Generative AI Engineering Services in Salt Lake City | IoTree Minds

Enterprise AI, from strategy to production

Salt Lake City, UT · Generative AI Engineering

Generative AI Engineering for Salt Lake City businesses

Generative AI for Utah teams: in-product assistants, support knowledge and product content, grounded in your data and disclosed to users.

Generative AI Engineering in Salt Lake City

LLM features for growing products

Salt Lake City and the Silicon Slopes corridor are home to fast-growing software companies, financial services operations, integrated health systems and a big distribution hub. Utah was also early to regulate AI, with rules on telling people when they are dealing with generative AI.

Where it fits

What Salt Lake City teams build with it

SaaS and software

In-product assistant

Users get help without leaving the product.

  1. 1Grounded in each customer's data
  2. 2Clearly labelled as AI
  3. 3Measures quality and cost
Direct selling and e-commerce

Product content

Descriptions and guides drafted from product data.

  1. 1Drafts from product attributes
  2. 2Checks claims against approved copy
  3. 3Queues for review

Built for Utah rules

Regulations we design around

Businesses using generative AI with consumers

Utah AI Policy Act

Disclosure when people are talking to generative AI.

Businesses handling Utahns' data

UCPA

Notice and opt-outs for targeted advertising and sensitive data.

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.

Working together · Mountain Time

Working with us from Salt Lake City

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

  1. 8–10 AM MT Live call Review progress and make decisions together
  2. Your working day Your feedback Your team tests and comments in writing
  3. Overnight MT 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 Salt Lake City teams

Ask us something else
How do you stop product content making unapproved claims?

Drafts only use your approved product claims, and a checker flags anything new for review before publishing.

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.