What does LLM development include?

LLM development covers designing and building applications on top of large language models, such as assistants, summarization tools and chat interfaces, connecting them to your systems through APIs, testing the quality of their responses, and deploying them with guardrails. HATZS also advises on model and platform selection before the build.

What is RAG and when should we use it?

RAG (retrieval-augmented generation) lets a language model look up information in your own documents and data before it answers, so responses are grounded in your sources instead of only the model's general training. It fits cases where answers must reflect company knowledge, such as policies, product information or internal documentation.

How is a RAG chatbot different from a generic chatbot?

A generic chatbot answers from what the underlying model already knows. A RAG chatbot retrieves relevant material from your knowledge sources first and answers from it, which makes responses more specific to your business and easier to check against a source. HATZS has built chatbots that run in production for platforms such as DealerIQ and BrokerOS.

How does generative AI consulting relate to LLM development?

Consulting comes first when you are not sure where to start: we assess readiness, prioritize use cases and plan governance and adoption. LLM development is the build that follows. Some teams need both from the same partner; others only need one of them.

Can you build on our existing systems and data?

Yes. We connect through the APIs and data layers your systems already expose rather than asking you to replace them.

LLM DEVELOPMENT & GENERATIVE AI CONSULTING

LLM development company

HATZS is an LLM development company that builds custom LLM-powered applications, connects them to your company knowledge with RAG, and plans adoption with generative AI consulting.

Explore AI Solutions→

LLM DEVELOPMENT & GENERATIVE AI CONSULTING

HATZS is an LLM development company that builds custom LLM-powered applications, connects them to your company knowledge with RAG, and plans adoption with generative AI consulting.

Explore AI Solutions→

LLM and RAG projects at a glance

Always-On AI Engagement

24/7

Continuous AI customer engagement on the Befer CRM platform.

Consulting Stages

Readiness, use-case discovery, solution planning, risk and governance review, and adoption strategy.

Project Pitfalls Addressed

Ungrounded answers, ungoverned AI tools and unclear ROI are the three problems this service starts from.

From generative AI strategy to a working LLM application

We help you decide what to build, then build it on your own knowledge.

GENERATIVE AI CONSULTING

Strategy before build

A readiness assessment, use-case prioritization and risk review show which LLM projects are worth starting first.

RAG SYSTEMS

Grounded in your documents

Retrieval connects the language model to your company knowledge, so answers come from your own sources.

LLM APPLICATIONS

Built for production

Custom applications run on APIs and services that connect to the systems your teams already use.

RAG development services for company knowledge

Knowledge source connection

Connecting the model to documents, knowledge bases and internal data so it can look up information before it answers.

Retrieval pipelines

Preparing your content and building the retrieval step that finds the right material for each question.

LLM application development

Custom assistants, summarization tools and chat interfaces built on large language models and connected through APIs.

Production AI chatbots

Chatbots that run live for customers and staff, as on the DealerIQ and BrokerOS platforms. Our AI agent services cover conversational agents in more depth.

Model and platform selection

Guidance on which LLMs and AI platforms fit your case, with vendor and architecture evaluation and cost-aware deployment.

Quality checks and guardrails

Response testing and guardrails that keep answers accurate and inside the scope you set.

How an LLM project runs, phase by phase

Phase 1

Readiness review

Deliverables: business process evaluation, AI maturity analysis and a shortlist of use cases.

Readiness assessment

Use case discovery

ROI focus

Phase 2

Solution design

Deliverables: RAG and LLM architecture recommendations, model selection guidance and an implementation plan.

Architecture

Model selection

Implementation plan

Phase 3

LLM build

Deliverables: a working LLM application connected to your knowledge sources and systems.

LLM application

RAG pipeline

API integration

Phase 4

Governance and adoption

Deliverables: responsible AI framework, security assessment and team enablement plans.

Responsible AI

Security review

Team enablement

Use cases for LLMs and RAG across industries

How LLM applications and RAG typically support teams in each industry.

Give brokers and policyholders fast, sourced answers from policy and service content.

Answering coverage and policy questions from approved documents.

Summarizing claim files for adjusters.

Drafting routine broker and client correspondence.

Guiding customers through quotes in a chat interface.

Searching underwriting guidelines in plain language.

Avoid the Common LLM and RAG Project Pitfalls

Challenge

Your LLM gives confident answers that don't come from your own documents.

Solution

We'll connect it to your company knowledge with RAG, so answers are grounded in your own sources.

Ground Your LLM→

Challenge

Teams are experimenting with AI tools without governance.

Solution

We'll establish policies, frameworks, and responsible AI practices for safe adoption.

Establish Governance→

Challenge

AI investments aren't generating measurable ROI because there is no clear roadmap.

Solution

We'll identify the highest-value use cases and prioritize initiatives that create operational efficiency and business impact.

Plan Your AI Roadmap→

LLM applications
grounded in your knowledge.

From generative AI strategy to RAG-powered applications, built on the systems you already use.

LLM APPLICATIONS

Assistants and tools built for your use case.

Custom applications on large language models, connected to your systems through APIs.

RAG SYSTEMS

Answers that come from your documents.

Retrieval connects the model to your knowledge sources before it responds.

CHATBOTS

Conversational interfaces that run live.

Chatbots in production for customers and staff, as on the DealerIQ and BrokerOS platforms.

READINESS & USE CASES

Know where to start.

Readiness assessment and use-case discovery focus effort on the opportunities worth pursuing.

RESPONSIBLE AI

Governance from the first prototype.

Responsible AI frameworks, compliance planning and security assessment shape each build.

ADOPTION

A team ready to use it.

Enablement plans and change guidance help people adopt the new tools.

Generative AI consulting, from readiness to adoption

RAG & LLM Architecture Planning

Recommend RAG and LLM architectures and guide selection.

Our Services Include:

→ RAG and LLM architecture recommendations
→ Model selection guidance
→ Implementation planning

LLM application development: what we deliver, from strategy to adoption

Evaluation of current business processes
Identification of high-impact AI opportunities
Clear implementation roadmap

Focus on measurable ROI opportunities
Business-aligned AI initiatives
Risk and feasibility analysis

Governance and adoption guidelines
Responsible AI implementation practices
Long-term scalability planning

Recommendations for LLMs and AI platforms
Vendor and architecture evaluation
Cost-effective deployment strategies

Team training and AI adoption planning
Internal stakeholder alignment
Continuous optimization recommendations

OUR LLM PROJECT PROCESS

STEP 1

Assess and prioritize

AI maturity analysisUse case discoveryOpportunity identification

STEP 2

Architect and build

RAG architectureModel selectionImplementation planning

STEP 3

Govern and adopt

Responsible AI frameworkSecurity assessmentTeam enablement

Ready to build with LLMs?

Share the questions your team or customers ask most, and we will outline how an LLM application with RAG could answer them from your own knowledge.

Talk to our team about your LLM project.

View full case study

AWARDS & RECOGNITIONS

A multi-award-winning software development
partner

ENGAGEMENT MODELS

Build around the outcome. Not the org chart.

Tell us what you're trying to achieve. We bring together product, design, engineering and AI into the right team — from first experiment to continuous product evolution.