CONSTRUCTION AI AGENTS

AI agents for construction,
built where the work happens.

Operational agents for quoting, scheduling, reporting and document-heavy construction workflows, with human confirmation points and explicit data boundaries.

Meet the agents · How we deploy AI

The short answer.

• What they are: Agents embedded in real construction systems. They read defined operational data, draft the repetitive work and flag exceptions; your people confirm every decision.
• Who they're for: Developers, builders, manufacturers, trade contractors and construction groups with repetitive, document-heavy workflows.
• What makes them different: Each agent runs on a system in live use inside DDDI Group's operations, not a chatbot bolted onto a website.

The agents available today.

• Quoting Agent: Drawings and specifications into a priced draft quote, estimator-approved.
• Scheduling Agent: Programme changes traced and re-sequenced, scheduler-approved.
• Reporting Agent: Live operational data into stakeholder-ready report drafts.
• Document Intelligence: The parsing layer beneath every agent: drawings, specs and contracts as structured data.

Deployed the accountable way.

Every agent ships inside a defined workflow: what it may read, what it may produce, who confirms it, what gets logged, and where exceptions go. Rollout is staged: shadow, assisted, then routine.

Our deployment approach

Proof, not promises.

The systems these agents run on are already in live deployment, and the drawing-review research behind document intelligence was presented at WSBE26.

• Quoting engine: Live for a manufacturer
• Proven inside: DDDI Group operations
• Research: WSBE26, Melbourne

See the deployments

Questions, answered plainly.

Are these chatbots?

No. They are workflow agents connected to operational systems, documents and role permissions. They draft, propose and flag inside a boundary.

Can the agents act without approval?

Commercial, legal, site-critical and externally issued outputs always require human confirmation. Routine low-risk steps can run assisted.

Where should we start?

With your most repetitive workflow. We define the data boundary, run the agent in shadow alongside your current process, and compare before anything goes live.

Put an agent on a workflow