and a hosted demo behind your SSO, typically runs USD 30,000 to 70,000 across six to ten weeks. A production agent against real systems of record, such as supplier shortage triage against your ERP and MES, prior authorization drafting against a payer portal, or document intake for a mortgage operation, typically runs USD 110,000 to 300,000 including guardrails, the human approval console, observability, and the compliance documentation pack. A multi agent platform with shared memory, custom evaluators, and deep integration into SAP, Epic, ServiceNow, or a dealer management system runs USD 300,000 to 800,000 and up, usually phased. Detroit senior engineering rates sit below San Francisco and New York at equivalent seniority and track closer to Chicago and Minneapolis than to the coasts, though the gap moves with the market and we quote against your scope rather than a benchmark. Defense work costs more because the self hosted, no outbound inference architecture removes the cheap option. We deliver from Edmonton and Chandigarh, which is part of why the number lands where it does.

Correct, and it is worth being precise about it. Michigan has not enacted a comprehensive consumer data privacy statute. Senate Bill 359 of 2025, the proposed Michigan Personal Data Privacy Act sponsored by Senator Rosemary Bayer, was introduced on June 5 2025, reported favorably out of the Senate Finance, Insurance and Consumer Protection committee in mid June 2025, and referred to the Committee of the Whole, where it has not advanced to law. Its predecessor, Senate Bill 659 of 2023, also failed to be enacted. So what governs you is a different stack. Section 5 of the FTC Act reaches unfair and deceptive practices, including claims you make about what an agent does. The Michigan Consumer Protection Act, 1976 PA 331, MCL 445.901 and following, covers deceptive practices in trade and commerce at the state level. The Michigan Identity Theft Protection Act, 2004 PA 452, requires breach notice to Michigan residents without unreasonable delay under MCL 445.72. The Social Security Number Privacy Act, 2004 PA 454, effective March 1 2005, requires a written privacy policy for anyone handling SSNs in the ordinary course of business. Layer sector rules on top: HIPAA, GLBA, ECOA, FCRA, CMMC. We build to that stack and note in the risk file that a Michigan statute may land later, so the controls do not need a rebuild if it does.

Michigan has no comprehensive AI governance act. The AI statutes actually on the books are narrow and election focused. Public Act 264 of 2023, enrolled House Bill 5143, was approved by the Governor on November 30 2023 and took effect February 13 2024, adding a definition of artificial intelligence to the Michigan Campaign Finance Act at MCL 169.202. Public Act 265 of 2023, enrolled House Bill 5144, was approved on the same date with the same February 13 2024 effective date, and adds MCL 168.932f prohibiting the distribution of materially deceptive media in connection with elections, with disclosure requirements where AI generated content is used. Unless you are building political communications tooling, those acts will not touch your agent directly. What does apply is the general body of law that does not care whether a model was involved: consumer protection, employment discrimination law, lending and insurance rules, professional licensure, and contract. We therefore govern Detroit agent builds against the NIST AI Risk Management Framework rather than a Michigan checklist, because NIST AI RMF is the artifact your counsel, your customers, and your auditors will actually recognize, and it maps cleanly onto whatever statute Michigan eventually passes.

Yes, and the architecture is different from everything else we do in this market. The Detroit Arsenal in Warren hosts the Army DEVCOM Ground Vehicle Systems Center and TACOM, and the supplier base around it, including General Dynamics Land Systems in Sterling Heights and American Rheinmetall Vehicles, handles controlled unclassified information. The CMMC Program rule at 32 CFR Part 170 was published October 15 2024 and took effect December 16 2024. The acquisition side, the DFARS rule that puts CMMC requirements into contracts, was published September 10 2025 and took effect November 10 2025, phasing in through solicitations. For a supplier at Level 2, that means NIST SP 800-171 controls with a third party assessment. Practically, we run the entire agent stack inside your assessed boundary: open weight models such as Llama or Qwen on your own GPU capacity, orchestration inside your VPC or on premise, retrieval against internal repositories only, and zero outbound calls to hosted model APIs. Guardrail classifiers run locally too. Our Chandigarh engineers work on non controlled layers behind clean interfaces where US person constraints apply. We are not your assessor and we do not claim to be, but we build to the boundary your assessor signs.

Four patterns hold up in production. Shortage and constraint triage: the agent reads MRP exception reports, supplier confirmations, and inbound logistics status, then assembles a case file for the buyer with the root cause, the affected builds, and the two or three options, and it does not reschedule anything by itself. Change notice and PPAP chasing: the agent tracks engineering change notices through the supplier base, drafts the follow ups, and escalates the ones aging past threshold. Customs and cross border exception handling, which matters more in Detroit than almost anywhere given the Ambassador Bridge and the Gordie Howe International Bridge, where the agent reconciles broker documents, commercial invoices, and carrier status against a shipment record and flags mismatches before a truck is sitting at the crossing. Warranty and field quality triage, where the agent reads dealer repair narratives and telematics context, clusters them, and routes candidate issues to the right engineering owner. What does not work is letting an agent write to a production schedule, release a supplier payment, or close a quality hold. Those stay behind a named human approver with a structured payload and a logged decision.

The rule we hold to is that a language model may draft, summarize, retrieve, and route, but it does not decide anything a regulator would call a credit decision. Under the Equal Credit Opportunity Act and Regulation B, an applicant who is denied credit or gets less favorable terms is entitled to specific principal reasons, and those reasons have to trace back to the actual decisioning logic. A generated explanation that sounds plausible but does not correspond to the model that made the call is a compliance problem, not a UX improvement. So we architect the agent around the decision engine rather than through it: the agent pulls the reason codes from the governed system and renders them, it never authors them. Adverse action timing, FCRA disclosures where a consumer report was used, and record retention all stay in the system of record. On the operations side the agent is genuinely useful for document intake and classification, condition clearing checklists, servicing correspondence drafts, complaint triage, and quality assurance sampling of call transcripts. GLBA safeguards apply to the whole path, including the FTC Safeguards Rule breach notification amendment that took effect May 13 2024, so incident detection and reporting hooks are part of the build, not a follow on project.