We build private, auditable workflows that read drawings, specifications, addenda, RFIs, submittals, schedules, and code references, then prepare structured work for architect or project-manager review.
Teams lose hours normalizing plan sets, specs, addenda, sketches, PDFs, spreadsheets, RFIs, submittals, and email attachments before review work can begin.
Back-and-forth piles up when missing sheets, stale revisions, conflicting notes, incomplete submittals, or discipline mismatches are discovered deep into review.
Architecture teams cannot treat public AI tools as a dumping ground for client plans, budgets, site details, design intent, owner records, or proprietary project knowledge.
Document automation research highlights the same hard problems architecture teams face: multi-page files, dense tables, inconsistent layouts, scanned pages, signatures, stamps, and mixed document formats.
Plan-review workflows often need reasoning across disciplines, drawings, specifications, and code references. That only works when extracted facts can be traced back to the original source.
Architecture workflows need outputs tied to exact sheets, sections, specs, notes, and revision history so project teams can audit the result instead of trusting a black box.
Extracts sheets, notes, dimensions, schedules, details, and revision data so reviewers can find issues faster with source evidence.
Turns specifications, finish schedules, door schedules, room data, and equipment lists into structured fields for QA and coordination.
Checks submittals against spec sections, required attachments, product data, drawings, samples, and reviewer routing before work stalls.
Classifies RFIs, change requests, field notes, and email threads, then routes them with extracted facts, linked files, and open questions.
Compares project facts against code references, accessibility requirements, zoning notes, and firm standards for architect review.
Project teams manually sort plan sets, specs, submittals, RFIs, revisions, and email attachments before architects can assess coordination, compliance, and next actions.
The workflow extracts verified project facts, flags missing or conflicting information, links source evidence, and packages work for architect, PM, or principal review.
Private workflows can use hosted private-cloud inference, dedicated cloud or VPC deployment, or local/on-prem inference when client, site, or project data cannot leave your environment.
Avoid billing surprises with clear workflow-based packages. We do not penalize you for using more AI like other vendors.
Every result can include source sheets, spec sections, extracted fields, reviewer approval, risk notes, exception status, and write-back history.
We choose one plan review, submittal, RFI, spec extraction, or code pre-check workflow with clear cycle-time and quality impact.
We configure extraction, source evidence, reviewer queues, approval gates, and project-data boundaries.
You see cycle time, missing-info reduction, reviewer quality, exception rates, and the next architecture workflow roadmap.
Founder, Performance AI Lab | Former Meta SuperIntelligence Lab
With 25 years in software engineering and enterprise infrastructure, I've built systems for some of the world's most demanding environments. I founded Performance AI Lab to bring private, auditable AI workflows that reduce manual work and preserve operating know-how to mid-market operators.
If your team is buried in documents, emails, approvals, spreadsheets, or software handoffs, we can help identify the first workflow worth automating.
Private AI workflows for the work that falls between your documents, apps, approvals, and operating knowledge.