AI consulting & engineering in Australia
ComplxAI provides AI consulting in Australia from the engineers who build what they recommend. Every recommendation arrives with an architecture, a cost model and a delivery plan, and the same small senior team ships the system.
- The first two conversations are free
- Priced per stage, exit at any boundary
- You own the IP from day one of any paid stage
For the people who choose, fund and live with the result
Owners and executives ask where AI would actually pay. Product, operations and technology leaders ask whether an idea is feasible, what it costs to run and how it passes security review. We answer both, whether you have a platform team or no engineers at all.
Either way the answer has one shape: a prioritised use-case map, a feasibility verdict, an architecture with a cost model, and a plan the same team executes. Need only the roadmap? AI strategy consulting stops there. Decision already made? Go straight to custom AI development.
A good fit if…
- You want an independent, technical view on where AI pays
- A vendor proposal has landed and nobody can test its claims
- A pilot exists and nobody can say what it costs to run
- You need governance designed before anything ships
What the work covers
Eight disciplines. The first four belong to Discovery and Strategy, the rest to Implementation blocks, with an exit at every boundary.
- Opportunity identification
- Workshops with the people who do the work, then a candidate map scored by impact, effort and risk, dependencies explicit.
- AI strategy
- A board-ready implementation plan: build, buy and defer calls, sequencing, a budget by stage and the metric each project is judged on.
- Technical feasibility
- A scoped test on a sample of your real data: can a model clear your accuracy bar, and what would each transaction cost at your volume. Numbers first, then a price.
- Architecture and cost model
- A reference design fixing execution model, data locations, model choice per task, approval points and integrations, costed per block and per transaction.
- Implementation and integration
- Working software every couple of weeks, connected to Xero, MYOB, HubSpot, Salesforce, document stores and internal databases. Detail under AI engineering.
- Productionisation
- A working prototype taken through evaluation, error handling, observability, access control and infrastructure-as-code until it can be operated, audited and handed over.
- Governance, security and privacy
- Model risk assessment, approval workflows, audit trails and responsible-use policies, with data residency, access control and PII handling designed in.
- Measurement and optimisation
- A baseline before the build, a KPI owned by the person accountable for it, an evaluation set your experts maintain, and scheduled tuning of prompts, routing and spend after launch.
Six layers of a recommendation you can build from
Advice you cannot build from is not finished. Each recommendation carries these six layers, so the build can start without a second scoping exercise.
- Business casewhyScored use-case mapBaseline metricsKPI targetsBuild, buy or defer
- ArchitecturewhatReference designModel strategyData flowsIntegration contractsApproval points
- Cost modelhow muchBuild cost per blockRun cost per transactionToken forecastReview time
- GovernancecontrolsModel risk registerApproval workflowsAudit trail designResponsible-use policyData residency
- Delivery planwhen12-week blocksTeam and rolesExit pointsHandover
- Platformwhere it runsYour AWS accountAny AWS region, Sydney for Australian residencyLambda and Step FunctionsPostgres + pgvectorBedrock, OpenAI and Anthropic APIsTerraform/CDK
Six stages from strategy to a system that keeps improving
The spine of every engagement, from a single feature to a programme across an operation. It maps onto the five commercial stages in how we work, with the same people at every step.
- StrategyScored, sequenced opportunity map with build, buy or defer calls, a baseline and a budget by stage.
- ArchitectureReference design, model strategy, data boundaries, approval points and the cost model.
- EngineeringRetrieval, orchestration, evaluation harness and interfaces, built in vertical slices.
- IntegrationOutputs written into Xero, HubSpot, Salesforce or your database, with reconciliation.
- DeploymentInfrastructure-as-code in your own cloud account, access control, monitoring, runbooks.
- OptimisationEvaluation scores, spend and the agreed KPI reviewed on a schedule; every change passes a regression gate.
Four engagement shapes and what each one produces
Representative shapes, no client names; for named, client-reported work see the systems we have shipped.
AI roadmap for a professional-services firm
Partners have a dozen ideas and one budget. Workshops across practice groups, every candidate scored, a sequenced plan costed per stage, first project ready to start.
Feasibility and architecture for an AI feature in a SaaS product
The product team cannot price the feature. We test the model on samples of real customer data, design it into the existing architecture and cost it per tenant. If the numbers hold, it continues into AI product development.
Productionising an internal proof of concept
The pilot works on curated examples. We build the evaluation set, redesign the pipeline around queues and approvals, add observability and access control, and deploy to your account with runbooks. Read AI proof of concept vs production for the usual failure points.
AI governance and an agent programme for a regulated business
Agents could take intake, triage and follow-up off the team, but the regulator will ask who approved what. Risk register and responsible-use policy first, then agents with confidence thresholds, human approval and an audit trail. See AI agent development.
Advice-only consulting vs ComplxAI
Both have a place. Here is where an advice-only AI consultancy and ComplxAI differ.
| Concern | Advice-only consulting | ComplxAI |
|---|---|---|
| Deliverable | A strategy document, sometimes with a high-level roadmap. | Strategy, architecture, cost model, governance design and delivery plan, then the working system. |
| Who builds it | A separate vendor or your internal team. | The same small senior team that wrote the recommendation. |
| Feasibility validation | Typically desk-based: references and interviews. | Scoped tests on samples of your data, cost projected at your volume, before the recommendation is issued. |
| Architecture and cost model | Often out of scope, or indicative only. | Specified to the service, priced per block and per transaction, assumptions listed. |
| Accountability for outcomes | Ends when the report is delivered. | Continues through implementation, deployment and optimisation, measured against the baseline and KPI agreed in Strategy. |
| IP ownership | Varies; frameworks often stay with the consultancy. | Yours from day one of any paid stage: documents, code, prompts, evaluation sets, infrastructure. |
| Pricing model | Commonly time and materials, or a retainer. | Each stage priced in writing before it begins; exit at any boundary; no mandatory retainers. |
Controls designed in before the first model call
In regulated and customer-facing settings the controls are the product, so we design them with the architecture and scope them into the first Implementation block.
Governance
A model risk register scored by consequence and reversibility, approval workflows so consequential actions wait for a person, audit trails of model and tool calls at the depth the risk warrants, and responsible-use policies.
Security and privacy
Storage, databases and orchestration are deployed in the region you need, in your own AWS account or one we set up for you; for Australian data residency that is the AWS Sydney region. Any offshore model API is named in the plan, with PII redaction designed around it. Least-privilege access control and tenant isolation throughout.
Human oversight
Confidence thresholds set what runs unattended and what stops for a human, and each reviewer decision becomes an evaluation example. We do not promise full autonomy where a person should sign off.
AI consultants who stay for the build
We build what we recommend
The people in the strategy workshop are the people in the code review. No hand-off, no re-scoping by a second team: the architecture we recommend is the one we ship.
Stop at any boundary, keep the IP
Two free conversations, then Discovery, Strategy and 12-week Implementation blocks, each priced in writing before it begins. Stop or take the work in-house at any boundary; everything from a paid stage is yours from day one. No mandatory retainers.
Systems we have shipped
DisabilityAssessments automated 90% of operations. Modedu reported 2× revenue within six months, and ARR from $400k to $1M. Manual workload at Luxpip fell 75%. Outcomes as reported by each client.
Where an engagement usually goes next
AI strategy
A scored roadmap with build, buy and defer calls.
AI strategy consultingAI development
Applications built around a model, end to end.
Custom AI developmentAI engineering
Retrieval, evaluation, observability and AWS deployment.
Production AI engineeringAI agent development
Tool-using agents with approval steps and an audit trail.
Custom AI agentsAI product development
AI-native products and AI SaaS, discovery to scale.
AI product developmentGenerative AI
Model strategy, RAG and LLM systems.
Generative AI consultingWhat buyers ask before engaging AI consultants
The rest we answer on the free intro call.
Book a free intro callHow is the work priced, and how long does it take?
The first two conversations are free. Discovery runs two to four weeks from a fixed fee, Strategy two to four weeks priced upfront, Implementation per 12-week block, each quoted in writing with an exit at every boundary. The AI project cost calculator gives a rough range. For market context see the State of AI Development in Australia 2026 report.
Do we need clean data before we start?
No. Discovery assesses data quality, and first projects are often chosen because their data is already good enough. Where a valuable use case is blocked by data, the plan sequences the fix.
Will you tell us to buy rather than build?
Yes, when that is the right call. Strategy makes an explicit build, buy or defer decision per use case, licence cost against build and run cost, and we are not tied to any vendor's platform.
How do you work with our internal IT or engineering team?
As one team. Your engineers join architecture sessions and code reviews from the first block, the repositories and cloud account are yours, and handover is included, so you can take it in-house at any block boundary.
Do you only work with large language models?
No. LLMs suit document, language and workflow problems; forecasting, matching and anomaly detection often do better with classical machine learning or plain rules. Feasibility testing decides. Where LLMs win, LLM consulting sets the model strategy; we do not train foundation models.
Can you deliver for businesses in Brisbane, Sydney and Melbourne?
Yes. We are remote-first across Australia in Australian business hours, onsite where it helps, and have delivered for organisations in Brisbane and Sydney. See AI consulting in Brisbane, AI consulting in Sydney, AI consulting in Melbourne and where we work for the detail.
Bring the question you are being asked
Where AI would pay, whether an idea is feasible, or what a pilot costs to run. Two free conversations first; after them, nothing begins without a written price.