Custom LLM App Development
for Perth, Melbourne, Sydney, Brisbane businesses.
Custom 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. A production tool your team uses every day.
Quoting assistants, document reviewers, knowledge search, compliance tools, customer advisers. Purpose-built. Guardrailed. Secure.
You have an AI use case — you need someone to build it properly
Your team tried ChatGPT. It is useful for generic tasks. But for your actual business workflow — pricing a job, reviewing a contract, answering questions about your specific products — the generic chat window falls short. It does not know your data, it does not follow your process, and it is not connected to your systems.
Off-the-shelf AI tools get you 70% of the way. That last 30% — your pricing logic, your compliance rules, your product catalogue, your brand voice, your data security requirements — is the part that matters. And it is the part only a custom-built application can handle.
HELLO PEOPLE builds custom LLM applications for Australian businesses. Not experiments or proof-of-concepts that sit in a folder. Production-grade apps connected to your real data, deployed in your environment, used by your team daily.
The problems that lead businesses to custom AI apps
If you have tried generic AI tools and hit the wall of 'almost but not quite' — you are ready for a custom build.
ChatGPT Demos That Go Nowhere
Someone on the team built a proof-of-concept with the ChatGPT API over a weekend. It looked impressive. It answered questions. Then the hard questions came: How do we connect it to our data? Where does it run? Who can access it? How do we stop it making things up? The demo sits in a folder. Nobody is using it.
Generic Tools That Do Not Fit Your Workflow
You tried a no-code AI platform. You tried a ChatGPT wrapper. They generate text. They answer questions. But they cannot follow your quoting process, reference your product catalogue, enforce your compliance rules, or match the way your team actually works. Close — but not close enough to replace what you do manually.
Data Security Concerns Blocking Adoption
The IT team said no. Legal said maybe. The CEO said "I read an article about data leaking to OpenAI." Everyone wants AI. Nobody is willing to risk feeding business data into a tool they do not control. And nobody has proposed a deployment model that addresses the concerns.
Valuable Staff Time Spent on Repetitive Knowledge Work
Your best people spend hours drafting responses, summarising documents, classifying records, checking compliance, or translating technical content into plain language. This is knowledge work — but it is repetitive knowledge work. The same patterns, the same data sources, the same output formats. Every day.
An AI Use Case That Needs Its Own Interface
Embedding AI into an existing system sometimes is not the right answer. Sometimes the use case is distinct enough — specific enough workflow, specific data, specific users — that it needs a purpose-built application. But building an LLM-powered app from scratch is not a weekend project.
Need to Ship Something Real, Not Another Experiment
You are past the "let us explore AI" phase. You have a specific workflow, specific users, and a specific outcome in mind. But the gap between a promising ChatGPT prompt and a production-grade business application is significant. You need someone who builds these for a living.
Get Started
What workflow would you automate with AI?
Tell us the task, the data involved, and who would use it. We will scope a custom LLM app with a clear price and timeline.
Custom LLM apps — by category
Five categories of AI applications we build. Most businesses start with one workflow and expand from there.
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.
Connected to your data. Running on your rules. Used by your people. The AI handles the heavy lifting. The interface makes it feel natural.
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.
Role-based access built in. Sales sees sales data. HR sees HR policies. Operations sees operational docs. The application respects your existing permissions.
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.
Output in your formats: Word, PDF, branded templates. Ready to send, not ready to edit for another hour.
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.
Integrated with your downstream systems. Once classified, the item moves to the right queue, the right team, the right workflow. No manual handoff.
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.
Deployed as web apps, embedded widgets, or portal features. Works on desktop and mobile.
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 →What makes a HELLO PEOPLE custom AI app different
Not a wrapper around ChatGPT. Purpose-built applications designed for specific tasks, connected to your data, deployed securely.
Purpose-Built Interfaces
Not a chat window. Custom UI designed for the specific task — fields, actions, outputs, and workflows tailored to how your team works.
Connected to Your Data
Databases, CRM, documents, APIs, knowledge bases. The app reads your real business data so answers are grounded, not invented.
Secure Deployment
Runs in your cloud or ours. Australian data residency. Role-based access. Encrypted at rest and in transit. Audit trail on every interaction.
Guardrails & Validation
System prompts, output constraints, hallucination detection, and topic boundaries. The AI stays accurate, on-brand, and within its lane.
Model-Agnostic Architecture
Built to swap models without rebuilding. GPT-4o today, Claude tomorrow, an open-source model next quarter. Your app keeps working.
Usage Analytics
Track who uses the app, what they ask, how often, and whether the AI responses are helpful. Data to improve the app over time.
What changes when you have the right AI tool
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.
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.
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.
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.
Custom LLM apps by use case
Any workflow with repetitive knowledge work, data lookup, or document creation. These are the most common starting points.
AI Quoting & Estimating
Read specifications, reference pricing data, and generate first-draft quotes. Human reviews and adjusts. Cuts quoting time by 60-80% for trades, construction, and professional services.
Document Review & Compliance
Contracts, policies, regulatory submissions reviewed against your rules. Risk clauses flagged. Missing sections identified. Compliance gaps highlighted before human sign-off.
Internal Knowledge Search
Staff ask questions in plain English and get answers from company policies, procedures, product manuals, and project histories. With source citations. Replaces hunting through folders and asking colleagues.
Customer Response Drafting
AI drafts customer replies based on your knowledge base, past responses, and brand voice. Staff review and send. Consistent quality. Faster response times.
Report Generation
Pull data from CRM, accounting, project management, and operational systems. AI writes the narrative — progress reports, project summaries, board papers — in your language and format.
Product Recommendation Engine
Customer-facing app that asks questions, understands requirements, and recommends products from your catalogue. Connected to stock, pricing, and availability. Replaces generic comparison pages.
From use case to production app in 6 steps
A structured approach that validates the AI works before committing to the full build.
Models, platforms, and data sources we work with
Model-agnostic. Cloud-flexible. Connected to whatever systems your business runs on.
LLM Models — GPT-4o, Claude, Gemini, Llama
Model-agnostic. We use the right model for your use case and switch when better options emerge. No vendor lock-in.
Cloud — Azure, AWS, GCP
Deployed in Australian data centres. Azure OpenAI (Sydney), AWS Bedrock (Sydney), or private infrastructure. Your choice.
CRM — HubSpot, Salesforce, Zoho
Read customer data, enrich records, draft communications, and generate reports from your CRM data.
Databases & Documents
SQL, PostgreSQL, SharePoint, cloud storage, PDFs, Word documents, spreadsheets. The app reads what your team reads.
Accounting — Xero, MYOB
Financial data for reporting apps, invoice processing, and AI-powered analysis connected to your accounting system.
Custom APIs & Webhooks
Any system with an API. Industry-specific software, internal tools, legacy platforms. If it has data, the AI app can use it.
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.
Why HELLO PEOPLE
We build, not just advise
We write the code, design the interface, deploy the systems, and support them long-term. No subcontracting, no offshore handoffs.
Fixed-price quoting
You get a clear price before we start. No hourly billing that spirals, no surprise invoices at the end of the month.
Built for Australian business
We understand BAS, super, award rates, Australian privacy law, and the tools local businesses actually use — Xero, MYOB, ServiceM8, Tradify.
Senior team, direct access
You talk to the people building your software. No account managers, no project managers relaying messages, no ticket queues.
Full code ownership
You own everything — the code, the data, the hosting. No lock-in. No proprietary platforms you cannot leave.
Common questions about custom LLM app development
What is a custom LLM app exactly?
A purpose-built application powered by a large language model (like GPT-4, Claude, or Llama) designed for a specific business workflow. It has its own interface, its own data connections, and its own guardrails. Unlike ChatGPT in a browser, it is tailored to one job, connected to your data, and deployed securely.
How is this different from using ChatGPT directly?
Three key differences. First, a custom app is connected to your business data — it answers from facts, not general knowledge. Second, the interface is designed for the specific task — not a general-purpose chat window. Third, it is deployed securely in your environment with access controls and audit trails.
Can we use an open-source model instead of GPT?
Yes. We build model-agnostic applications. If data privacy or cost concerns favour an open-source model like Llama or Mistral, we can deploy it on your own infrastructure. The application architecture stays the same regardless of the underlying model.
How long does a custom LLM app take to build?
A focused single-workflow app — for example, a quoting assistant or document reviewer — takes 6 to 10 weeks including prototype, testing, and deployment. Multi-workflow apps or apps requiring complex data integrations take 10 to 16 weeks.
How much does custom LLM app development cost?
A focused single-workflow app starts from $20,000 to $40,000. Multi-workflow apps with complex data connections and multiple user roles range from $40,000 to $80,000. Ongoing costs include AI model API fees ($200–$1,500/month depending on usage) and optional support.
Where does the app run?
Your choice. Azure (Sydney), AWS (Sydney), GCP, or on-premises. We recommend Australian-hosted cloud for most businesses — it balances performance, cost, and data residency requirements. All data encrypted at rest and in transit.
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 instead of guessing.
Do you maintain the app after launch?
Yes. Monthly reviews, prompt tuning, model updates, and data source maintenance. We track accuracy, usage patterns, and user feedback. The app improves continuously based on how your team uses it.
Get Started
Ready to move from AI experiment to AI tool?
Tell us the workflow, the data, and the users. We will scope a custom LLM app that works for your business — not a demo that impresses for five minutes.
Tell Us About Your AI App Idea
What task or workflow would you automate? What data is involved? Who would use it? We will come back with a practical scope and clear pricing.
Prefer a quick chat? Call 0425 531 127 – we're Perth-based and we answer the phone.