AI Solution

Custom LLM Development Services
for Perth, Melbourne, Sydney, Brisbane businesses.

Custom LLM development services and llm application development for Australian businesses. Purpose-built AI applications powered by large language models, built around your specific workflows, connected to your data, and deployed in your environment. Not another ChatGPT experiment.

Quoting assistants, document reviewers, knowledge search, compliance tools, customer advisers. Purpose-built. Guardrailed. Secure.

Book a 20-min scoping call Call 0425 531 127

AU-wide Perth-based · servicing Australia
Purpose-built Not a wrapped chatbot
Fixed Price scopes, no surprises
100% Your data, your infra

Week 0

Custom LLM App Audit

15-minute scoping call. We map your current process, the documents/data the AI will work with, and the systems the AI output must land in. Fixed-price quote inside 48 hours.
Weeks 1–N

Build & Parallel-Run

AI system built and parallel-run against your live operations for two weeks. Every extraction, decision and integration validated before production. Human-in-the-loop where confidence is lower.
Cutover

Go-Live & Handover

Production cutover on a planned window. Team training, monitoring active, 30 days post-launch support. Documentation, prompts and source code handed over in full.

What We Build

Custom LLM development services, by app category

Five categories of llm application development we ship most often. Most businesses start with one workflow and expand from there. Pick a tab to see how yours would take shape.

Workflow-Specific AI Applications

Custom AI apps built around your actual business workflow. A quoting assistant that reads specs and generates estimates using your pricing. A compliance checker that reviews documents against your regulatory framework. A customer response tool that drafts replies using your knowledge base and tone.

These are not chat windows with a text box. They are purpose-built interfaces with the right fields, the right buttons, the right output format. Designed for the specific task your team does every day.

AI-Powered Knowledge & Search Applications

Applications that let your team search, query, and get answers from your business information: policies, procedures, product catalogues, project histories, technical manuals, compliance documents. Ask in plain English. Get specific answers with source references.

This is RAG-powered search done properly. Not just keyword matching. Real comprehension. "What was our warranty claim rate for Product X in Q3 last year?" gets a specific answer pulled from your data, not a vague summary.

Document Generation & Processing Apps

Custom applications that generate, review, or transform documents using AI. A proposal generator that creates tailored proposals from CRM data and a template library. A contract review tool that highlights risk clauses and missing terms. A report builder that pulls data from multiple systems and writes the narrative.

Not template mail-merge. The AI understands context. A proposal for a mining company reads differently from one for a healthcare provider, even when the service is the same.

AI Classification & Triage Applications

Applications that read incoming content (emails, forms, tickets, documents), classify it, extract key data, and route or action it. Support ticket triage that categorises by issue, priority, and team. Insurance claim classification that assesses type and complexity. Lead scoring that evaluates enquiry quality.

The classification model is trained on your categories, your patterns, your business rules. It does not use generic labels. It uses yours.

Customer-Facing AI Applications

AI applications for your customers and clients, not just internal staff. A product adviser that helps customers find the right product based on their needs. A self-service portal where customers get answers from your knowledge base. An onboarding assistant that walks new users through setup.

Branded to match your business. Guardrailed to stay on-topic and accurate. Connected to your product data, pricing, and availability. The customer gets immediate, accurate help. Your team handles the cases that genuinely need a human.

Custom AI quoting tool with purpose-built interface for a specific business workflow

AI document generation app producing a tailored business proposal from CRM data

AI classification app triaging support tickets by type, priority, and team assignment

Customer-facing AI product adviser helping users find the right solution

Pricing

How we price custom llm development services

No hourly billing, no vague estimates. Here is how private llm services and enterprise llm services projects are scoped, from a free 15-minute call to a fixed-price proposal.

Free 15-minute call

Tell us what problem you are trying to solve. We will give you an honest answer on whether AI is the right fit and what it would involve.

Fixed-price quote

After discovery, you get a fixed price for the project. No hourly billing that spirals. You know exactly what it costs before we build.

Staged delivery

PoC first, then production build in phases. You see real value early, give feedback, and pay in milestones.

Why HELLO PEOPLE

Custom LLM app development
HELLO PEOPLE vs the alternatives

ChatGPT Enterprise, no-code AI tools and an in-house AI team all solve pieces of the problem. Here is how HELLO PEOPLE compares on the things that actually matter to an AU SMB.

Concern	HELLO PEOPLE	ChatGPT / Off-shelf	No-code AI (Zapier)	In-house AI hire
Fit to your LLM use case + budget	Built around your workflow & spend	Generic chat, no fit	Trigger-based, brittle logic	Depends on the hire
LLM app live in 8-14 weeks	Full app, tested end to end	Live in minutes, no app	Weeks of DIY glue code	Months to hire and train
AU privacy + on-prem/private cloud fit	AU-hosted or your own tenancy	US-hosted, no private option	Depends on your setup	Depends on the hire
Ongoing cost	Fixed price, one-time	Per seat/token, per month	Per-run + prompt engineering	Salary + super + overhead
When base models change	We patch, no extra charge	You rewrite the prompt	You rebuild the flow	Internal team owns it
Direct access to the builder	Email Kasun, get Kasun	OpenAI support tier	Community forum	Internal AI team
App code + fine-tuning data ownership	You own it, nothing sits with us	Vendor holds prompts & logs	Vendor holds middleware	You own it internally

Mining operations and field work — Case Study

Case Study

AI-powered document search across 4,000+ mining procedures

We built a RAG-powered search system for a mining company. Workers ask questions in plain English and get accurate answers from thousands of safety and procedure documents.

Read the full case study

4,000+ Documents searchable

< 5s Answer time

92% Query resolution rate

We needed a quoting tool that understood our product range and pricing rules. Off-the-shelf AI could not do it. HELLO PEOPLE built us a custom app that reads the spec, pulls pricing from our catalogue, and generates a first-draft quote in under two minutes. Our estimators still review everything, but they start from 80% done instead of a blank page.

General Manager Perth industrial supplies distributor · 45+ staff

Business Impact

What custom llm app development australia actually changes

Real workflow fit, faster knowledge work, data security your CEO can defend, competitive advantage your competitors cannot copy. Here is what a fine-tuned llm development project really delivers, not what a brochure promises.

Real Workflow Fit Faster Output Data Security Competitive Advantage

Custom AI estimating tool built to match a construction company workflow

AI that matches how your team actually works

The biggest reason AI experiments fail is not the technology. It is the workflow gap. A general-purpose chat window does not match how your estimator prices a job, how your compliance officer reviews a document, or how your account manager qualifies a lead.

A custom LLM app closes that gap. The interface matches the task. The data connections match the sources your team already uses. The output format matches what they need to produce. The AI is not an extra tool. It is the tool.

One construction estimator went from "ChatGPT is interesting but I cannot use it for real quotes" to "The AI gives me a first-pass estimate in 2 minutes that used to take 45." Same AI model underneath. Completely different interface on top.

AI generating a first-draft proposal in minutes for human review and refinement

Hours of knowledge work compressed into minutes

A proposal that takes 3 hours to draft. A compliance review that takes 90 minutes to complete. A customer email that takes 15 minutes to research and write. These are tasks where humans add real value: understanding context, making judgements, applying expertise.

But a lot of the time is spent on the mechanical parts: finding the relevant data, assembling it, writing the first draft, checking the formatting. The AI handles the mechanical parts. Your team handles the judgement parts.

The result is not lower quality. It is the same quality in a fraction of the time. A 3-hour proposal becomes 30 minutes of AI generation plus 30 minutes of human review and refinement.

Secure AI deployment architecture diagram showing Australian data residency

AI adoption without the data risk

The single biggest blocker for AI adoption in Australian businesses is data security. "Where does our data go? Who can see it? Does it train the model? Does it leave Australia?" These are reasonable questions, and public ChatGPT does not answer them well.

A custom LLM app answers them completely. Deployed in your cloud (Azure Sydney, AWS Sydney) or on-premises. Data encrypted in transit and at rest. User permissions enforced. Audit logs on every query. The model does not train on your data.

This is how you get legal, IT, and the CEO to say yes. Not by arguing that ChatGPT is safe, but by deploying AI in a way that is genuinely secure and demonstrably controlled.

Custom AI application providing competitive advantage through proprietary data and workflows

AI tools your competitors cannot replicate

Your competitor can sign up for ChatGPT too. They can use the same no-code AI platforms you can. That is not an advantage. That is table stakes.

A custom LLM app built on your data, your workflows, and your business logic is different. It knows your products. It follows your rules. It reflects your expertise. It is trained on the patterns and knowledge your competitors do not have.

This is where AI becomes a genuine business advantage. Not because you have better AI, but because you have better data, better workflow integration, and better deployment.

How We Build It

How our custom llm development services actually run

Every project is different, but the six-step structure stays the same. Use case definition, data and architecture, prototype and test, build and guardrail, deploy and train, then optimise and evolve once you are live.

Use Case Definition

We define the specific workflow the app will serve: what users do today, what data they need, what output they produce, and where time is wasted. A clear scope, not a vague AI exploration.

Data & Architecture

We map the data sources (CRM, databases, documents, APIs) and design the architecture. Hosting environment, model selection, security model, access controls. Blueprint before building.

Prototype & Test

We build a working prototype connected to your real data. Your team tests it on actual tasks from their day. We measure accuracy, speed, and usability. Adjustments before we commit to the full build.

Build & Guardrail

Full application development: UI, backend, AI pipeline, data connections, guardrails, and access controls. System prompts tuned with your team. Output validation configured. Security tested.

Deploy & Train

Deployed in your environment. Team trained on the application. Documentation provided. Usage monitoring live from day one. First-week check to catch any edge cases.

Optimise & Evolve

Monthly reviews. Prompt refinement based on real usage. New data sources added. Model swaps when improvements justify them. The app gets better the more your team uses it.

FAQs

Common questions about custom LLM app development

What do custom llm development services actually deliver?

A purpose-built AI application connected to your data, hosted in your environment. You get the frontend, backend, LLM pipeline, RAG data connections, guardrails, access controls, dashboards, monitoring and full source code. Not a chat window. A production tool with the fields, buttons and outputs designed for your specific workflow.

How is custom llm app development different from using ChatGPT directly?

Three differences. The app is connected to your business data, so it answers from facts, not general knowledge. The interface is designed for the specific task, not a general chat window. And it is deployed securely in your environment with access controls and audit trails.

How much do custom llm app development australia projects cost?

Fixed price after scoping. A focused single-workflow app sits at the lower end. Multi-workflow apps with complex data connections and multiple user roles are larger. Book a free 15-minute call, we run paid discovery, then you get a fixed quote before build starts. Model API fees and optional support are scoped inside the same quote.

How long do llm application development projects take?

About 6 to 10 weeks for a focused single-workflow app (quoting assistant, document reviewer) including prototype, testing and deployment. 10 to 16 weeks for multi-workflow apps or apps needing complex data integrations. Prototype ships early so your team can test on real work before we commit to full build.

Do private llm services support open-source models like Llama or Mistral?

Yes. We build model-agnostic applications. If data privacy or cost favours an open-source model like Llama, Mistral or Qwen, we deploy it on your own infrastructure. The application architecture stays the same regardless of the underlying model. Swap later without rebuilding.

What about fine-tuned llm development on our data?

Yes. LoRA and adapter fine-tuning on your historical content for tone, format and domain terminology. Full fine-tuning where the use case justifies it. Most projects start with RAG (which works well) and only fine-tune when RAG hits its limits, since fine-tuning is expensive to maintain.

Where does an enterprise llm services deployment run?

Your choice. Azure OpenAI (Sydney), AWS Bedrock (Sydney), GCP, or on-premises. Air-gapped for high-security workloads. Australian data residency by default. VPC and private-link networking. Data encrypted at rest and in transit. Compliance-ready logs for ISO 27001, IRAP or SOC 2.

What if the AI gives wrong answers?

Multiple layers of control. RAG grounds answers in your real data. System prompts constrain the AI to its domain. Output validation catches hallucinations. Confidence scoring flags uncertain responses. When the AI is not sure, it says so, rather than inventing an answer.

Custom LLM Development Services
for Perth, Melbourne, Sydney, Brisbane businesses.

Your custom llm development services project, end to end

Custom LLM App Audit

Build & Parallel-Run

Go-Live & Handover