Services
Generative AI Development Services that Reach Production
Our generative AI development services focus on building practical AI systems around your data, processes, and people.
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- Years10+
Field Experience
- Projects50+
Around the Globe
- Minute0
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- Client approval10%
What Goes Into Generative AI Development?
Generative AI development involves the development of AI systems that work with your business’s data and day-to-day processes. These systems can offer services ranging from searching internal documents and creating a team-specific copilot to extracting essential information from documents and generating content.
The model is only one part of the book. A majority of the effort goes into connecting the right data, managing access, testing responses, and keeping everything secure.
Use the right data
AI requires clean, relevant, and properly permissioned information to deliver useful outputs.
Test the results
A clear evaluation process helps you understand what’s working, what’s not, and what needs to be changed.
Safeguard personal information
Under Australian privacy guidance, fine-tuning a model with personal information brings various additional responsibilities.
Begin with a clear goal
The most useful projects solve a clear business problem and have a simple way to measure results.
What Causes Generative AI Pilots to Stall
Most failed pilots don’t fail because of the AI model. They fail because the business foundations around it aren’t ready.
No Clear Success Metric
If success is based on whether people liked the tool, it becomes difficult to prove its value. A clear metric should be set before the pilot even begins.
A Broken Process
AI can speed up an existing process, but it won’t fix a flawed one. Automating unnecessary steps or unclear approvals simply makes the process faster without making it better.
Poor-Quality Content
Outdated or duplicate documents can lead the AI to use the wrong information. A trusted source of truth is essential for consistent results.
Weak Permissions
AI can only respect the access rules it inherits. If the underlying systems have loose permissions, sensitive information can be exposed to the wrong users.
No Clear Ownership
When IT builds the pilot and the business owns the process, it becomes quite unclear where the responsibility lies when it comes to the outcome. Someone needs to own both the use case and its results.
No Quality Checks
AI output changes when there’s even the slightest change in prompts, models, or source content. It helps to test against a set of real questions to catch quality problems before a user can.
Prompting, retrieval, or fine-tuning
Three different techniques get sold as the same thing. They solve different problems, cost different amounts, and carry different obligations. Choosing wrongly here is the most expensive mistake available on a generative AI project.
Prompt engineering
Structuring the instruction, the examples and the output format so a general model does your task reliably. No new infrastructure, no new data pipeline.
| Solves | Format, tone, consistency and task framing |
|---|---|
| Does not solve | The model knowing anything about your business |
| Typical effort | Days, inside a larger build |
| Running cost | Lowest. You pay for tokens only. |
| Privacy position | You are a user of a commercial AI product. The OpenAI's business guidance applies. |
| Honest limit | It is a component, not a project. Anyone selling prompt engineering as a deliverable is selling you a week of work with a strategy label on it. |
Retrieval-augmented generation
At question time, the system searches your own content, pulls the relevant passages, and gives them to the model as context. The answer is grounded in documents you control and can be shown with its source.
| Solves | The model needing current, specific, organisation-owned knowledge |
|---|---|
| Does not solve | Teaching the model a new skill or output style on its own |
| Typical effort | Four to six weeks to a proof of value, longer to production |
| Running cost | Moderate. Tokens plus a vector index plus content pipelines. |
| Privacy position | Usually still a deployer. Personal information can be kept inside your tenancy. |
| Honest limit | Retrieval quality is content quality. If your document set is contradictory, RAG will faithfully retrieve the contradiction. |
Retrieval-augmented generation
At question time, the system searches your own content, pulls the relevant passages, and gives them to the model as context. The answer is grounded in documents you control and can be shown with its source.
| Solves | The model needing current, specific, organisation-owned knowledge |
|---|---|
| Does not solve | Teaching the model a new skill or output style on its own |
| Typical effort | Four to six weeks to a proof of value, longer to production |
| Running cost | Moderate. Tokens plus a vector index plus content pipelines. |
| Privacy position | Usually still a deployer. Personal information can be kept inside your tenancy. |
| Honest limit | Retrieval quality is content quality. If your document set is contradictory, RAG will faithfully retrieve the contradiction. |
Which approach does your problem need?
Three questions. The answer is indicative and takes about twenty seconds.
1. Does the answer depend on information that only exists inside your organisation?
2. How often does that information change?
3. Do you need the output in an unusual, highly consistent format every single time?
Answer the three questions above
The recommendation will appear here as you answer.
Prompting, retrieval, or fine-tuning
Three different techniques get sold as the same thing. They solve different problems, cost different amounts, and carry different obligations. Choosing wrongly here is the most expensive mistake available on a generative AI project.
Knowledge Assistant
What It Does
Answers questions from internal documents and references the source.
Common Use Cases
Policies, contracts, manuals, procedures
What You Need
A clear source of truth and suitable permissions
Team Copilot
What It Does
Drafts, summarises, and finds information within a team’s workflow.
Common Use Cases
Tender responses, claims summaries
What You Need
A business owner and a process ready to improve
Document Processing
What It Does
Extracts information, checks it against rules, and sends it to the right system.
Common Use Cases
Invoices, insurance, certificates, site reports
What You Need
A review threshold and a system of record
Content Generation
What It Does
Creates first drafts from approved templates and reference material.
Common Use Cases
Product descriptions, SOWs, client reports
What You Need
An approved template and human review
Classification & Routing
What It Does
Identifies incoming items and routes them to the first place.
Common Use Cases
Email triage, support tickets, complaint classification
What You Need
A clear classification system
Summarisation
What It Does
Utilises huge amounts of information to create concise, traceable short summaries.
Common Use Cases
Meetings, regulatory updates, tender documents
What You Need
A clear audience and purpose
How We Deliver
We take on a practical approach when it comes to moving AI from just a concept to a fully functioning final product. When finalising a generative AI development company, ensure you go with one that can work across data, architecture, testing, and governance—not just build the model.
Choose the Use Case
Our experts review potential use cases based on business value, data readiness, and risk. Then we choose one and set a clear metric to measure its impact.
Check Your Data
We check the data the system will use, where it comes from, how up-to-date it is, who has complete access to it, and where gaps exist. This helps us decide whether or not to move ahead, narrow the scope, or fix the data first.
Prove the Value
We create a working solution with the help of your real data and test it against real questions and expected outcomes. After four to six weeks, the results help determine whether the project should move forward.
Make it Production-Ready
Once the solution proves its value, we prepare the same for real-life usage. This includes access controls, monitoring, audit logs, cost controls, human review, and safeguards for cases where the AI is uncertain.
Measure and Hand Over
We measure the results against the original baseline, improve the system based on real usage, and train your team to run the tests and maintain it.
AI Use Cases That Fit the Work
The right AI use case often depends on the work, the data behind it, and how the result will be used. Work alongside an experienced generative AI development company to create lucrative solutions for any use case.
Mining and Resources
| Use Case | Challenge | Approach | Watch-out |
|---|---|---|---|
| Site Documentation Assistant | Procedures, necessary equipment manuals, and JSAs are spread across different systems. This makes it hard to find existing information. | Retrieve answers from approved documents along with the revision and approval date shown. | Document control comes first. Conflicting versions would still require a solution. |
| Shift & Incident Reporting | Insightful patterns often get buried in reports that teams don’t get a chance to read across. | Summarise reports into a daily digest with links and references to the original reports. | Someone needs to act on the insights, or the daily digest would add little-to-no value. |
Professional Services
| Use Case | Challenge | Approach | Watch-out |
|---|---|---|---|
| Tender & Proposal Drafting | Teams repeatedly rebuild capability statements and responses under tight deadlines. | Retrieve approved content and past submissions to create a structured first draft. | A reviewer should always own the final submission. |
Wholesale and Distribution
| Use Case | Challenge | Approach | Watch-out |
|---|---|---|---|
| Supplier Document Processing | Invoices, remittances, and delivery dockets most of the time come in different formats and are often entered manually. | Extract data, validate it against business rules, and route exceptions for review. | Review thresholds need to balance automation with accuracy. |
Government and Non-Profit Organisations
| Use Case | Challenge | Approach | Watch-out |
|---|---|---|---|
| Enquiry Triage & Response Drafting | Teams handle huge volumes of enquiries, with many covering the same recurring and repetitive questions. | Classify and route enquiries, then prepare a grounded draft for an officer to review. | AI-assisted decisions often require more additional transparency and logging. |
What to Consider
What Australian Businesses Need to Consider Before Deploying AI
As of now, Australia does not have a single AI act, but that does not mean AI operates without any rules or regulations or is off-limits. Existing privacy, security, and other laws still apply, alongside national guidance for responsible AI adoption.
Good governance does not have to be complicated. Start with a clear owner, basic documentation, an AI register entry, and defined review points. Build these into the project right from the very beginning instead of adding them just before an audit.
The 2024 Voluntary AI Safety Standard previously had 10 guardrails. However, in 2025, the National AI Centre introduced six crucial practices that cover both AI developers and users.
If you adapt, build, train, or improve an AI model, you may be considered an AI developer. However, when personal information comes into the picture, additional privacy requirements are applicable.
Australian privacy rules require businesses to take reasonable steps to keep personal details accurate. This is necessary when AI is being used to create, process, or share that information.
Ensure you know where your prompts, sensitive documents, and outputs are processed and stored. Data location and retention should be decisions you make, not defaults you inherit from an AI provider.
AI systems must work within your existing security framework. Australian guidance also points firms toward measures like Essential Eight to effectively manage cyber risks or potential threats.
It’s necessary to have someone responsible for the AI system. One who can review or override output whenever needed. Human oversight must be a part of the workflow, not something that’s added afterwards.
How engagements are shaped
Three shapes cover most of what we do. Pricing is quoted per engagement after the first conversation, because the variable that moves it is your data, not your user count.
Use case assessment
Two to three weeks. Candidate use cases scored, one selected, data readiness assessed, value hypothesis written. Ends with a recommendation to proceed, narrow or stop, and we do say stop.
Proof of value
Four to six weeks. A working system on your real data, measured against an evaluation set built with your experts. Fixed scope and fixed price.
Production build
Eight to sixteen weeks depending on integration and governance scope. Includes hardening, monitoring, the regression suite and handover to your team.
Ongoing support
Optional and separately priced. Some clients take it, others take the handover and run it themselves. Both are fine and we will not price the build on the assumption of the retainer.
From AI Idea to Production
Most projects follow one of the four stages. The scope and price are defined after the initial discussion on the basis of your use case, data, integrations, and governance needs.
Use Case Assessment
Our experts assess potential use cases, check your data readiness, and identify where AI can make a huge impact. You leave with a clear recommendation: proceed, narrow the scope, or stop.
Proof of Value
We create a working solution with the help of your real data and test it against agreed evaluation criteria. The scope and price are fixed upfront.
Production Build
Once the use case is proven, we take it into production. This can include integrations, security, monitoring, testing, and handover to your team.
Ongoing Support
You can keep us involved for ongoing support and improvements, or take the solution over with your own team. The choice doesn’t affect how we scope the initial build.
FAQ
Frequently Asked Questions
What is generative AI development?
Simply put, generative AI development is the process of building AI systems that use large language models (LLMs) with your data, workflows, and business processes.
How does RAG differ from fine-tuning?
RAG gives a model relevant information from your data when it answers. Fine-tuning, on the other hand, trains it on examples to change how it responds.
Does fine-tuning create obligations under Australian privacy law?
Fine-tuning can create obligations regarding privacy, usually when personal information is used to adapt models. The requirements usually depend on how the data and model are used.
How do you stop it from making things up?
You cannot completely remove the risk. We reduce it by giving the AI trusted sources to work from, showing where its answers come from, keeping its scope clear, and testing the same with real questions.
Why not just go for ChatGPT or Copilot?
Obviously, you can go for them when it comes to everyday tasks; they’re a great fit. A custom solution makes more sense when you need AI to work with your own data, connect to business systems, follow specific controls, or deliver measurable results.