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Claude, GPT, and Gemini API integration built into your product - by a dedicated engineering team.
Remote Generative‑AI Development for Australia A Dedicated Developer, Hourly or Monthly
Generative-AI features inside the product your Australian team already ships - summarise, draft, classify, extract, semantic search - integrated by a remote dedicated developer for AUD $40/hr or AUD $3,000/mo on Australian afternoon overlap, Privacy Act aware.
Generative-AI Features From Empiric Infotech LLP
Empiric Infotech LLP integrates generative-AI features inside the product your Australian team already ships - SaaS, B2B, internal tools, consumer products. A summarise button, a draft-this assistant, a smart-classify on inbound, a structured-extract from a PDF, a semantic search across your data, a generate-from-template feature - shipped as a feature in your codebase, behind a feature flag, with the prompt engineering, eval discipline, Privacy Act-aware logging, and cost/latency observability that turn an AI demo into a real product line. Two ways to engage a remote dedicated generative-AI developer, billed in AUD: book hours at AUD $40/hr for a defined scope (a v1 LLM feature, a model swap, a Claude or GPT integration on a single surface, a Privacy Act-aware evals pass), or lock a month at the standard AUD $3,000 for 160-172 hours of full-time, exclusive work on Australian afternoon overlap when AI features are a rolling roadmap. The developer works in your GitHub or GitLab org, your Australian cloud (AWS Sydney ap-southeast-2, Azure Australia, GCP, or on-prem), and your model keys (Anthropic, OpenAI, Google, or self-hosted Llama / Qwen), with the LLM library that fits your stack. We design the feature surface and the prompt, wire the API call and the streaming UI, structure the output, add retrieval where the feature needs context, build the evals on your real users' inputs, ship behind a flag, monitor cost and latency per feature, and run the model swap when a newer or cheaper one wins. A senior team lead reviews and tests every release. Why the hourly premium? Generative-AI integration is high-iteration expert work; the monthly rate is the same flat AUD $3,000 as any Empiric engagement once you commit. No Fair Work obligations, superannuation, or payroll tax, because the developer is not your employee.
What a generative-AI engagement delivers for Australian teams
Not a demo notebook that does one nice thing on a slide. A production AI feature inside your real product, in your Australian cloud, behind a feature flag, with the evals, cost guardrails, and a compliance-readable audit trail.
An LLM feature inside your existing product
A summarise button, a draft-this assistant, a smart-classify on inbound, a structured-extract from a PDF, a semantic search across your data, a generate-from-template feature - integrated where your Australian users already are, in your codebase, behind a feature flag. We will tell you when a single Claude or GPT call beats RAG beats an agent.
The right model, with AWS Sydney residency where it matters
Claude (Anthropic) is our default for long-context and tool use; GPT (OpenAI) for cases where the latest model lineup wins; Gemini (Google) for multimodal or long-context cases; open-source (Llama, Mistral, Qwen) on Bedrock ap-southeast-2 or self-hosted for cost-sensitive or fully residency-bound cases. We wire it into your existing app server, your existing auth, your existing observability, your existing rate limits.
Structured outputs and streaming UI
Where the feature needs structured data, the LLM returns JSON Schema-conformant outputs or uses tool calling - not a regex. Where the feature needs a fast-feeling UI, the response streams to the user, with cancel, retry, and graceful failure. Where the feature needs both, we wire both.
RAG where it earns its place, with AWS Sydney
Some features need retrieval - we build the RAG pipeline (chunking, embeddings, vector DB, reranking, citations) tuned for your corpus, with data in AWS Sydney if you want it there. Many features do not need retrieval; we will tell you which is which and not bill a vector DB you do not need.
Privacy Act-aware evals
Eval-driven from week one. Golden sets of your real users' inputs (anonymised, Privacy Act-aware), adversarial sets, LLM-as-judge accuracy grading, your team grading edge cases. Privacy Act-aware PII redaction at write time. A compliance-readable audit trail. Spam Act-aware handling on outbound where it applies.
Cost and latency observability per feature
A dashboard on per-feature LLM cost (input + output tokens, model, day), p95 latency, fallback rate, and the cost-per-correct-answer line from evals - per feature, not lumped.
A developer who is still there next month
Models change, prompts drift, features grow. A dedicated engagement means the same developer ships the next feature, swaps the model, keeps the evals green, and tunes the cost line - without a Fair Work process to manage.
How we scope a generative-AI engagement for an Australian team
No multi-week sales cycle and no twenty-page statement of work. A call, a written scope, a trial, then hourly or monthly - your call. All on Australian afternoon overlap.
A scoping call
Thirty to forty-five minutes on Australian afternoon overlap. You tell us what AI feature you want shipped, what product it goes inside, what model and SDK you are already using, the data sensitivity, and what would count as a measurable outcome. No charge, no obligation.
A written scope and team proposal
We send back the feature definition, the model and SDK we would use, the prompt design, the structured-output schema, the eval plan and golden set, a rough cost-per-call estimate, the feature flag and rollout plan, who we would put on it, and the price both ways - in AUD.
A 7-day risk-free trial (on the monthly plan)
The developer gets into your repo and Australian cloud account and ships the first slice - the feature working end to end inside your app on a small, real input set, evals on a golden set, behind a feature flag - inside the first week, reviewed and tested by the senior lead. Not a fit by day 7, full refund on the monthly plan.
Hourly or monthly, your choice
Hourly: billed by the hour at AUD $40, time tracked to the minute, a weekly time report and a demo, stop any time - best for a defined scope or burst work. Monthly: 160-172 hours at the standard AUD $3,000, monthly billing in AUD, cancel with 7 days notice - the better value when AI features are a rolling roadmap. No Fair Work process either way.
Two ways to engage a generative-AI developer
Two ways to engage a remote generative-AI developer, billed in AUD. By the hour at AUD $40 - pay as you go, time tracked, a weekly report and demo, no monthly commitment - best for a defined scope like a v1 feature, a model swap, a single-surface integration, or a Privacy Act-aware evals pass. Or monthly at the standard AUD $3,000 for 160-172 hours of full-time, exclusive work on Australian afternoon overlap - the better value when AI features are a rolling roadmap, with a 7-day risk-free trial. Either way: your repo, your Australian cloud, the right SDK, Privacy Act aware, data in AWS Sydney if you want it there, and a senior lead reviews and tests every release. No Fair Work, superannuation, or payroll tax either way. Model and platform usage is billed to your own accounts at cost.
Hourly plan
- A dedicated generative-AI developer, exclusive to you while you have hours booked
- Pay as you go - billed by the hour in AUD, time tracked, a weekly report and demo
- Best for a defined scope (v1 feature, model swap, single-surface integration, Privacy Act-aware evals); no monthly commitment, stop any time
- Your repo, Australian cloud, and model keys from day one; Privacy Act aware
- Every release reviewed and tested by a senior lead; no Fair Work, super, or payroll tax
Monthly plan
- A dedicated generative-AI developer, full-time and exclusive - 160-172 hours on Australian afternoon overlap
- The best value when AI features are a rolling roadmap - feature after feature, model swaps, eval iteration
- Your repo and Australian cloud from day one; the same flat rate as any Empiric engagement
- 7-day risk-free trial, monthly billing in AUD, cancel with 7 days notice; no Fair Work, super, or payroll tax
- A senior lead reviews and tests every release; data in AWS Sydney if you want it there
Dedicated team
- A small dedicated team - developers plus a senior team lead who reviews and tests every release
- Add a developer (or a designer for AI-feature UI) at the same rate, in 48 hours
- Pair a generative-AI developer with a chatbot or agent developer to ship the related surfaces at once
- Best for multi-feature roadmaps, a multi-surface rollout, or shipping AI features across product lines
What the first 90 days look like for an Australian team
Whether you are booking hours or on the monthly plan, the shape is the same, all on Australian afternoon overlap. Here is a typical first three months.
- Week 1
Onboarding and the first AI feature
Repo and AWS Sydney (or Azure Australia / GCP) access, a working local environment, the model and SDK chosen, the feature surface mapped, and a first slice live - the AI feature working end to end inside your app on a small, real input set, evals on a golden set, behind a feature flag, logging on cost and latency - shipped and reviewed. Day 7 is the risk-free decision point on the monthly plan.
- Month 1
An AI feature shipped to Australian users
The first feature rolled out (behind a flag, then a fraction of users, then GA), prompt and structured output tuned, evals expanded, cost and latency dashboards on per-feature spend and p95 latency, a fallback path on outages, data in AWS Sydney if you want it there.
- Month 2
The second feature, the second surface
Edge cases month one surfaced - smoothed, prompt and output schema tightened, the second AI feature scoped or shipped, a model-fallback path for outages, the eval and observability scaffolding reused.
- Month 3 and on
Model swap, cost tuning, Privacy Act pass
A model swap if a newer or cheaper one wins on your evals, a prompt-caching pass, an Australian Privacy Principles and Spam Act compliance pass where relevant, a fine-tuning pass when the general model still misses, and the next AI feature scoped.
A remote generative-AI developer - hourly or monthly - vs a fixed-price AI integration agency, a no-code AI-feature platform, or an Australian in-house hire
| Empiric Infotech (generative-AI developer - hourly or monthly) | Fixed-price AI integration agency | No-code AI-feature platform (Vellum, Humanloop, etc.) | Hire an AI engineer in-house in Australia | |
|---|---|---|---|---|
| What you actually get | AI features shipped inside your existing product, owned by you, with the developer who built them still there to grow and tune them | An AI feature built to a spec, then a maintenance retainer or you are on your own | A dashboard and a prompt UI; the actual integration, evals, and observability are on you | Whatever your team can build alongside their other work |
| Pricing model | AUD $40/hr for hourly work, or the standard AUD $3,000/mo for a full-time developer if you lock a month; model and platform usage billed to your accounts at cost | AUD $15K-$90K fixed bid for a v1 AI feature; change orders billed extra | Platform subscription (AUD $80-$1,500/mo) plus your team's time integrating | AUD $150K-$210K salary + superannuation + payroll tax - and rarely a full-time hire on its own |
| Estimate before you commit | An estimate both ways - hours per feature or what a month covers - plus a weekly time report and a demo | A fixed bid - you wear the overage as change orders | Platform demos; the real cost shows up after week two of integration | Internal estimates, if any |
| Privacy Act, data residency, and audit trail | Australian Privacy Principles aware, data in AWS Sydney if you want it, a compliance-readable audit trail - built in | Per the spec; new gates may be change orders | Per platform; check the location and the sub-processors | In-house, on your own terms |
| Structured outputs and streaming UI | JSON Schema-conformant outputs or tool calls, streaming UI with cancel/retry, multimodal where the case calls | Per the spec; advanced cases are change orders | Whatever the platform supports | As much as your team builds |
| Cost and latency observability | Per-feature LLM cost, p95 latency, fallback rate, cost-per-correct-answer | Per the spec; new dashboards are change orders | Platform dashboards on platform calls only | As much as your team builds |
| Quality control | A senior lead reviews and tests every release before it goes live - built in, no extra charge | Per agency - often the same people who built it | On you to review and verify | Your own review process, if you have one |
| When the model changes (or breaks) | The same developer swaps the model, re-runs the evals, and ships the fix - book an hour, or it is in the monthly plan | A support ticket, or a new maintenance retainer | Wait for the platform to support it | Whoever built it, if they are still at the company |
| Employment overhead, and time to start | None - the developer is not your employee; no Fair Work, super, or payroll tax; 48 hours to start | None; 2-6 weeks (proposal, SOW, kickoff) | None; days to start, a week or two of integration | Fair Work, super, payroll tax, leave; 2-4 months in a thin AI-talent market |
Figures are typical Australian market ranges, not quotes. Model and platform usage costs apply on top of any build cost in every option and are billed to your own accounts in ours. A fixed-price agency build of a comparable LLM feature commonly lands in the AUD $15K-$90K range before change orders.
Working hours and Australian overlap
Our team works 09:30 AM - 07:30 PM IST and a project manager is on call 07:30 AM - 10:30 PM IST, Monday to Friday. Here is exactly when that lands for clients in the US, Europe and Australia, your region first.
Australia East (Sydney, Melbourne, Brisbane) - full team online 2:00 PM - 12:00 AM (next day), project manager 12:00 PM - 3:00 AM (next day).A solid block of live hours every business day, with async cover on either side.
US Eastern (New York, Boston, Atlanta) - full team online 12:00 AM - 10:00 AM, project manager 10:00 PM - 1:00 PM (next day).
US Pacific (Los Angeles, San Francisco, Seattle) - full team online 9:00 PM - 7:00 AM (next day), project manager 7:00 PM - 10:00 AM (next day).
UK & Ireland (London, Dublin) - full team online 5:00 AM - 3:00 PM, project manager 3:00 AM - 6:00 PM.
Central Europe (Berlin, Paris, Amsterdam, Madrid) - full team online 6:00 AM - 4:00 PM, project manager 4:00 AM - 7:00 PM.
Want it to the half-hour in your own time? Slide through your day and book a slot below.
Why Australian teams ship their generative-AI features with a dedicated developer, not a fixed-price agency
A Sydney or Melbourne LLM hire who has actually shipped a production AI feature (evals, structured outputs, cost lines, a fallback on outage) runs roughly AUD $14,700 to $20,400 a month all-in once you add superannuation, payroll tax, and on-costs, in a local AI-talent market thin enough that the names worth interviewing usually fit on one screen. A fixed-price AI agency build of a v1 LLM feature typically runs AUD $15,000 to $90,000 before the first change order, then a separate maintenance retainer. Empiric Infotech is billed two ways - AUD $40 an hour for a defined scope, or the standard AUD $3,000 a month per developer for 160-172 hours of full-time, exclusive work - in AUD, with the same person on your AI features the next month, and a senior lead reviewing and testing every release at no extra cost.
Most generative-AI integrations fail in the same places: an impressive demo on a curated input set that falls over on real inputs; a single Claude or GPT call dropped in with no eval; free-form outputs your product has to parse with a regex; cost lines that nobody is monitoring per feature; no Privacy Act-aware logging. A dedicated Empiric developer has shipped AI/LLM features in production for Australian SaaS, agencies, and product teams - structured outputs, evals, retrieval, integration discipline - and is still there next month.
We have built web and mobile products since 2020 and AI/LLM features since the current wave began. The depth shows up in the parts a quickstart skips: structured outputs your product can consume, evals on real inputs from week one, a per-feature cost line, a fallback path on outages, a feature flag and a rollout plan, Privacy Act-aware PII redaction, and the honesty to say when a single LLM call beats RAG beats an agent.
Recent AI, product, and integration work
Acceleread - an Australian product engineered end to end
An Australian product built for an Australian client by an Empiric Infotech team - the application, the integrations, and the backend behind it. The integration discipline an AI-feature build sits inside, on Australian afternoon overlap.
Read case studyYield Magnet - an Australian product engineered end to end
An Australian B2B product built for an Australian client by an Empiric Infotech team. The shape of the wiring an AI feature needs - your repo, your AWS Sydney, your data.
Read case studyDRT - a trans-Tasman build
A New Zealand product built for an NZ client by an Empiric Infotech team - trans-Tasman overlap, the same engagement model, the same senior-lead review on every release.
Read case studyReady to ship your generative-AI feature?
Tell us what AI feature you want shipped inside your Australian product - the surface, the input, the output, the user, the data sensitivity, and what would count as a real outcome. Within 24 hours we will send back a feature definition, a model and SDK recommendation, the prompt and structured-output design, an eval plan, a feature-flag and rollout plan, a team proposal, and an estimate both ways - in AUD. Your developer starts inside 48 hours on Australian afternoon overlap.
Who This Is For
Built for Businesses Ready to
Harness AI Creativity at Scale
We partner with founders, product teams, and innovators who want AI that doesn’t just automate - it creates. From generating personalized content to building adaptive AI tools, we make sure it works for your real-world needs.
This Is for You If:
You need AI-generated outputs that meet brand, compliance, or industry standards
You’ve tried ChatGPT or Midjourney but can’t scale quality or integrate results
You want AI that can create across multiple formats : text, image, video, or code
You’re looking for secure, private AI that learns from your data without leaking it
You want generative models fine-tuned for your audience, domain, or products
You’re done with one-size-fits-all tools and need a system tailored to your workflows
What We Do
We Build Generative AI
Systems That Create Like Experts, Operate Like Engineers
We don’t stop at “prompt engineering.” We architect full-stack generative AI solutions - from model selection and fine-tuning to API integration and deployment - all designed for accuracy, reliability, and scalability.
What We Build:
AI-powered content creation pipelines (text, image, audio, video)
Domain-specific fine-tuned LLMs for better accuracy & compliance
Intelligent content moderation, filtering, and fact-checking layers
Multi-format generation workflows integrated into your existing tools
Fully automated creative processes - from ideation to publishing
Platforms & Tools We Work With (and Beyond):
We’re platform-agnostic - if it can generate, we can integrate and optimize it.
Core Capabilities
Text-to-Anything Content Generation
Produce high-quality articles, ad copy, product descriptions, and more - tailored to your brand voice and optimized for SEO or engagement.
Powered by: Chat-GPT, Claude, Gemini, custom fine-tuned LLMs
Image & Creative Asset Generation
From photorealistic product images to AI-assisted illustrations and marketing creatives - generated in seconds, not days.
Built with: Midjourney, DALL·E, Stable Diffusion
Custom Fine-Tuned Models
Train models on your proprietary data to create industry-specific AI systems that understand your niche, tone, and workflow.
Tech behind the scenes: OpenAI fine-tuning, LoRA, embeddings, Pinecone
Multimodal AI Experiences
Combine text, image, and audio generation into unified tools - for example, AI that can write a script, create visuals, and generate voiceovers in one flow.
Built using: OpenAI, ChatGPT, Runway, ElevenLabs
Data-to-Insight AI Reports
Turn raw datasets into insightful summaries, visuals, and recommendations - without a single pivot table.
Integrated into: Notion, Data Studio, PowerBI, and custom dashboards
How We Build Systems That Scale
A Collaborative Process, Built for Your Creative and Data Needs
We don’t just plug in AI APIs - we design, train, and optimize generative AI systems around your goals, workflows, and audience.
Discovery & Use Case Mapping
We explore your goals, datasets, and workflows - identifying where generative AI can replace manual work or unlock new capabilities.
Outcome: Clear ROI-backed AI roadmap
Data Preparation & Model Selection
We prepare your content/data, choose the right base models, and plan whether fine-tuning or prompt engineering is needed.
Outcome: Optimized, domain-specific AI foundation
Prototype & Validate
We create a functional prototype to showcase capabilities and gather feedback early.
Outcome: Stakeholder alignment and proof of value
Full Development & Integration
We build and integrate the generative AI solution into your tools, systems, and workflows.
Outcome: AI running seamlessly in production
Onboarding & Creative Enablement
We train your team to use, adapt, and improve the AI’s outputs - ensuring long-term productivity.
Outcome: Confident, in-house AI adoption
Optimization & Continuous Learning
We monitor performance, retrain when needed, and adapt to your evolving business goals.
Outcome: AI that stays relevant and grows with you
Industries We Build For
Generative AI Solutions for Every Industry
From innovative startups to enterprise-scale operations, we deploy generative AI that adapts to your sector’s unique workflows and challenges.
Industries We Serve:
SaaS & Startups
Content generation, customer onboarding, product documentation
E-commerce
Automated product descriptions, personalized recommendations, support chat
HR & Recruitment
AI-powered screening, resume parsing, interview question generation
Healthcare Admin
Clinical documentation assistance, patient communication, compliance reports
Logistics & Supply Chain
Route optimization, predictive demand planning, document automation
EdTech
Adaptive learning content, grading automation, personalized study materials
If your industry requires nuanced, context-aware AI, we can build it.
Why Empiric for Generative AI
Why Teams Trust Empiric for Generative AI Development
We don’t just integrate AI APIs - we design, fine-tune, and deploy custom generative AI systems built for real business impact.
Our Approach:
Domain-specific fine-tuning
AI that understands your industry’s language and rules
Custom model workflows
No one-size-fits-all templates
Privacy & security first
Data-safe solutions, self-hosted options available
Rapid prototyping
Validate with a functional v1 before scaling
Founder-led delivery
Direct collaboration with decision-makers
Post-launch iteration
Continuous improvement for lasting value
Tools We Work With
Flexible Tech Stack. Built Around Your Needs.
We leverage the best in AI, LLMs, and supporting infrastructure - and adapt the stack to fit your business goals.
Stack Includes:
AI & Language Models
Automation Platforms
AI Frameworks
Voice & Communication
Backend & Database
OpenAI
Claude
Gemini
Mistral
Meta LLaMA
Prefer open-source, enterprise-grade, or hybrid? We’ll build with what makes sense for you.
Why Businesses Choose Empiric Infotech LLP?
Working with Empiric Infotech has been exceptional from start to finish. Their team is incredibly talented, highly responsive, and consistently delivers at a world-class level across UI/UX and back-end development. No matter the challenge or complexity we have brought to them, they have always found a solution. Having a development partner you can fully trust is invaluable, and I would highly recommend them to any company or founder looking for an elite development team.
Founder and CEO Roamate
We worked with Empiric Infotech to build a chatbot for our children’s theater project, and we’re so glad we did. The team was fast, responsive, and kept working until everything was perfect. The project was delivered on time, within budget, and the end result looks and works great. We’re truly grateful for their support
E-commerce Brand
I found in Empiric Infotech an excellent partner to build my blockchain platform. They are professional, knowledgeable, and supportive at every stage. Even after launch, we keep collaborating - and the results have always been wonderful
Founder Winupdraws
I was looking for a skilled software team to help me transform my prototype into a complete app. Empiric Infotech not only delivered exactly what I needed, but also added custom features, backend notifications, and guided me through publishing on both app stores. Their communication was smooth and reliable. I can gladly recommend them.
Founder and consultant at tak innovation
We needed support to strengthen our technical platform, and Empiric Infotech delivered exactly what we were looking for. The team was professional, responsive, and kept everything on track with both time and cost. As our company grows, we’re glad to have them as a trusted partner and would happily recommend their services.
Founder Skapasaga
Finding the right agency for our My Ayur app was challenging, but Empiric Infotech delivered exactly what we needed. Over past months, their expertise in FlutterFlow and MongoDB stood out, consistently delivering high-quality work. Professional, friendly, and reliable - a partner I would confidently recommend.
Founder and CEO Sresht Ayur
Working with Empiric Infotech has been exceptional from start to finish. Their team is incredibly talented, highly responsive, and consistently delivers at a world-class level across UI/UX and back-end development. No matter the challenge or complexity we have brought to them, they have always found a solution. Having a development partner you can fully trust is invaluable, and I would highly recommend them to any company or founder looking for an elite development team.
Charli SharpFounder and CEO Roamate
We worked with Empiric Infotech to build a chatbot for our children’s theater project, and we’re so glad we did. The team was fast, responsive, and kept working until everything was perfect. The project was delivered on time, within budget, and the end result looks and works great. We’re truly grateful for their support
Eva MesmanE-commerce Brand
I found in Empiric Infotech an excellent partner to build my blockchain platform. They are professional, knowledgeable, and supportive at every stage. Even after launch, we keep collaborating - and the results have always been wonderful
John FelipeFounder Winupdraws
I was looking for a skilled software team to help me transform my prototype into a complete app. Empiric Infotech not only delivered exactly what I needed, but also added custom features, backend notifications, and guided me through publishing on both app stores. Their communication was smooth and reliable. I can gladly recommend them.
Andrzej KarelFounder and consultant at tak innovation
We needed support to strengthen our technical platform, and Empiric Infotech delivered exactly what we were looking for. The team was professional, responsive, and kept everything on track with both time and cost. As our company grows, we’re glad to have them as a trusted partner and would happily recommend their services.
Fredrik HagenFounder Skapasaga
Finding the right agency for our My Ayur app was challenging, but Empiric Infotech delivered exactly what we needed. Over past months, their expertise in FlutterFlow and MongoDB stood out, consistently delivering high-quality work. Professional, friendly, and reliable - a partner I would confidently recommend.
Eshu MiddhaFounder and CEO Sresht Ayur
Compliance & Security
Generative AI Without Compromising Privacy or Control
What We Deliver:
GDPR-compliant data handling
Role-based access controls (RBAC)
Self-hosting options
Audit logging & retention governance
Built for teams who want the power of generative AI - without the risk.
Let’s Build the Generative AI Solution
Your Business Deserves
FAQs
Answers to Common Questions - From Founders, Ops Teams & Tech Leads
Frequently asked questions
Generative-AI development (this page) is about adding LLM features inside the product your team already ships - a summarise button, a draft-this assistant, a smart-classify, a structured-extract, a semantic search. Not a standalone bot, not an agent. An AI chatbot (see /services/chatbot-development) is a separate surface that answers from a knowledge base. An AI agent (see /services/ai-agent-development) is a multi-step LLM workflow.
Two ways, billed in AUD. By the hour at AUD $40 - pay as you go, time tracked, a weekly report and demo, no monthly commitment - best for a defined scope like a v1 feature, a model swap, or a Privacy Act-aware evals pass. Or monthly at the standard AUD $3,000 per dedicated developer for 160-172 hours of full-time, exclusive work on Australian afternoon overlap, with a 7-day risk-free trial. Either way: Privacy Act aware, a senior lead reviews and tests every release, and no Fair Work, super, or payroll tax.
You own it - your repo, your AWS Sydney or Azure Australia or GCP account, your prompts, your model keys, your data - from day one. Data in AWS Sydney (ap-southeast-2) if you want it there. Australian Privacy Principles aware handling, a sub-processor list, Privacy Act-aware PII redaction at write time, and a compliance-readable audit trail on day 0. Spam Act-aware handling where the use case involves outbound messages.
Whichever wins on your evals at a cost that works. Claude (Anthropic) is our default for long-context and tool use; GPT (OpenAI) for cases where the latest model lineup wins; Gemini (Google) for multimodal or long-context cases; open-source (Llama, Mistral, Qwen) on Bedrock ap-southeast-2 or self-hosted for cost-sensitive cases.
Depends on the feature. A summarise button on the page the user is already on does not need a retriever. A doc Q&A across a 10,000-page corpus does. A draft-this assistant in the editor often does not. We will tell you which is which and not bill a vector DB you do not need.
Where the feature needs structured data, the LLM returns JSON Schema-conformant outputs or uses tool calling. Where the feature needs a fast-feeling UI, the response streams to the user, with cancel, retry, and graceful failure. Where the feature needs both, we wire both.
No. The developer is not your employee. There is no Fair Work process to manage, no superannuation, no payroll tax, no annual leave or long-service accrual. The engagement is a service contract between two companies, billed monthly or hourly in AUD, with 7 days notice to stop on the monthly plan or stop-any-time on hourly.
Within 48 hours of sign-off: a scoping call on an Australian afternoon slot, a written scope and team proposal, then onboarding on day one. The first 7 days on the monthly plan are a risk-free trial with a full refund. After that it is monthly billing with 7 days notice to stop, or hourly with stop-any-time.
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