MEP and AEC firms don't have a technology problem. They have a documentation problem. RFIs, submittals, daily reports, version control. AI handles the groundwork on all of it. Your engineers and field crews handle the judgment calls. That's the division that actually works.
The billing rate on RFI coordination and submittal review is real. So is the schedule impact when responses are slow and documents get lost between systems.
On a large commercial or industrial project, RFI counts regularly reach several hundred. Drafting responses requires pulling specs, cross-referencing drawings, and coordinating with design teams. Most of that groundwork can be done by AI in minutes, not hours.
Manual submittal review is time-consuming and prone to inconsistency, especially across large package batches or when the reviewer changes mid-project. Delays in the review queue compress downstream schedules directly.
When teams work across Procore, email, shared drives, and field apps simultaneously, version control becomes a daily problem. Outdated drawings in the field and missed spec revisions are among the most common and costly sources of rework in construction.
These are the use cases where AI consistently delivers measurable time savings for MEP and AEC teams. Each one is scoped and implemented based on your specific tools and project environment.
AI reads the RFI, pulls the relevant spec sections and drawing callouts, and drafts a response for engineer review. Your team reviews and approves. They stop spending hours building the response from scratch on every ticket.
AI screens incoming submittals against spec requirements and flags exceptions before they reach the responsible engineer. Routine submittals move faster. Complex exceptions get proper attention instead of getting buried in a growing backlog.
AI monitors document repositories for version conflicts, flags superseded drawings still in active use, and routes updated documents to the right people automatically. Version errors in the field become an exception rather than a daily risk.
AI drafts daily field reports, safety inspection logs, and incident documentation from brief voice or text inputs. A foreman who typically spends 20 to 30 minutes writing end-of-day reports can review and sign off an AI draft in a fraction of that time.
Greg Stone built his career in CQV engineering (Commissioning, Qualification, and Validation) inside FDA-regulated pharmaceutical and life sciences manufacturing. It's some of the most documentation-intensive work in industry.
That background translates directly to MEP and AEC. Construction document control, spec compliance, and field reporting have the same core requirements as IQ/OQ/PQ validation: clear scope, documented procedures, and a defensible audit trail. The contexts are different. The discipline is the same.
Every AI workflow QP builds for a construction or engineering firm comes with a full Standard Operating Procedure, documented validation testing, and a handoff package your team can actually use. Not just a demo that works once and breaks six months later.
Discovery & Scoping
We review your project workflows, document environment, and team structure to identify the highest-ROI starting point.
Build & Validate
We build the workflow against a real sample of your project data and validate performance before anything goes to production.
Deploy & Train
Full handoff with SOPs, team training, and 30-day post-launch support. Your team owns it when we leave.
Measure & Optimize
Success criteria are defined before we start. Every engagement tracks against them. If the numbers aren't there, we fix it.
These figures come from AEC industry research. They're baselines for what firms are spending before any automation is in place, not projections of what AI will save.
of construction worker time spent on non-value-adding activities, including documentation, coordination overhead, and rework
PlanGrid / FMI, 2018
in annual rework costs across US construction, with poor project data and miscommunication as the primary drivers
PlanGrid / FMI, 2018
of all construction rework traces back to poor project data and document miscommunication, not field execution errors
PlanGrid / FMI, 2018
The ROI on AI-assisted documentation comes from recovering a fraction of this time. A project team saving 5 hours per week on RFI and submittal coordination, at a fully loaded rate of $125/hr, covers a typical single-workflow engagement cost within one project cycle.
QP works with small and mid-size MEP and AEC firms: large enough to have real documentation overhead, lean enough that adding headcount isn't the answer.
Yes. Procore is the most common project management environment we see in MEP and AEC firms, and the RFI, submittal, and daily reporting workflows we build are designed to integrate with it directly. We use Procore's API to pull and push data with no double entry and no separate login. If your team is on Procore already, that's a solid starting point.
Bluebeam is where a lot of submittal review and markup work happens in the field. We build AI workflows that run alongside Bluebeam, reading the PDF annotation layer to pull markup comments and cross-reference them against your spec requirements. Your team keeps working in Bluebeam the same way they do today. The time spent manually compiling review notes and chasing spec references goes away.
Yes. Autodesk Construction Cloud has a well-documented API that connects AI workflows directly. Document version monitoring, drawing set management, and RFI workflows all integrate cleanly. If you're still on BIM 360, that works too since Autodesk's underlying platform API covers both environments. We start with whatever your team is actually using.
An AI Readiness Assessment takes 2–3 weeks and gives you a prioritized map of where to start. A focused workflow implementation, like RFI drafting or submittal screening, typically runs 4–8 weeks from scoping to deployment. If someone is quoting you 6 months to automate RFI responses, ask why.
Strict document control is exactly where structured AI performs well. The key is validation: building the workflow against a real sample of your project data, defining the rules it follows, and testing it before any production use. Greg's CQV background means he approaches this the same way a validation engineer would. Define the requirements, test against them, document the results. If it doesn't pass validation, it doesn't ship.
Scope determines cost: the number of workflows, your existing tool environment, and how deep the integration needs to go. Advisory work is available with no long-term commitment for firms that want to start smaller. The best way to get a realistic number is a 30-minute discovery call where we scope your situation directly.