Search Precision Consulting
Type to search · ↑↓ to navigate · Enter to open · Esc to close
AI Automation and Agents
AI automation and AI agents
measured first, then built to work within limits you set
How many hours a week does your team spend on work a well-governed AI agent could do? We measure it first, so the saving is a number from your data. Then our engineers build agents that complete multi-step work inside your systems, within limits you set, with every action recorded in an audit log we configure for the build and a named person accountable for each decision that affects a client, an employee or the public.
The first conversation is confidential, without obligation and without charge.
Have a named process and data access ready? Go straight to a proof of value
What can AI automation do for an organisation like yours?
It does the preparation in repetitive, multi-step work, so your people spend their time approving and deciding.
Typical work that suits it:
- reading invoices, forms and dockets into a finance or operations system;
- matching records across systems and explaining the exceptions;
- sorting incoming enquiries and drafting replies for a staff member to approve;
- answering staff questions from approved content, with the source cited.
Most mid-sized organisations have started, and few have finished. Among Australian firms of 200 to 500 staff, 78 per cent report some degree of AI adoption and 16 per cent report broad use. What stands between the two is usually the process redesign, data, integration, controls and change that let a use case run at production volume. That is the work we do.
What are AI agents and agentic AI, in plain terms?
An AI agent is software that works through a task in steps, choosing the next step within limits you set. It reads, looks things up, matches, drafts and routes, and hands anything consequential to a person.
Rule-based automation follows the steps it was given and stops when something unexpected arrives. An agent can handle more of that variation, which is exactly why it needs clear limits.
Ours work to one design rule. Agents read, match, flag, draft and recommend. A named person approves anything that moves money, changes a supplier's bank details, sends a demand, makes a decision that significantly affects an individual, reports a figure to a board, or changes production. An agent never raises and clears its own exception.
Who is this for, and who is it not for?
It is for chief operating officers, chief financial officers, chief information officers and heads of operations who have repetitive, multi-step work that crosses systems and has a volume and a cost they can measure.
A good fit:
- A process your team runs every week, with a volume you can count
- Data the process uses, held in systems you can reach
- A person who will own the result after go-live
- A sponsor who wants the saving measured and is prepared to sign a baseline
- Mid-market firms, enterprises, government delivery owners and private equity portfolio companies
Not a fit:
- A one-off task, or a process that changes every month
- Anything that would make significant decisions about people with no human review
- An AI receptionist, a chatbot for the public, a voice agent or a dispatch agent. We do not build them
What does a build include?
A build puts the agent into production on your systems, with the controls, the measurement and the hand-over you need to run it.
- Production deployment, with the human approval points agreed in the business case
- The audit log, configured for the build
- An evaluation suite, delivered as a named artefact, that tests the agent against the agreed measures before go-live and after every change
- Hosting in your own cloud account, on an Australian-region service by default (see below)
- A runbook, and training for the people who will use and own the agent
- Formal reviews after go-live, scheduled when the build is signed, with a named analyst measuring the result and your data access for the review period written into the contract
- An internal go-live readiness review by a practitioner outside the build team, against your standard. It is labelled internal and is never called independent
- Hand-over of the source code, the infrastructure as code, the runbooks and the evaluation suite
Planned time: eight to sixteen weeks from build start to go-live, set mainly by the systems involved and your approvals.
What are the steps from one process to a system in production?
Four: Sprint, Prove, Build and Run. You decide after the Sprint and again after the proof of value whether to go further.
Planned elapsed time One to two weeks to a costed answer
| Step | What happens |
|---|---|
| Sprint | One named process, costed from your own data, ending in a go or no-go recommendation on a proof of value. One to two weeks from the start date. |
| Prove | A working agent on your data in a controlled environment, with a person approving everything it produces, measured against written success criteria and a baseline you sign. Planned at three to four weeks from data access. |
| Build | The agent in production on your systems, with the approval points, the audit log, the evaluation suite and the hand-over described above. Eight to sixteen weeks from build start to go-live. |
| Run | Your team runs it with the runbook and the evaluation suite we hand over. Managed operation by Precision is available on request. |
These are planned durations. Each proposal states its own dates, and we replace planned figures with measured ones as engagements complete.
Where should we start?
Start with one named process. There are three ways in, depending on how far you have got.
| If you | Start with | What you get | Time |
|---|---|---|---|
| Have one process in mind and want a costed answer | Use Case Sprint | A baseline, a costed proof of value and build option with its expected payback period, and a go or no-go recommendation | Planned at one to two weeks |
| Have a named process and data access in place | Proof of Value | A working agent on your data, measured against a baseline you sign, ending at a go or no-go gate | Planned at three to four weeks from data access |
| Have needs across several functions, or no process named yet | AI Readiness Assessment | Ranked use cases valued from your own data, and a costed roadmap to a first proof of value | Planned at three to six weeks |
What does the Use Case Sprint include?
One named process, costed in one to two weeks.
You bring a process your team runs every week. We measure what it costs you today in time, money and errors, from your own data. We test whether the process and its data will bear automation, and give you a costed option for a proof of value and a build, with the expected payback period and the assumptions behind it. We also tell you what not to automate and where it would run. You finish with a go or no-go recommendation on a proof of value.
Before day one, you name the process and its owner, and we agree the data we need and how we reach it. In week one we walk through the process with the people who run it, take a data extract, and measure time, cost and errors. In week two we cost the options, set out what not to automate and where it would run, and deliver the go or no-go recommendation and discuss it with you.
You receive:
- a first baseline of time, cost and errors for the process, which you confirm and sign in the proof of value;
- a view of whether the process and its data are ready;
- a costed proof of value and build option with its expected payback period;
- the parts of the process we recommend leaving with people;
- the hosting arrangement we would use;
- a go or no-go recommendation.
Who does the work: a senior practitioner leads scoping and signs the recommendation. An engineer does the process and data work. The statement of work sets out the days by role.
Time: one to two weeks from the start date.
Our interest: Precision builds AI, so a Sprint can lead to work for us. We say so when the Sprint is scoped, and we write the recommendation to stand on its own, so you can take it to any provider.
What does a proof of value include?
A working agent on your data, measured against a baseline you sign.
For a process that is already named, with data access in place. We build a working agent in a controlled environment on your data, with a person approving everything it produces, and measure it against written success criteria and the baseline you sign. It ends at a go or no-go gate, and you make the decision.
Time: a working agent planned at three to four weeks from data access.
You keep: the signed baseline, the success criteria, the measured results and the go or no-go recommendation.
Who builds it, and who is accountable?
A Precision engineer who scoped your build leads it through go-live, and every engineer on the team works to our method and our bar.
You will not be handed to an account manager. The statement of work names the lead engineer and any contractor on the team, and a named senior practitioner is accountable for the engagement.
We build agents that complete multi-step work within limits you set, with every action recorded in an audit log we configure for the build, and a named person accountable for each decision that affects a client, an employee or the public.
When we have built a system, we never review it independently. Any independent review of it goes to another firm. Read our independence and conflict rules
How do you keep an agent under control?
By design: every agent works inside limits you set, records every action, and hands every consequential decision to a named person.
- Limits. The agent can reach only the systems, records and actions listed in its design, and the design rule above decides what a person must approve.
- A record of everything. Every action is written to an audit log configured for the build.
- Tested before every change. The evaluation suite runs before go-live and before any change to the model, the prompts or the integrations.
- Designed to fail open. If an agent stops, the work returns to your existing manual process and nothing is blocked. A kill switch stops it at once.
- Privacy. The Privacy Act's automated decision rules apply from 10 December 2026, and a person approving each decision does not on its own take a system outside them, so every build hand-over includes input on APP 1.7 for your counsel.
This is not legal advice. Your counsel decides the legal position.
Where will it run, and can we host a private LLM in Australia?
Yes. By default we run each build in your own cloud account, on a model service whose processing is confined to Australian regions, with credentials you own and the least access our engineers need. Other arrangements, including your own premises, are available on request.
Sovereign and Australian-hosted options
- Sovereign:
- on your premises, or with an Australian-owned provider, operated and supported from Australia. Available on request.
- Australian-region:
- a global provider's Australian region, configured so your data is stored and processed in Australia. The provider remains subject to the laws of its home country.
Your data is not used to train public AI models.
Every build proposal carries a hosting statement naming each service, its region, the model family, whether any processing leaves Australia, and where our people and tools access your data from.
We use enterprise model services whose terms exclude training on your prompts and outputs, or models that run inside your own environment. We do not train shared models on one client's data.
We configure any load balancing to stay within the provider's Australian regions. We tell you where our own people and tools access your data from, which sub-processors are involved, and each provider's Hosting Certification Framework and IRAP status where you need it, as the provider states it.
Do you offer custom AI development?
Yes, where the process, the data and the value case are clear enough to scope.
Custom AI development covers agents and integrations built for your systems and your rules: search and answers over your own documents with the source cited, agents that work across your ERP, document stores and service tools, and the evaluation suite that shows each one works on your data. It starts the way every build starts, with a Sprint or a proof of value, so the price follows the evidence.
- What you own. You own your data and your configuration. We own our reusable components and give you a perpetual licence to them, which extends to your contractors, reviewers and successors and includes the right to modify. Reusable components contain no client data or client confidential information.
- What you receive. Source code, infrastructure as code, runbooks and the evaluation suite, with every build.
- Where we say no. If a request falls outside what we build well, we say so at scoping.
Who runs it after go-live?
Every build is handed over ready for your team to run, with the runbook and the evaluation suite. Managed operation by Precision is available on request.
Our monitoring of an agent we built is part of running it, and we never describe it as independent oversight. If you need independent oversight of an agent we run, it goes to another firm.
For APRA-regulated clients, we build and hand over, and you or your existing operator run the system.
Third-party AI usage is charged at the provider's cost, shown on each invoice.
Can your engineers work inside our team?
Yes, on request: an engineer or a small team working inside your team to your standard, engaged and insured through Precision.
- for three to twelve months, or as an engineering capacity retainer of 20 to 40 days a quarter;
- supplied through our contractor platform;
- a senior practitioner leads the engagement;
- if a person we supplied helps design or build a system, we do not independently review that system. We disclose any placement in a programme we review or recover.
What does it cost, and how is the return measured?
Every engagement is scoped to your situation and priced in the proposal. A build is priced after the proof of value, when the process and the data are known.
Every build carries a business case that states its expected payback period and the assumptions behind it. We measure the result at a formal review after go-live and report it against that case.
Our clients have seen a return on their investment within 90 days of go-live. For suitable use cases, a return can be expected within 90 days of go-live, measured against the baseline we agree with you before we build.
What will you not build?
We decline work where the risk to people outweighs the value, or where software you already pay for does the job.
- Facial recognition, in any form
- Agents that make significant decisions about individuals with no human review
- Personal financial advice, credit assistance or Statement of Advice drafting. Licensees who want their own advice or credit automation reviewed can ask for an Independent AI Review. About the Independent AI Review
- AI receptionists, public chatbots, voice agents and dispatch agents
- Analytics over HR records, CCTV or workforce telemetry
- Bespoke capture where a product you already own reads the documents. We build the exception and control layer on top of it
Common Questions
The questions we are asked most
How much does AI automation cost in Australia?
It depends on the number of systems involved, the state of the data and the approvals the process needs, so we price in stages. Every engagement is scoped to your situation and priced in the proposal, and a build is priced after the proof of value, when the process and the data are known.
How long does it take to get an AI agent into production?
Plan on a working agent three to four weeks from data access in a proof of value, and eight to sixteen weeks from build start to go-live. Data access and your approvals usually set the pace. Each proposal states its dates, and we replace these planned figures with measured ones as engagements complete.
Can AI automate accounting, data entry or email?
Parts of each, with a person approving. AI can read documents into a ledger, match invoices to orders and receipts, and sort incoming email and draft replies. The steps that post, pay or send a commitment stay with a person. A Use Case Sprint measures how much of your process suits automation before you spend more.
What is agentic AI?
Agentic AI describes systems that work through a task in steps, choosing each next step within limits and using tools such as search, your business systems and your document stores. In our builds an agent prepares the work and a named person approves anything consequential.
Is AI automation the same as robotic process automation?
No. Robotic process automation repeats fixed steps on screens and stops when something unexpected arrives. AI automation can read unstructured documents and cope with more variation, which is why it needs approval points and an evaluation suite. Many processes use both.
Do we need to replace our systems first?
Usually not. We build agents that work with the systems you run today, through their interfaces and exports. We check each platform's terms before any agent reads its data, because some platforms restrict how their data may be used with AI.
How we work, in writing
Start with one named process.
Bring us a process your team runs every week.
The first conversation is confidential, without obligation and without charge.