Use Cases

Pre-Construction Workflows That TeraContext.AI Automates

TeraContext.AI handles the most time-consuming parts of the pre-construction process — from the moment an RFP lands on your desk through final proposal submission. Here’s how estimating teams use it.

Spec Book Decomposition for GC Bid Response

Commercial construction project site

The Problem: A 2,000-page spec book hits your desk with a 3-week bid deadline. Your estimating team needs to read every section, classify each one by trade, and build scope packages for subcontractor bidding. Manually, this takes days — and missed sections become change orders.

How TeraContext.AI Handles It:

  1. Upload the spec book PDF (even 2,000+ pages). Processing starts automatically.
  2. The AI pipeline extracts every spec section and classifies it against masterformat (546 codes) — or any of 10 other WBS taxonomies.
  3. Each classification gets a confidence score. Your team reviews the results: bulk-confirm the 70-80% that are above 90% confidence, manually review edge cases.
  4. Approve the WBS, then auto-generate scope packages by division.
  5. Customize packages — move sections between trades, rename, split, or merge — then export as PDF, Word, CSV, or Markdown.

The Result: Days of manual spec reading and classification compressed into hours. Every section accounted for. Trade-ready scope packages your subs can bid on immediately.

Subcontractor Bid Solicitation & Comparison

The Problem: You’ve got scope packages for 15+ trades. For each trade you need 3-5 sub bids. That’s 50+ bid invitations to manage, dozens of bid responses to review, and the constant risk that a sub’s exclusions create a coverage gap you don’t catch until it’s too late.

How TeraContext.AI Handles It:

  1. Build your subcontractor directory — company, contact, and trade tags across 29 standard construction trades.
  2. Select a scope package, check the subs you want to invite, and click Invite.
  3. When bids come back, record amounts. For detailed analysis, upload the sub’s bid response document.
  4. The AI analyzes each response and reports: Coverage % (how much scope they addressed), Exclusions (what they left out), Alternates (different materials/methods proposed), Qualifications (conditions on their bid), and Risk Flags (potential issues).
  5. Use the Compare tab to see all bids for a package side-by-side — lowest, highest, average, and spread.

The Result: No more manually reading every sub’s bid letter to find the buried exclusion on page 3. AI catches the gaps. You compare apples to apples.

GC Proposal Assembly

The Problem: You’ve accepted your best sub bids across all trades. Now you need to assemble a complete GC proposal response — showing the owner that every spec section is covered, no gaps exist, and the total price is defensible. Manually pulling this together takes days.

How TeraContext.AI Handles It:

  1. Click Generate Proposal. The platform runs a 6-stage process:
    • Coverage Matrix — maps which subs cover which divisions
    • Sub Summaries — aggregates each sub’s bid analysis
    • Gap Analysis — identifies spec sections not covered by any sub, plus overlaps
    • Section Responses — generates per-trade narrative responses
    • Executive Summary — AI writes the proposal summary
    • Document Generation — compiles everything into exportable format
  2. Review five tabs: Summary, Coverage Matrix, Trade Responses, Gap Analysis, and Compliance.
  3. Export as PDF or Word. Edit offline, then re-upload for Compliance Check against the original RFP and Version Diff to track your changes.

The Result: A professional, comprehensive GC proposal with gap analysis and compliance checking — assembled in hours instead of days.

Drawing Set Analysis for Estimating

The Problem: A 243-sheet drawing set across 9 disciplines. Your estimators need to understand the scope, find the general notes, cross-reference to specs, and identify requirements scattered across dozens of sheets. Manually reviewing every page is slow and error-prone.

How TeraContext.AI Handles It:

  1. Upload drawing sets as Drawing type. The vision LLM analyzes every page automatically.
  2. The Drawings tab shows a complete sheet index with discipline breakdowns — how many Architectural, Structural, Mechanical, Electrical, Plumbing, Civil, Landscape, Fire Protection, and General sheets.
  3. Click View on any sheet to see the actual drawing page rendered at 150 DPI.
  4. Extracted text — general notes, keynotes, material specs, schedules, legends, detail callouts — becomes searchable through the Query tab.
  5. Cross-references between drawings and spec sections are mapped automatically (both explicit references like “see Drawing A-101” and discipline-based links).

The Result: Full visibility into a drawing set’s scope and content without manually reviewing every sheet. General notes and cross-references are searchable alongside specs.

Multi-Taxonomy Classification for Specialty Work

The Problem: Your firm bids on DoD projects requiring UFGS classification, DOE facilities with their own cost account structure, or power generation projects using FERC or EPRI taxonomies. Standard masterformat doesn’t fit, and manually mapping specs to an unfamiliar taxonomy is slow and error-prone.

How TeraContext.AI Handles It:

  1. When creating a project, select the appropriate taxonomy: UFGS (DoD), UniFormat II (cost estimating), DOE Nuclear, DOE Buildings, FERC Generation, EPRI GN-COA, OmniClass, Uniclass 2015, or NAHB Residential.
  2. The AI classifies spec sections against your selected taxonomy, using the same confidence scoring and human review workflow.
  3. If none of the 10 built-in standards fit exactly, clone any standard taxonomy and customize it — add, rename, delete, or reorganize codes to match your firm’s internal classification system.

The Result: One platform handles all your classification needs — commercial, government, power, and residential — without maintaining separate workflows for each.

Natural Language Spec Query

The Problem: During estimation, your team has dozens of quick questions: “What are the concrete PSI requirements for footings?” “What fire-rated assemblies are required?” “What type of insulation is specified?” Each question means searching through hundreds of pages.

How TeraContext.AI Handles It: The Query tab lets you ask natural language questions against your uploaded specifications and drawings. Choose Generic mode (~13 seconds, good for quick lookups) or Focused mode (~36 seconds, for detailed requirements extraction). Answers come with source citations — spec section codes, drawing numbers, and page references.

The Result: Instant answers with citations instead of manual page-flipping. Your estimators can verify requirements in seconds, not minutes.

Is TeraContext.AI Right For Your Team?

Good fit:

May not be the right fit:

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