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.
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.
Three stages. No surprises. Your live operations stay untouched until cutover — delivered personally by the founder.
Week 0
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
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
Production cutover on a planned window. Team training, monitoring active, 30 days post-launch support. Documentation, prompts and source code handed over in full.
If you have tried generic AI tools and hit the wall of 'almost but not quite', you are ready for a custom build.
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.
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.
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.
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.
Embedding AI into an existing system sometimes is not the right answer. Sometimes the use case is distinct enough (specific 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.
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.
Five categories of AI applications we build. Most businesses start with one workflow and expand from there.
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.
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.
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.
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.
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.
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 studyNot a wrapper around ChatGPT. Purpose-built applications designed for specific tasks, connected to your data, deployed securely.
Not a chat window. Custom UI designed for the specific task. Fields, actions, outputs, and workflows tailored to how your team works.
Databases, CRM, documents, APIs, knowledge bases. The app reads your real business data so answers are grounded, not invented.
Runs in your cloud or ours. Australian data residency. Role-based access. Encrypted at rest and in transit. Audit trail on every interaction.
System prompts, output constraints, hallucination detection, and topic boundaries. The AI stays accurate, on-brand, and within its lane.
Built to swap models without rebuilding. GPT-4o today, Claude tomorrow, an open-source model next quarter. Your app keeps working.
Track who uses the app, what they ask, how often, and whether the AI responses are helpful. Data to improve the app over time.
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.
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.
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.
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.
Any workflow with repetitive knowledge work, data lookup, or document creation. These are the most common starting points.
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.
Contracts, policies, regulatory submissions reviewed against your rules. Risk clauses flagged. Missing sections identified. Compliance gaps highlighted before human sign-off.
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.
AI drafts customer replies based on your knowledge base, past responses, and brand voice. Staff review and send. Consistent quality. Faster response times.
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.
Customer-facing app that asks questions, understands requirements, and recommends products from your catalogue. Connected to stock, pricing, and availability. Replaces generic comparison pages.
A structured approach that validates the AI works before committing to the full build.
Model-agnostic. Cloud-flexible. Connected to whatever systems your business runs on.
Model-agnostic. We use the right model for your use case and switch when better options emerge. No vendor lock-in.
Deployed in Australian data centres. Azure OpenAI (Sydney), AWS Bedrock (Sydney), or private infrastructure. Your choice.
Read customer data, enrich records, draft communications, and generate reports from your CRM data.
SQL, PostgreSQL, SharePoint, cloud storage, PDFs, Word documents, spreadsheets. The app reads what your team reads.
Financial data for reporting apps, invoice processing, and AI-powered analysis connected to your accounting system.
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.
You get working software, not a slide deck of recommendations. Everything we design gets built, tested and handed over — not left as a strategy artifact.
You know the cost before we start. No hourly surprises, no scope creep — we scope it, quote it, ship it.
Perth-based team who understand Xero, MYOB, ServiceM8, simPRO, Cliniko, Deputy — the tools your business actually runs on.
You email Kasun, you get Kasun. No account managers, no offshore handoff — straight from scope to ship.
When it is done, you own every line of code, every credential, every artefact. No lock-in, no license fees, no black-box dependencies.
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.
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.
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.
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.
We provide a fixed-price quote after scoping. A focused single-workflow app sits at the lower end of project size. Multi-workflow apps with complex data connections and multiple user roles are larger. Ongoing AI model API fees and optional support are scoped during the build.
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.
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.
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.
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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 answer the phone in Perth.
