Services/AI Agents for the Jobsite
Custom AI Agents for Construction

AI AgentsFor the Jobsite

Talk to your P6 schedule in plain English. Automate the document control work that burns engineer-hours. Turn 20 years of project archives into a queryable AI. No generic chatbots — agents built on your data, your tools, your workflows.

What Sets Us Apart

Built by people with 20 years on the jobsite
Trained on YOUR project data — not generic web
Native integration with P6, Procore, Aconex, BIM 360
Working agent in 2 weeks — on your real data
Complete source code ownership — no lock-in
MLOps & monitoring included in production builds
60–80%
Reduction in doc control manhours
2 weeks
Average time to working prototype
3–5×
ROI within first year
100%
Source code ownership for clients

Six Construction AI Agents We Build

Each agent is scoped, designed, and built specifically for your operation — using the right AI approach for the right construction workflow.

Flagship

Primavera P6 Natural-Language Interface

Talk to your P6 schedule in plain English. Anyone — superintendents, owners, executives, project engineers — can ask the schedule questions without ever opening Primavera or knowing what a logic tie is. Our flagship demo.

Example use cases

"What activities slipped in the last two weeks and why?"
"Generate a 3-week look-ahead for area 200"
"Which crews are over-allocated next month?"
"What's the critical path through commissioning?"

Document Control Automation

The highest-volume manhour drain on every project. We automate RFI logging, submittal routing, transmittal generation, drawing-log maintenance, and distribution matrices — eliminating the repetitive paperwork that burns engineer- and admin-hours.

Example use cases

Auto-log incoming RFIs and route to responsible disciplines
Submittal status tracking with auto-reminders
Drawing register updates from Bluebeam / ACC
Transmittal generation with smart distribution lists

Project Knowledge AI (RAG)

Your senior PMs are retiring. Their lessons learned, standard details, spec interpretations, and "how we handled this last time" are in PDFs, emails, and project archives. We turn 20 years of project history into a queryable AI your new PMs can ask.

Example use cases

"How did we handle prevailing-wage compliance on the Phase 2 job?"
"Show me change orders for HVAC scope creep across past projects"
"What standard details do we use for tilt-up panel connections?"
Auto-surfaced lessons learned during new-project setup

Field Operations AI

Automate the field-to-office paper trail. AI that ingests daily reports, photo logs, and inspection records — and turns them into structured data your PMs, schedulers, and superintendents can actually use.

Example use cases

Daily report parsing and NCR auto-flagging
Photo log tagging and search ("show me all rebar inspections")
Voice-to-daily-report from superintendents in the field
Equipment hours and crew time reconciliation

Engineering & Drawing Review AI

AI that pre-reviews submittals, checks drawing markups against specs, and flags coordination conflicts before they reach the engineer. Cuts review cycle time by half and catches the issues that fall through the cracks.

Example use cases

Submittal completeness checks against spec sections
Drawing rev comparison and change summarization
BIM clash review prep with prioritized issue lists
Spec compliance Q&A on the fly

Safety, Quality & QS Agents

Function-specific agents trained on your safety program, quality standards, and cost structure. Not generic chatbots — agents that know your project, your specs, and your contract.

Example use cases

Toolbox-talk generation from incident trends
NCR root-cause analysis and corrective-action drafting
Punch-list auto-categorization and routing
Takeoff QA, change-order pricing, and cost-engineering support

Our AI Technology Stack

We select the right tool for the job — not the one we're most comfortable with.

OpenAI GPT-4oClaude / AnthropicLangChainLangGraphLlamaIndexHugging FacePythonFastAPIn8nVapiAzure AI ServicesAzure OpenAIPinecone / pgvectorMLflowDocker / KubernetesSupabasePostgreSQL

From Idea to Production in Weeks

Our structured delivery model gives you something working at every checkpoint — no black-box development cycles.

Week 1–2

Discovery & Workflow Map

Walk the workflow you want automated
Data & tool inventory (P6, Procore, Aconex, archive)
AI feasibility assessment
Solution design document
Week 3–5

Prototype on Your Data

Working agent on your real project data
Stakeholder demo & feedback
Accuracy / quality benchmarking
Integration architecture finalized
Week 6–9

Production Build

Full feature development
Integration to live project tools
Project-level access controls
Monitoring & observability setup
Week 10+

Launch & Optimize

Staged rollout per project / division
PM and field team training
Performance tuning
Ongoing model improvement

Our AI Development Principles

Your Project Data Stays Yours

We never use your project archives, schedules, or proprietary data to train models for anyone else. Client data isolation is non-negotiable.

Explainability First

AI decisions affecting your project schedule, costs, or safety need to be understandable. We build in logging, reasoning traces, and override mechanisms from the start.

Production-Ready Always

A demo that can't survive contact with a real project isn't a solution. Every build includes monitoring, error handling, and a roadmap for scale before we ship.

Which Workflow Would You Automate First?

Start with a free 30-minute discovery call. We'll identify 2–3 high-ROI AI opportunities specific to your projects — no commitment required.