AI & AGENTS
Operational AI agents
for construction workflows.
Agents sit on top of a system that already controls the job. They read approved data, draft the repetitive work and flag exceptions. Your team still confirms every commercial, legal and site-critical decision.
Cyberate builds construction AI agents for Australian residential construction businesses: builders, developers, manufacturers, trades, project managers and multi-company construction groups.
A draft quote an agent can prepare. A person still confirms before anything is sent.
- Built on: Live operational systems
- Agents: Draft, propose, flag
- Humans: Confirm before anything is sent
- Rollout: Shadow → assisted → routine
SYSTEM OR AGENT
A system runs the job.
An agent only drafts inside it.
The two menus are not two products. Start with a system if the work itself is still uncontrolled. Look at an agent if the work already runs and the repetitive draft is the bottleneck.
| System | Agent | |
|---|---|---|
| What it is | The workflow itself: quoting, scheduling, procurement, reporting, group finance | A draft, proposal or flag inside that workflow |
| Who decides | The process is enforced in the system | A person confirms before anything is sent, priced or published |
| Start here if | The job still lives in spreadsheets, calls or one person's head | The job already runs and you want the repetitive draft done faster |
One operating pattern.
Every agent.
Every Cyberate agent runs the same accountable loop: it reads structured data, drafts the repetitive work, flags what it isn't sure of, and hands the decision to a person before anything changes.
Read approved data. Draft defined outputs. Flag uncertainty. A human confirms. Every step is logged.
Agent operating pattern: construction documents and site data flow through document intelligence into domain agents, which draft, propose and flag; a human confirms before any system update.
THE ADOPTION GAP
Innovation follows scale.
Agents break that link.
The Productivity Commission found only 35% of construction firms are innovation-active - but among firms with 20 to 199 employees the rate is nearly 80%. The gap between those two numbers is the report's quiet headline: firms with 20 to 199 employees innovate at nearly 80%, so the industry's innovation problem is substantially a scale problem. An agent running on a proven system is scale you subscribe to, not headcount you hire.
Figures as published in the Commission's February 2025 research paper, analysing data mostly to 2023-24.
WHY IT'S DIFFERENT
We don't start with a model.
We start with an accountable workflow.
What may the agent read? What may it draft? Who confirms it? What gets logged? Where do exceptions go? Cyberate designs these boundaries before an agent is allowed into the workflow.
| Generic AI tool | Generic automation | Cyberate agent | |
|---|---|---|---|
| Starting point | A chat box | A fixed rule | An accountable workflow |
| Understands documents | Loosely, on request | Barely | Drawings, specs, contracts, parsed with confidence flags |
| When context changes | User re-prompts | It breaks | The agent flags the exception to a person |
| Permission boundary | Weak | Static | Role-based, defined before deployment |
| Connection to systems | Output pasted by hand | Single-task script | Built on live quoting, scheduling, reporting and finance systems |
| Who decides | Unclear | Nobody, until it fails | Your team confirms; every step is logged |
The three we built first.
- Quoting Agent: From specifications and drawings to a priced draft quote, built on our automated quoting engine. (Commercial) (Reads: drawings, schedules, specifications, product rules; Produces: draft quote with confidence flags; You confirm: pricing, margins, release)
- Scheduling Agent: When plans change, it traces the dependencies and proposes a controlled re-sequence. (Delivery) (Reads: sequences, dependencies, crews, deliveries; Produces: proposed re-sequence and conflict warnings; You confirm: the new schedule, before it lands)
- Reporting Agent: Live operational and financial data assembled into stakeholder-ready report drafts. (Management) (Reads: live project, cost and progress data; Produces: report drafts with narrative summaries; You confirm: sign-off before anything is issued)
The wider agent suite.
Each agent below extends the same pattern over a system already proven in live use: shadow first, human-confirmed, inside an explicit data boundary.
- Compliance Review Agent: Approval conditions, drawings and project documents turned into a reviewable compliance workflow. (Compliance & risk)
- Feasibility Intelligence Agent: Site, planning and market constraints structured into early-stage development intelligence. (Commercial)
- Procurement Agent: Long-lead items, supplier confirmations and programme dependencies watched before risk reaches site. (Commercial)
- Call & Confirmation Agent: Confirmation calls made and missed calls answered on your existing phone system, every call recorded with a written outcome. (Delivery)
- Materials & Labour Agent: Site progress, crew availability and material demand matched before the day starts. (Delivery)
- Development Management Agent: Milestones, approvals and exposure across projects, with exceptions surfaced early. (Management)
- Variation & Claims Agent: Commercial change events captured with evidence and drafted for human review. (Commercial)
- Finance & Cashflow Agent: Cash position drafts, variance flags and payment-run reviews from live group finance data. (Management)
- Email Review Agent: Outbound correspondence checked for unconditioned commitments and claim triggers before it sends. (Compliance & risk)
- Executive Briefing Agent: Board and executive updates drafted from live data, every claim linked to its source. (Management)
Every agent stands on
a running system.
No agent here floats free. Each one extends a Cyberate system, so its data, permissions and outputs are already part of an operating workflow.
| Agent | Built on | System it updates | |
|---|---|---|---|
| Quoting Agent | Automated Quoting + Document Intelligence | Quote drafts, estimator queue | System → |
| Scheduling Agent | Scheduling & Coordination + Materials & Labour | Programme proposals, conflict flags | System → |
| Reporting Agent | Reporting & Dashboards | Report drafts, variance narratives | System → |
| Compliance Review Agent | Compliance & Approvals + Document Intelligence | Condition register, evidence queue | System → |
| Feasibility Intelligence Agent | Feasibility & Site Intelligence | Site summaries, constraint tables | System → |
| Procurement Agent | Procurement + Scheduling | Long-lead flags, order drafts | System → |
| Materials & Labour Agent | Materials & Labour | Crew and delivery proposals | System → |
| Development Management Agent | Development Management | Milestone exceptions, portfolio flags | System → |
| Email Review Agent | Document Intelligence + rule library | Review verdicts, conditioned rewrites | Agent → |
| Variation & Claims Agent | Document Intelligence + Compliance | Claim drafts, evidence packs | Agent → |
| Finance & Cashflow Agent | Group Finance & PM Platform | Position drafts, payment-run reviews | System → |
| Executive Briefing Agent | Reporting + Group Platform | Board pack drafts, what-changed notes | System → |
Drawings in. Structured data out.
Plans, specifications and contracts are where construction data hides. Our document intelligence layer parses them into structured data: the shared foundation under every agent in the suite.
Built for accountable AI deployment.
Every Cyberate agent is deployed inside an accountable workflow: what it may read, what it may produce, who confirms it, what gets logged, and where exceptions go.
- Quoting: Live deployment
- Proven inside: DDDI Group
- Platform: Multi-company group
- Research: WSBE26, Melbourne
- Defined data boundary: What the agent can read, where it runs, what never leaves.
- Role-based permissions: The agent sees what the role it serves would see. No more.
- Human confirmation: Commercial, legal and site-critical outputs are approved by people.
- Audit logs: What was read, produced and confirmed is traceable.
- Exception routing: Anything the agent isn't sure of goes to a person, not a guess.
- Staged rollout: Shadow, then assisted, then routine. Never big-bang.
Find your first agent.
Pick the workflow that hurts most. We'll show you the agent, the system underneath it, and the safe way to start.
AI agents, in plain answers.
What AI agents does Cyberate build for construction?
Cyberate builds AI agents for quoting, scheduling and reporting, plus a document intelligence layer that parses drawings, specifications and contracts into structured data. Each agent runs on top of a real operational system in live use, not a chatbot bolted onto a website.
Do the agents replace people?
No. Agents draft, propose and flag; people confirm. Human confirmation points are designed into every workflow, and exceptions always route to a person.
What data do the agents use, and where does it stay?
Agents work inside an explicit data boundary agreed before any build: which data they may read, where processing happens, and what never leaves. Their access mirrors your role-based permissions, and every action is auditable.
How does deployment work?
In stages: the agent first drafts alongside your current process (shadow), then enters the workflow behind a human confirmation step (assisted), and only then handles routine cases with spot-check review. No big-bang AI programmes.
Is this just a chatbot?
No. Cyberate agents are built inside defined workflows and connected to operational systems, documents and role permissions. They draft, propose and flag within a boundary; they don't improvise from the open internet.
Can agents act without approval?
Routine low-risk steps can run in assisted workflows, but commercial, legal, site-critical and externally issued outputs always require human confirmation.
Can we run the system without an agent?
Yes. Agents draft inside a system that already runs the work. If you do not use an agent, the same quoting, scheduling or reporting system still operates with your team doing those steps by hand.
Can Cyberate build a new agent for our workflow?
Yes, if the workflow is known, the data boundary can be defined and the output can be reviewed by the right human role. That test is the design method, not a disclaimer.
Which agents are available today?
The Quoting, Scheduling and Reporting Agents, each built on an operational system in live use, plus the document intelligence layer beneath them. The wider suite extends the same pattern over systems already running, and ships agent by agent, shadow-first.