// industries · aec field operations
Reporting that leaves the spreadsheet behind.
AEC projects generate field data all day — equipment hours, quantities, inspections — and most of it dies in spreadsheets. We build the pipelines that move it from the field to the office, and make it ready for AI.
> ask_ai("which projects are behind on daily reports?")
x ERROR: source of record = site_super.xlsx (last sync: unknown)
> deploy field_reporting_pipeline
+ field capture → structured records
+ daily reports generated, not transcribed
+ project status queryable by AI
// the operating reality
Field data dies on the way to the office.
The superintendent knows exactly what happened on site today. By the time that knowledge survives a notebook, a spreadsheet, a PDF export, and an email thread, it's three days old and nobody can query it. Project controls end up steering by documents instead of data.
Field data is captured automatically at the source — not entered once, not transcribed three times.
The AI-Ready Data Audit turns a preliminary blueprint into a validated engineering plan, for $5,000.
The same onshore team with subcontractor experience on US Navy and US Air Force programs.
// what we build
From the field to the office
Equipment state, runtime, and downtime captured automatically at the source — not reconstructed from a notebook at the end of the shift. Daily reports and inspection logs delivered where the office actually reads them. No evening transcription shift, no operator stuck filling out a form.
Project history locked in spreadsheets, Access databases, and aging project-management systems — extracted, structured, and made queryable without disrupting the jobs that depend on them.
Governed, read-only access layers so AI can answer real questions about schedules, quantities, and crews — without touching the source systems or leaking project data.
// proven on the ground
We've already built this for a foundation contractor.
We built the custom ERP behind a large commercial foundation contractor's operations — then extended it with sensor-driven field capture aimed at the problem every heavy civil operator knows: rig downtime and shift reports that don't survive the trip from the field to the office.
We don't learn the field from a brochure. We build operational data infrastructure for regulated, hard-environment industries — aviation MRO, defense, industrial manufacturing — where the data has to be accurate, traceable, and yours. Heavy civil is the same problem on a different jobsite.
// what you could ask
Questions your project data should answer
// yours to keep
On-prem. Air-gap-capable. Yours.
Field capture, equipment telemetry, and project record stay in your environment. On-prem and air-gap-capable. No mandatory cloud, no offshore handling — which matters when the work is publicly funded or government-adjacent.
> deploy field_capture --target on_prem
+ equipment telemetry → your network
+ project record → your database
+ air-gap mode: supported
> locate mandatory_cloud_dependency
x not found
// built for this industry
Alice writes the report so the field doesn't have to.
Alice is our construction and civil reporting bot: punchier reporting, delivered automatically from what the field already captures. If your daily reports are a nightly chore, start there.
Punchier construction reporting, delivered by a bot.
alice.dev →// faq
AEC field ops — straight answers
Can this run on-prem or air-gapped for publicly funded work?
Yes. Field capture, equipment telemetry, and the project record stay in your environment — on-prem and air-gap-capable, with no mandatory cloud and no offshore handling. That matters when the work is publicly funded or government-adjacent.
Our project history is stuck in spreadsheets and Access databases. Can you extract it?
Yes. We extract and structure project history locked in spreadsheets, Access databases, and aging project-management systems, and make it queryable — without disrupting the jobs that depend on them.
What is Alice?
Alice is our construction and civil reporting bot: punchier daily reports delivered automatically from what the field already captures, so reporting is a byproduct of the work, not a second job.
What does it cost to start?
The preliminary AI Data Access Blueprint is free. The AI-Ready Data Audit is a five-day, $5,000 engagement, credited back toward the build if you hire The Mad Botter.
// where to start
Map the access problem before buying another AI tool.
The AI Data Access Blueprint is a free preliminary map of where an AI initiative will stall in your operation: data access, legacy systems, governance, or ownership. The paid audit then validates it against your real systems.
We map the fix in 5 days for $5,000 — credited toward the build if you hire us.