Live concept demo for retail construction workflows

Turn messy plans, redlines, RFIs, and permit packs into one coordinated agent loop.

This is a mockup of how G Store could combine in-house designers with OpenClaw-style agents to accelerate construction drawing review, drafting paperwork, revision control, scope extraction, and rollout coordination, without replacing the humans doing the thinking.

6 specialised agents working across the same drawing set and document trail
14 construction outputs drafted from one upload, from discrepancy reports to permit-ready packs
1 shared source of truth for revisions, comments, RFIs, and sheet-level decisions
openclaw://retail-fitout-agent-stack
Agent swarmjob run #0187
📐 Plan reader
active

Reads PDF drawing sets, spot-checks dimensions, extracts room labels, identifies missing annotations, and compares revision deltas.

  • Flags missing accessibility note on sheet A-4.2
  • Detects elevation mismatch between plan and schedule
🧾 Permit pack assembler
drafting

Builds transmittals, material schedules, compliance summaries, legend consistency checks, and issue logs for approval rounds.

✍️ RFI + variation writer
queued

Turns drawing conflicts into clean RFIs, variation notes, subcontractor briefs, and email-ready responses for clients or certifiers.

🏗️ Rollout board
watching

Keeps every site, version, issue, owner, and approval status synced so the latest plan set is never a guessing game.

ENTRY / BRAND WALL SERVICE COUNTER STOCK DISPLAY BOH Rev C • Sheet A-4.2 Scale 1:100
Compliance gap detected Agent flagged missing accessibility clearance note near counter return. Drafted correction note and certifier summary.
Revision delta Plan reader spotted BOH door swing change between Rev B and Rev C, then drafted the issue-log entry automatically.
Output bundle ready Marked-up drawing summary, schedule cross-check, RFI draft, permit pack cover sheet, and transmittal all assembled.
Illustrative concept only, built to show the workflow and product direction. Real outputs would be driven by G Store drawing sets, standards, and approval logic.
What the agents actually do

They do the repetition, the comparisons, and the paperwork. Your team keeps the taste and judgement.

The point is not “AI draws buildings alone.” The point is that your designers and project leads should not waste hours chasing revision mismatches, manually building construction paperwork, or rewriting the same scope notes for the sixth time.

01

Drawing intelligence

Agents read sheet sets, compare revisions, extract room labels, check schedule consistency, and surface likely errors before they get expensive.

Revision compare Callout checks Legend consistency
02

Drafting paperwork

Once the plan is understood, agents generate the ugly but necessary layer: issue logs, transmittals, RFIs, cover sheets, variation notes, schedules, and permit-pack structure.

RFIs Issue logs Permit docs
03

Rollout coordination

Every store, sheet, status, owner, and blocker can live in one operating layer so the latest version is obvious and approvals do not vanish into inboxes.

Site tracker Owner routing Approval history
Pilot workflow

A realistic first use case for G Store

1

Upload the live job pack

PDF plans, sketches, scope brief, brand guidelines, standards, and any prior revision notes go into one job space.

2

Agents read, compare, and organise

Plan-reading agents break the drawing set into rooms, sheets, revisions, annotations, schedules, and likely inconsistencies.

3

Humans review the high-value edges

Designers keep control, but instead of hunting through the pack they review a structured list of issues, gaps, and proposed edits.

4

Construction paperwork is drafted automatically

The system prepares RFIs, issue logs, transmittals, compliance notes, schedules, and site handoff docs off the same source material.

5

Every revision stays traceable

Approvals, comments, new uploads, and doc versions stay tied to the job so the rollout team has one view of truth.

What comes out the other side

Fourteen outputs from the same plan set

Marked-up discrepancy reportSheet-by-sheet review notes with flagged mismatches and probable fixes.
RFI draftsClear, reusable requests for missing info or unresolved drawing conflicts.
Permit pack cover sheetJob summary, revision metadata, document bundle status, and submission notes.
TransmittalsIssue-ready document summaries for clients, certifiers, or contractors.
Variation notesDraft explanations when scope shifts create build or commercial impact.
Schedule cross-checksCompare room finishes, fixture schedules, and drawing references for consistency.
Site rollout boardJob-level tracker for every location, status, blocker, and approval.
Construction brief summariesOne-page client-ready summaries pulled from the deeper documentation set.

This is exactly the kind of ugly operational work agents are great at. Humans should edit, approve, and push taste into the system, not build every document from scratch.

Why this matters

“The real win is not a chatbot bolted onto drawings. The real win is a system that remembers the job, understands the document set, and drafts the paperwork around it before your team asks.”

That is the Hyro/OpenClaw angle here. Memory first. Workflow second. Interface third. Once the job context is structured, agents can stop being gimmicks and start behaving like useful junior operators.
To flesh this out fast

What we’d need from G Store

  • 2 to 3 real drawing packs
    PDF or DWG exports, ideally across two revisions so we can show plan comparison and issue detection.
  • Their actual paperwork stack
    Examples of RFIs, issue logs, transmittals, permit cover sheets, schedules, or construction summaries they already produce.
  • Internal standards and review logic
    Naming conventions, legends, compliance checklists, scope rules, and what “good enough” actually means internally.
  • Where the pain is today
    Is the real bottleneck revision checking, paperwork generation, approval routing, consultant coordination, or handoff to site teams?
  • One pilot KPI
    For example: reduce first-pass paperwork time by 60%, catch revision mismatches before issue, or generate complete site packs in under 15 minutes.