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Transparency

How Accurate Is Trueleveler?

We believe in radical transparency. Here is how our AI works, what it catches, what it misses, and what you can do to get the best results from every analysis.

Methodology

Dual-model AI with cross-validation.

Trueleveler uses a dual-model architecture — Claude and Gemini run independently on every document, then cross-validate results. This catches errors that any single model would miss and produces more reliable findings.

Step 01

Document Parsing

Your documents are parsed into structured data. Tables, line items, clauses, and terms are extracted and normalized for analysis regardless of source format.

Step 02

Independent Analysis

Two AI models (Claude by Anthropic and Gemini by Google) analyze each document independently. Neither model sees the other's output during this stage.

Step 03

Cross-Validation

Results are compared and reconciled. Where both models agree, confidence is highest. Where they disagree, the system flags the discrepancy for closer review.

Step 04

Structured Output

Final results are presented with confidence indicators, source references, and clear explanations so you can quickly verify findings against the original documents.

Accuracy by Engine

What each engine catches.

Accuracy depends on document quality, format, and complexity. All engines deliver consistently high accuracy on well-formatted documents. Below is what each engine is designed to identify.

Engine What It Catches Accuracy Factor
Bid Leveling Line-item discrepancies, scope gaps, missing items, unit price outliers, math errors, and bid-to-bid inconsistencies across multiple proposals Highest on structured bid tables with clear line items
Contract Review Risky clauses, indemnification gaps, payment term issues, insurance requirements, change order provisions, and termination conditions Best on standard contract formats (AIA, ConsensusDocs, NEC, JCT)
Submittal Extractor Submittal requirements from specifications, section references, responsible parties, due dates, and approval workflows Strongest on CSI-formatted specifications
Scope Check Bid-to-spec misalignments, missing scope items, qualification conflicts, and exclusion gaps when comparing a bid against project requirements Most effective with clear scope of work definitions
RFQ Generator Generates comprehensive RFQs from project specs with appropriate scope, terms, and evaluation criteria for each trade package Quality improves with detailed project specifications
Change Order Review Pricing reasonableness, scope justification, markup compliance, schedule impact, and alignment with original contract terms Best with itemized change order breakdowns
Pay App Review Overbilling, schedule of values discrepancies, retainage errors, percent-complete mismatches, and stored materials issues Most accurate with standard AIA G702/G703 formats
Document Compare Clause changes, added/removed terms, modified pricing, scope alterations, and hidden revisions between document versions Best comparing documents of the same type and format
Known Limitations

What the AI cannot do.

No AI system is perfect. Here are the areas where Trueleveler has known limitations and where human review remains essential.

Handwritten Notes

Handwritten annotations, margin notes, and hand-drawn markups are not reliably extracted. If critical terms exist only in handwritten form, they may be missed.

Severely Damaged Scans

Documents that are heavily skewed, extremely low resolution, or have significant portions obscured will produce incomplete results. Clean re-scans dramatically improve output.

Highly Unusual Formats

Proprietary or non-standard contract formats, bespoke bid structures, and highly customized templates may reduce accuracy. Standard industry formats yield the best results.

Implied Context

The AI analyzes what is written in the document. Industry-specific verbal agreements, local customs, or context that exists outside the document cannot be considered.

Local Code Compliance

While the AI understands general construction standards, it does not verify compliance with specific local building codes, municipal regulations, or jurisdiction-specific requirements.

Multi-Language Documents

Documents mixing multiple languages in the same page may reduce extraction quality. Single-language documents, particularly in English, produce the most reliable results.

Quality Factors

What affects accuracy.

You can significantly improve your results by understanding what factors affect AI analysis quality.

Document Format

Native PDFs (digitally created) produce far better results than scanned documents. If you have the option, always upload the digital original rather than a scanned copy.

Scan Quality

When scans are necessary, use 300+ DPI, ensure pages are straight, and avoid dark edges or fold marks. Color scans outperform black-and-white for documents with highlighted sections.

Standard Formats

Industry-standard templates (AIA, ConsensusDocs, CSI-formatted specs, standard bid forms) produce the highest accuracy. The AI recognizes these patterns immediately.

Language

English-language documents produce the strongest results. All major European languages are supported with consistently high accuracy, but English remains the benchmark.

Document Completeness

Complete documents with all pages included yield better analysis than partial uploads. Missing pages, especially scope descriptions or pricing schedules, will create gaps in findings.

Table Structure

Clear, well-structured tables with consistent column headers and row formatting are parsed with high fidelity. Merged cells, nested tables, and irregular layouts can reduce extraction accuracy.

Continuous Improvement

Getting better with every analysis.

Trueleveler improves continuously through multiple feedback channels. Your usage helps make the platform more accurate for everyone — without ever storing or training on your documents.

01

Prompt Engineering

Our engineering team continuously refines the prompts and instructions that guide each AI model, improving how they parse, interpret, and cross-validate construction documents.

02

Edge Case Library

When users report unexpected results, we add those document patterns to our internal test suite. This prevents regressions and ensures known edge cases are handled correctly.

03

Model Updates

As Claude and Gemini release improved model versions, we evaluate and integrate upgrades that improve construction document understanding while maintaining result consistency.

04

User Feedback Loop

Every analysis includes feedback options. When you flag an inaccuracy or confirm a finding, that signal feeds into our quality pipeline to prioritize the most impactful improvements.

Important Notice

Always verify against source documents.

AI Analytical Aid — Not a Replacement for Professional Judgment

Trueleveler is an AI-powered analytical aid designed to augment — not replace — the expertise of construction professionals. All AI-generated findings should be verified against the original source documents before making project decisions. No AI system can guarantee 100% accuracy, and results should be treated as a highly capable first pass that accelerates your review process, not as a final determination. For contractual, legal, or financial decisions, always consult with qualified professionals.

See the accuracy for yourself.

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