Generative AI Engineering Services in Detroit | IoTree Minds

Enterprise AI, from strategy to production

Detroit, MI · Generative AI Engineering

Generative AI Engineering for Detroit businesses

Generative AI for Detroit engineers: search across specs, lessons learned and service information, grounded in your documents.

Generative AI Engineering in Detroit

Engineering knowledge at your fingertips

Detroit is still the centre of the US auto industry: automakers, a deep supplier base, EV and mobility engineering, plus large mortgage and fintech companies and health systems. Engineering, quality and supplier data are where AI earns its keep here.

Where it fits

What Detroit teams build with it

Automotive OEMs

Lessons learned search

Find past issues and fixes before repeating them.

  1. 1Indexes 8D reports and lessons
  2. 2Answers with references
  3. 3Suggests related issues
Mortgage and fintech

Loan document review

Key data pulled from loan documents for checking.

  1. 1Reads loan documents
  2. 2Extracts key fields
  3. 3Links values to sources

Built for Michigan rules

Regulations we design around

Banks, lenders, fintechs

GLBA Safeguards

A written security program covering every system that handles customer financial data.

Healthcare providers, plans and their vendors

HIPAA

Safeguards and a business associate agreement for anything touching patient data.

Anyone holding Michigan residents' data

Michigan breach law

Security safeguards and prompt notice if personal data leaks.

Working together · Eastern Time

Working with us from Detroit

Your morning is our evening, so we meet live at the start of your day and build while you sleep.

  1. 8–10 AM ET Live call Review progress and make decisions together
  2. Your working day Your feedback Your team tests and comments in writing
  3. Overnight ET We build Progress is ready for your next morning

How we work

From first conversation to production

  1. 1

    Define quality

    We agree what a good answer looks like and build a test set.

  2. 2

    Ground the model

    We connect trusted sources and design retrieval around them.

  3. 3

    Evaluate

    We measure accuracy, safety and cost, and iterate until it holds up.

  4. 4

    Ship and monitor

    We launch with monitoring and feedback loops that keep it improving.

FAQ

Questions from Detroit teams

Ask us something else
Can supplier data be kept separate?

Yes. Answers are filtered by each user's access, so confidential supplier or program data is only visible to the right people.

What is RAG and why does it matter?

Retrieval-augmented generation (RAG) finds the most relevant passages in your own content and gives them to the language model with the question, so answers are grounded in your data and can cite their sources.

Do we need to fine-tune a model?

Often not. Good retrieval and prompt design solve most enterprise use cases. We recommend fine-tuning only when evaluations show it clearly improves quality, cost or speed.

How do you keep our data private?

We design around your security requirements: private deployments where needed, access controls applied to retrieval, no training on your data without consent, and full logging.