Practical AI Enablement for Construction Commissioning and Field Quality

Executive Summary

Prasino Engineering is using applied AI to tighten execution in the field, reduce rework, accelerate issue identification, and standardize deliverables across commissioning and construction quality workflows. The strategy is not experimental. It is operational. Prasino Engineering is integrating three pillars into a single field to report pipeline:

• Site Inspector as the structured capture and issue intelligence layer

• Wearable vision systems such as META glasses as the hands-free evidence collection layer

• Large language models (LLMs) as the reasoning, summarization, and deliverable automation layer

Together, these tools are turning field observations into consistent, defensible, client ready outputs with materially less cycle time and fewer gaps. The operating model is simple: capture faster, classify more consistently, document more completely, and close issues earlier.

Audience and Use Case Fit

This white paper is intended for owners, facility teams, construction managers, architects, engineers, and commissioning authorities who are evaluating AI enabled workflows and want to understand practical adoption, governance, and measurable business outcomes.

Typical project environments include:

• K 12, municipal, higher education, and commercial facilities

• New construction commissioning, existing building commissioning, and targeted investigations

• Multi stakeholder environments where documentation quality, traceability, and speed matter

Current State Challenges in Construction and Commissioning

Most construction field documentation still suffers from recurring failure modes:

• Most construction field documentation still suffers from recurring failure modes:

• Inconsistent issue taxonomy: different engineers describe the same problem differently, complicating tracking and trending

• Incomplete evidence packages: photos, notes, and context are not consistently captured at the moment of discovery

• Administrative overload: senior technical staff spend excessive time on formatting, narrative writing, and repetitive reporting

• Closeout friction: teams struggle to prove resolution due to missing baseline evidence and unclear acceptance criteria

These conditions increase delivery risk, slow down closeout, and create disputes that could have been avoided with better capture and clearer documentation

Prasino Engineering AI Enablement Strategy

Prasino Engineering is implementing AI as an integrated workflow, not as standalone tools. The objective is to strengthen four operating outcomes:

• Speed to insight

• Consistency of technical judgment

• Traceability and defensibility of documentation

• Scalability of delivery without proportional headcount growth

AI is positioned as a delivery multiplier. Human expertise remains the control point for engineering judgment, acceptance, and client commitments.

Solution Architecture Overview

Site Inspector as the Structured Capture and Intelligence Layer

Site Inspector functions as the field level system of record for observed conditions. It standardizes how observations are captured and converted into actionable issues. Key capabilities include:

• Photo first capture aligned to construction trade and system categories

• Structured issue statements, recommended actions, and prioritization logic

• Repeatable outputs aligned to field checklists and commissioning workflows

• Consistent formatting for tracking, closeout, and reporting

Operationally, Site Inspector reduces variability between engineers and enables predictable deliverable quality.

META Glasses as Hands Free Evidence Collection

Wearable vision systems such as META glasses provide high leverage in active field environments, especially where hands are occupied, PPE is required, ladders are used, or equipment access is constrained.

Benefits include:

• Rapid capture of point of view evidence without stopping work

• Improved documentation of context, sequence, and adjacency conditions

• Higher volume of usable evidence with less effort

• Reduced missed observations caused by switching between tools

This addresses a core field reality: the best time to capture evidence is at the moment of discovery, not after the fact.

LLMs as the Reasoning and Deliverable Automation Layer

LLMs are used as an operations enablement layer, not as an engineering authority. They provide:

• Drafting of issue narratives based on evidence and structured inputs

• Summarization of site walks into executive ready updates

• Conversion of field notes into consistent report sections

• Standardization of language, formatting, and completeness checks

• Rapid generation of client ready deliverables aligned to Prasino reporting templates

In practice, LLMs reduce administrative time and increase documentation consistency. The engineering team remains responsible for technical validation.

End to End Workflow

Step One: Capture

• Engineer captures images and short context statements using META glasses and mobile capture

• Evidence is tagged to location, system, and condition type where feasible

Step Two: Normalize and Classify

• Site Inspector ingests evidence and applies a consistent taxonomy

• Issues are grouped by trade, system, severity, and schedule risk

Step Three: Analyze and Draft

• LLM drafts an issue description, recommended action, and acceptance criteria aligned to Prasino conventions

• Where applicable, it produces closeout-oriented language that clarifies what constitutes resolution

Step Four: Human Validation

• Engineer reviews and edits for technical accuracy, feasibility, and client alignment

• Final issue severity and recommendations are confirmed by Prasino

Step Five: Output and Delivery

• Outputs are compiled into Prasino standard deliverables such as site visit reports, issue logs, and executive summaries

• Evidence is packaged for defensibility and ease of decision making

Step Six: Closeout and Learning Loop

• Closeout evidence is captured and compared against baseline

• Recurring issues are trended to drive upstream prevention in future projects

Value Delivered to Clients

Reduced Rework and Earlier Issue Containment

When issues are identified earlier and documented clearly, downstream rework declines. Clear evidence packages shorten debate cycles and accelerate corrective action.

Faster Reporting Cycles

The time from site walk to client ready report is reduced, enabling quicker decision making and fewer stalled work fronts.

Consistent Quality and Professionalism

Standardized issue language improves clarity across stakeholders and improves trust in the commissioning process.

Improved Traceability

Evidence, context, and acceptance criteria are packaged together, supporting dispute avoidance and reliable closeout.

Better Resource Utilization

Senior engineers spend more time on high value technical review and less time on repetitive narrative writing.

Scalability Without Diluting Standards

AI supported workflows reduce dependence on individual writing style and field documentation habits. This supports growth while maintaining consistent delivery.

Reduced Variability Across Staff

Structured capture and LLM assisted drafting compress the skill gap for junior engineers and produces outputs closer to senior level standards.

Knowledge Capture and Reuse

Observations become reusable institutional knowledge: recurring issue patterns, corrective actions, and best practice language.

Enhanced Differentiation

Prasino is positioning itself as a technology forward commissioning partner with measurable operational advantages, not just additional reporting.

Risk Management and Governance

Engineering treats AI as an enablement layer that requires controls. Key governance principles include:

Engineering Judgment Control

AI outputs are drafts. Prasino engineers validate all technical claims, severity assignments, and recommended actions prior to issuance.

Data Handling

Project evidence is handled with client confidentiality in mind. Sensitive images and project data are governed under existing confidentiality practices, with additional attention to where data is stored and processed.

Auditability

Deliverables maintain traceability back to field evidence. This supports defensibility and preserves professional accountability.

Model Limitations

LLMs can produce plausible but incorrect statements. Prasino mitigates this through structured inputs, constrained output templates, and required human review.

Implementation Roadmap

Phase 1: Standardize Capture and Taxonomy

• Align Site Inspector categories to Prasino service lines and client deliverables

• Define required minimum evidence and context for common issue types

Phase 2: Deploy Wearable Capture for Target Use Cases

• Focus on high value scenarios: above ceiling, mechanical rooms, rooftop equipment, envelope details, electrical rooms

• Establish field habits for consistent tagging and capture quality

Phase 3: Integrate LLM Drafting into Reporting

• Implement report section templates aligned to Prasino standards

• Enable automated first drafts for issue narratives, executive summaries, and closeout statements

Phase 4: Closeout and Metrics

• Add closeout validation workflows and trending

• Build simple dashboards for cycle time, issue recurrence, and closeout duration

Success Metrics

Prasino Engineering can track AI enablement impact using operational KPIs:

• Time from site visit to issued report

• Percentage of issues with complete evidence packages

• Average days to close issues by category

• Rework events linked to missed or unclear documentation

• Staff hours spent on documentation versus engineering validation

• Client satisfaction scores tied to clarity and speed of reporting