Generative AI Development & LLM Engineering Services - IoTree Minds

Enterprise AI, from strategy to production

03 · Generative AI Engineering

Generative AI your users can trust

We engineer LLM applications that answer from your own knowledge, show their sources, and are tested for accuracy and safety before anyone relies on them.

Talk to our team What's included

Why it matters

From impressive demo to dependable product

Getting a large language model to produce a good answer once is easy. Getting it right every day, on your data, for thousands of users, is an engineering problem.

We treat generative AI like any other critical software: grounded in trusted sources, measured with proper evaluations, secured, and monitored after launch.

What's included

Generative AI Engineering services

01

LLM Application Development

Assistants, search experiences and content tools built on leading language models and fitted into your products and workflows.

  • Model selection for cost and quality
  • Prompt and tool design
  • Web, mobile and in-app experiences
02

RAG & Knowledge Systems

Retrieval-augmented generation that grounds answers in your documents, databases and policies, with sources users can check.

  • Document ingestion and chunking
  • Vector and hybrid search
  • Access control on every answer
03

Model Evaluation & Fine-Tuning

Test sets, automated evaluations and fine-tuning where it helps, so you know how well the system works before and after launch.

  • Accuracy and hallucination testing
  • Safety and bias checks
  • Fine-tuning for domain language

Where it fits

Where teams put it to work

Enterprise knowledge assistant

Answers from policies, manuals and past work, with citations.

Document drafting

First drafts of proposals, reports and responses in your house style.

Contract and document review

Summarise, compare and flag clauses across large document sets.

Customer-facing assistants

Accurate, on-brand answers on your website or app.

Search across silos

One question, answered from many systems at once.

Content localisation

Adapt content across languages and markets at scale.

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 about Generative AI Engineering

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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.

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Other services

All services

AI Strategy & Consulting

We find where AI actually pays back in your business, and what it takes to get there.

Learn more

Agentic AI & Automation

AI agents that do real work inside the systems and processes you already run.

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Data & AI Platforms

The data foundations, pipelines and governance that keep AI reliable in production.

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Application Development

The web, mobile and business applications that put AI in front of your users and teams.

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Let's talk

Not sure where AI fits?

We help you navigate Enterprise AI, from first use case to production.

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