AI & Machine Learning Company in Detroit

We price in three bands and quote fixed fee against a signed statement of work. A feasibility and data readiness engagement, meaning extract review, label quality assessment, leakage checks, a baseline model, and a written go or no go memo, runs USD 25,000 to 60,000 over four to eight weeks, and it is the single best money a Detroit buyer spends because it kills bad projects early. A production model with a real pipeline, meaning training code, registry, monitoring, serving, and documentation your model risk or quality function will accept, runs USD 90,000 to 260,000 depending on how many upstream systems have to be integrated and how heavy the validation regime is. A platform engagement covering multiple models, a feature store, CI for training pipelines, and an internal MLOps capability your team can operate runs USD 250,000 to 700,000 and up, usually phased over quarters. Edge computer vision on a plant floor sits at the upper end of the middle band because the hardware, lighting, fixturing, and integration work is real. The variable that moves an ML budget most is not the rate card, it is how many of these bands you actually need. Buyers who run the feasibility engagement first frequently stop there, either because the data cannot support the ask or because a simple baseline already clears the business threshold, and that is a cheaper outcome than a platform nobody asked for. We deliver from Edmonton and Chandigarh, and the overnight training window is part of why the schedule compresses.

Michigan has no comprehensive AI statute and no comprehensive consumer privacy statute. The proposed Michigan Personal Data Privacy Act, Senate Bill 359 of 2025, was introduced June 5 2025 and reported favorably out of committee in mid June 2025 but has not been enacted. Michigan's enacted AI laws are narrow: Public Act 264 and Public Act 265 of 2023, both approved November 30 2023 and effective February 13 2024, deal with artificial intelligence in political advertising and materially deceptive election media. Neither reaches a demand forecasting model. What actually binds your model is sector law. Credit and insurance models fall under ECOA and Regulation B, FCRA, and state insurance regulation supervised by the Michigan Department of Insurance and Financial Services. Clinical models fall under HIPAA and, where they run inside certified health IT, the ONC algorithm transparency requirements introduced by the Health Data, Technology, and Interoperability rule published January 9 2024 and effective February 8 2024. Employment related models attract Title VII and EEOC scrutiny. Safety related automotive functions attract ISO 26262 and ISO 21448 and NHTSA interest. We govern to the NIST AI Risk Management Framework because it is the framework that maps onto all of these and onto whatever Michigan eventually passes.

We build and validate perception and sensor fusion models, and we work alongside your functional safety and validation organization rather than pretending we can substitute for it. On the Michigan legal side, MCL 257.665 within the Michigan Vehicle Code, 1949 PA 300, governs research and testing of automated motor vehicles and automated driving systems on Michigan highways. Before testing begins the manufacturer of automated driving systems or upfitter must file proof of insurance satisfactory to the Secretary of State under chapter 31 of the Insurance Code. During testing the vehicle must be operated by an authorized person who can monitor performance and take control promptly, and if they cannot, the vehicle must be capable of achieving a minimal risk condition. When engaged, the automated driving system is treated as the driver for traffic law compliance. On the engineering side, the deliverables that matter are dataset curation strategy, a documented operational design domain, scenario coverage analysis against ISO 21448, failure mode analysis feeding the ISO 26262 work products, and an evaluation suite that reports per scenario rather than aggregate. We do the data and model engineering. Homologation, safety case ownership, and the Mcity or American Center for Mobility test campaign stay with you.

Far more than model training. The order of difficulty in a Detroit plant is usually lighting, then fixturing, then labels, then the model, then integration, then change management. Lighting and camera placement determine whether the defect is even visible, and getting that right on a line that cannot stop takes iteration with your maintenance and controls team. Labelling is expensive because defects are rare by definition, so we plan for synthetic augmentation, anomaly detection framings that learn normal rather than defect, and an active learning loop that keeps sending uncertain frames back to a human. Inference runs at the edge, on Jetson class hardware or industrial x86 with TensorRT or ONNX Runtime, because cycle time budgets are in tens of milliseconds and a round trip to a cloud region in Ohio is not viable. Integration means talking to the PLC and the MES over the protocol your controls engineers already use, and it means deciding what the system does when it is unsure, which is almost always flag for human review rather than reject. Then there is drift: a new supplier's steel, a seasonal humidity change, or a relamping event can move the distribution, so monitoring and a retraining trigger are part of the build.

Model risk management is the deliverable, and the model is a component of it. We build to the structure your model risk function and your regulators expect: a documented development record covering data sources, exclusions, and treatment of missing values, a rationale for the model form, benchmark comparison against a simpler challenger, and stability testing over time. Reason codes are mapped explicitly so that adverse action notices under ECOA and Regulation B state principal reasons that trace back to the model's actual drivers, not a post hoc narrative. Where a consumer report is used, FCRA disclosure obligations are wired into the notice path. Fair lending testing includes proxy based disparate impact analysis across protected classes, tested at the model level and again at the policy cutoff level, because a fair model behind an unfair cutoff is still an unfair outcome. Monitoring covers population stability, score distribution shift, and approval rate by segment, with defined thresholds that trigger review. GLBA safeguards apply to the data path, including the FTC Safeguards Rule breach notification amendment effective May 13 2024. We do not sign off on your fair lending posture. We build the evidence your second line reviews.

You do, and we design the handover so that is a real statement rather than a polite one. Every model ships with a reproducible training pipeline that runs from raw extract to registered artifact in your environment with one command, not a notebook someone ran on a laptop. Code lives in your repository. The registry, whether MLflow, SageMaker Model Registry, Vertex AI Model Registry, or Azure ML, is yours. Monitoring dashboards are built in whatever your team already watches rather than a tool they will stop opening in a month. The runbook names the retraining trigger explicitly: a drift threshold, a performance floor, a calendar cadence, or an upstream schema change, and it names the human accountable for each. We run a knowledge transfer series with your data engineers and analysts during the last sprint, recorded, with the recordings handed over. Where you want ongoing support we scope it separately as an explicit retainer rather than baking dependency into the architecture. The failure mode we are designing against is common in this market: a model built by an outside team, deployed, then quietly degrading for eighteen months because nobody owned the retraining decision.