Discovery and data readiness
We turn a business problem into a delivery plan: users, data quality, systems of record, risk, and what can wait for a later release.
Dev House Australia designs and builds AI that runs in the systems you already operate, not a slide-deck pilot. We help you decide whether the work is a language-model feature, a classical model, automation, or a conversation product, then take it from discovery through integration, evaluation, and support. Delivery is backed by Dev Centre House's 15+ years of global engineering, with collaboration that fits Australian business hours.
Bring the problem, the data you can share, and the systems the solution must talk to. We will talk through approach and a first milestone, in Australian time zones.
CLIENTS
Scope
This page is the commercial home for the AI build. Typical work covers discovery, data readiness, the right technical approach, integration, evaluation, and the run after launch. Narrower work lives on the sibling pages linked below.
We turn a business problem into a delivery plan: users, data quality, systems of record, risk, and what can wait for a later release.
We add scoring, retrieval, document intelligence, or recommendations to the web, mobile, CRM, or ERP estate you already run, without replacing the core system.
When the job is language, retrieval, or generation against your sources, that work is owned on the LLM development page.
When the job is a model on structured or visual data (forecasts, ranking, inspection) that work is owned on the machine learning page.
When the job is a workflow or RPA path with or without a model, that work is owned on the AI automation page.
When the product is a chatbot or assistant with channels and hand-off, that work is owned on the chatbot development page.
We set success metrics before scale, add the obvious privacy and access controls, and leave monitoring and documentation your team can operate.
We stay on for drift, prompt or model change, and the next slice of the roadmap. We do not publish a catalogue price before the scope is clear.
Technological Stack Expertise
Primary delivery is Python and SQL against the cloud you already use. TensorFlow and PyTorch stay available when a custom model is the responsible choice. Language-model, automation, and conversation work is detailed on those service pages, this page owns the commercial AI engagement.
Industries
We apply the same delivery model to sector constraints (identity, records, and operational systems) and link through to the industry pages that already exist on this site.
Decision support and document or onboarding work where auditability and existing banking systems are part of the brief.
Clinical or admin AI that treats privacy, consent, and reliable messaging as product requirements.
Service and staff tools that need clear information, secure access, and a maintainable operating model.
Catalogue, demand, and customer-operations AI that has to stay in step with inventory and fulfilment.
Approach
There is no default stack. A language model, a classical model, an automation path, and a chatbot are different products. We recommend after we understand the workflow, the data, and who is accountable when the output is wrong.
Search, draft, summarise, or answer from governed sources when the input and output are language.
Scores, forecasts, and inspection when the signal is in tables, events, or images rather than chat.
Repeatable hand-offs across systems when the value is cycle time, not a new model.
A bot or assistant with channels, policy, and human hand-off, not a generic widget.
Bring the problem, the users, the data, and the systems it must talk to. We will recommend an approach and a first milestone, in Australian time zones.
Process
The sequence stays the same whether the first release is a retrieval feature, a scoring model, or a governed assistant. What changes is how much we prove before anything is called production.
We capture the workflow, who is harmed if the output is wrong, what data you can use, and which systems are the source of truth.
We recommend a language-model feature, a classical model, automation, or a conversation product, or we say AI is the wrong spend.
We define APIs, identity, and where results are written back so the feature is not a sidecar nobody operates.
We implement the thinnest slice that can be evaluated, with review and the logging you will need after launch.
We test against the metrics agreed in discovery, including failure and hand-off paths, before we call it ready.
We deploy into your environment with access control, monitoring, and a rollback so go-live is controlled.
We stay on for drift, change requests, and the next slice of the roadmap.
You get regular progress you can inspect. If the first conversation is only an LLM, a model, automation, or a bot, start on that sibling page, this URL still owns the broader AI development engagement.
Cost
We do not publish a fixed project price. The estimate follows the work: data readiness, which approach you need, how deep the integrations go, and how much you need after launch.
After a short discovery conversation we can outline a first milestone and the factors that would move cost or date. Use the form below or book a call. We will not invent a number before the scope is clear.
Why Choose Us
The right AI partner turns a problem into software you can operate: clearer scope, earlier no, and a system a later team can change. Dev House Australia pairs engineering depth with delivery discipline for enterprises and startups in Australia.
We take on work that has to write back to a real workflow. A slide-deck pilot is not the commercial target of this page.
This URL owns the AI development engagement. Sibling pages own the narrower product. We will send you to the right one.
Success metrics, failure paths, and access control are planned before scale, not after a surprising go-live.
The case study below is AI work we can show. We will not invent prices, certifications, or local offices to win a conversation.
Reviews & Testimonials
Locations
Looking beyond Adelaide? Explore Dev House Australia artificial intelligence services in other Australian cities, same engineering standards, local context.
We turn a business problem into software that uses a model, retrieval, or automation inside a real workflow, then integrate it, evaluate it, and leave it operable. We do not treat a chat widget or a one-off notebook as the engagement.
Use this page for the overall AI development engagement or when you are not sure which approach fits. Use LLM development for RAG, retrieval, and language-model applications. Use machine learning for predictive or visual models. Use AI automation for workflow and RPA. Use chatbot development when the product is a conversation channel.
Yes. Most work is an addition to web apps, mobile apps, CRM, ERP, or document stores through APIs and services, not a replacement of the core system.
Pricing is an estimate against an agreed scope (data, approach, integrations, evaluation, and support), not a catalogue fee. Discovery is how we make that estimate honest.
We agree success metrics first, then harden data, access, and monitoring so the feature can fail safely and be operated by your team. If the pilot cannot meet the metric, we say so instead of scaling it.
No. The Australian office is in Sydney. Other city pages on this site are for teams we work with in those markets, not extra offices. Australia-wide commercial intent stays on this page.
Send the problem, the users, the data you can share, and the systems the solution must talk to. We will schedule a conversation, recommend an approach, and outline the first artefact. Use the contact page or book the call from this page.
Tell us about your project and we will respond from our Sydney team, usually within one to two business days. * indicates a required field.
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