Generative AI Engineering Services in Austin | IoTree Minds

Enterprise AI, from strategy to production

Austin, TX · Generative AI Engineering

Generative AI Engineering for Austin businesses

Generative AI for Austin teams: in-product assistants, engineering search and public service answers, grounded in your data and tested before release.

Generative AI Engineering in Austin

LLM features built to ship

Austin pairs fast-growing software companies with semiconductor fabs, state government and a growing health sector. Texas now has its own AI governance law on top of its privacy act, so AI plans here need to account for both.

Where it fits

What Austin teams build with it

SaaS and software

In-product copilot

Help users get more done inside your product.

  1. 1Grounded in each customer's data
  2. 2Takes allowed actions
  3. 3Measures quality and cost
Semiconductors and hardware

Engineering knowledge search

Find past failure analyses, specs and procedures.

  1. 1Indexes reports and specs
  2. 2Answers with references
  3. 3Respects export controls
Government and public sector

Public information assistant

Clear answers from official guidance, with sources.

  1. 1Indexes official guidance
  2. 2Answers in plain language
  3. 3Discloses that it is AI

Built for Texas rules

Regulations we design around

Businesses handling Texans' data

TDPSA

Consent for sensitive data and assessments for high-risk processing.

Businesses using AI in Texas

TRAIGA

Prohibited AI uses, with added duties for government AI.

Semiconductor and hardware makers

Export controls

Controlled technical data kept away from tools and people it may not reach.

Healthcare providers, plans and their vendors

HIPAA

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

Working together · Central Time

Working with us from Austin

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

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

Ask us something else
Can generative AI run in our own cloud account?

Yes. We can host models or use managed model services inside your cloud account, keeping data within your security controls.

How do you keep answers accurate?

Answers are grounded in your approved sources, linked to them, and tested against a set of real questions before launch and after every change.

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.