Generative AI Engineering Services in New York | IoTree Minds

Enterprise AI, from strategy to production

New York, NY · Generative AI Engineering

Generative AI Engineering for New York businesses

LLM applications for New York teams that answer from your own documents, cite their sources and are tested before anyone relies on them.

Generative AI Engineering in New York

Generative AI for a city that runs on documents

New York runs on regulated, document-heavy work: onboarding clients, pricing risk, handling claims and managing leases. It is where AI has the most work to do, and the highest bar for control.

Where it fits

What New York teams build with it

Legal and professional services

Contract review assistant

Find and compare clauses across large contract sets.

  1. 1Extracts key clauses
  2. 2Flags deviations from your playbook
  3. 3Links each finding to its source
Financial services

Research and policy assistant

Cited answers from research, policies and filings.

  1. 1Indexes research and policies
  2. 2Answers with citations
  3. 3Respects existing access rights
Media and advertising

On-brand content drafting

First drafts in your house style, reviewed by people.

  1. 1Learns tone from approved work
  2. 2Drafts variants per channel
  3. 3Routes to editors for sign-off

Built for New York rules

Regulations we design around

Banks, insurers, DFS-licensed firms

Part 500

Access controls, MFA and audit trails on every system AI touches.

Anyone holding New Yorkers' data

SHIELD Act

Reasonable safeguards for the private data AI systems read and write.

Hiring for NYC roles

Local Law 144

Bias audit and candidate notice for tools that score or rank candidates.

Healthcare providers, plans and their vendors

HIPAA

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

Working together · Eastern Time

Working with us from New York

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 New York teams

Ask us something else
Can we use generative AI with data covered by the SHIELD Act?

Yes, with safeguards. We keep private data inside systems you control, limit what the model can see by role, and log access, which are the kinds of reasonable safeguards the SHIELD Act expects.

Is AI-drafted content safe for regulated New York firms?

It can be, with review built in. Drafts go through your compliance or legal reviewers before anything is published, and the tool keeps a record of who approved what.

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.