Follow each major step as CAM works through the review.
📧 Get notified when done (optional)
Analysis takes a few minutes. Enter your email and we'll send you this same return link when it's ready.
Email addresses don't match
You can wait here and watch progress update, or safely close this page and come back using the link below.
Ready now
Leases ready to review
Open completed contracts immediately while CAM continues processing the rest.
Coverage check
Review Areas
CAM is checking all 32 standard issue areas.
Stage-by-stage progress
Contract status by stage
Ask while you wait
AI-generated guidance — not legal advice.
🔍
What CAM is doing right now
CAM is parsing the documents, aligning them to the review framework, checking the selected issues, and running multiple reviewers in parallel. It is looking for meaningful changes, not just word differences, so the output is review-ready instead of noisy.
📋
What you'll get in results
You will get more than a list of flags: structured findings, side-by-side comparison, an audit trail for why conclusions were made, and working outputs you can review immediately.
🧠
Why CAM exists
Most AI review tools are designed to give an answer even when the evidence is shaky. CAM starts from a stricter question: has this conclusion earned enough support to be stated confidently?
⚖️
What CAM does differently
CAM uses multiple independent AI reviewers on the same clause, keeps disagreement visible, and can qualify or withhold a conclusion when confidence is not strong enough. That helps prevent false certainty.
🎯
Applied here: document review
In this workflow, CAM compares documents against a reference standard and highlights the changes most likely to matter in downstream review, negotiation, or decision-making.
🔎
Why the audit trail matters
If a finding looks wrong or surprising, you can trace it. The audit trail shows the reference text, tenant text, what changed, how reviewers disagreed, and how CAM reached the final call.
🎚️
What the scores are telling you
CAM scores are meant to show how stable a conclusion is, not just how strongly a model stated it. Higher disagreement or a larger sensitivity gap means the finding deserves a closer human look.
💬
Ask questions while CAM works
Use the chat panel while processing runs to ask what CAM is checking, what the results screens will show, or what to review first once a contract is ready.
💻
View Results on Desktop
The full analysis view is optimized for desktop browsers.
Or email yourself the link:
Overview
Open Issues
Generating portfolio summary…
Critical — Immediate action required High — Significant deviation, review recommended Medium — Moderate deviation, monitor Low — Minor deviation, informational Conforms — Aligns with reference lease
Primary Order:
Lease Summary
Deviations (Review Recommended)
Additional Provisions
Provisions identified outside the evaluated scope with no standard provision match.
Export audit trail:
Ask About This Analysis
Scope
Models:
Response:
Synthesized by:
AI-generated analysis — not legal advice.
How CAM™ Intelligence Analyzes Your Lease
How CAM analyzes leases — two modes
CAM stands for Constrained Assertion Method, Vered.ai’s framework for evidence-aware AI analysis. In lease text, Common Area Maintenance (CAM) is a separate lease issue area.
CAM operates in two analysis modes. The behaviors described on this page apply differently depending on which mode produced your report:
Coverage Analysis (single document) — When you upload a single lease without a reference template, CAM runs Coverage Analysis. This mode uses CAM’s schema-governed assertion architecture to evaluate the lease against a curated library of expected provisions. Each issue area is classified by materiality (covered, worth reviewing, needs attention) and surfaced with exposure prose framed for the perspective you selected (tenant, landlord, or neutral). Coverage Analysis surfaces what’s missing, what’s incomplete, and what’s structurally asymmetric — without requiring a reference document to compare against.
Comparison Analysis (lease vs template) — When you upload a tenant lease alongside a reference template, CAM runs Comparison Analysis. This mode uses CAM’s full multi-evaluator pipeline: independent evaluator roles, challenger review, severity assessment, and the disagreement governance described below. The confidence indicators, Finding Reliability scores, and Assertion Sensitivity Gap all apply to Comparison Analysis findings.
Contract Interaction Review — After Coverage Analysis completes, CAM runs a third pass that reads the entire lease as a whole rather than provision by provision. This pass looks for three things: whether a missing provision’s substance is supplied by another clause elsewhere in the document; whether cure, remedy, or rights language is running in favor of the correct party; and whether two or more provisions combine to create exposure that neither reveals on its own. This layer operates strictly within the four corners of the lease — it does not credit common law, background doctrine, or implied terms. Findings appear in the Contract Interaction Review tab and are flagged on individual provision cards where they apply.
The remainder of this page describes Comparison Analysis behavior in detail. Coverage Analysis surfaces issues based on schema-driven classification with materiality grading rather than multi-evaluator agreement; the confidence-score machinery described below does not apply to Coverage findings.
👀
Most AI tools hide disagreement. CAM surfaces it.
When AI evaluators disagree about a clause, conventional tools quietly resolve that disagreement and deliver one confident-sounding answer. You never see the uncertainty. CAM works differently: each provision is evaluated independently by multiple AI roles, without any of them seeing the others’ conclusions. Where they agree, the finding is reliable. Where they don’t, that disagreement is shown to you — because a split evaluation is itself important information.
📊
Most AI tools ask the model how confident it is. CAM doesn’t — because that answer isn’t reliable.
AI models routinely express high confidence in wrong answers. A model’s self-reported certainty is not a meaningful signal — it reflects how the model “feels” about its answer, not whether the answer is actually well-supported. CAM doesn’t ask evaluators to rate their own certainty. Instead it measures things that are independently observable: did all evaluators reach the same conclusion without seeing each other’s work? Is the reasoning grounded in explicit clause text, or inferred? Did the finding survive a dedicated challenge step? Does the clause depend on how other parts of the lease are read? The scores you see in CAM reflect those factors — agreement, evidence quality, reasoning completeness, and structural complexity — not a self-assessment.
⚖
CAM knows when not to assert.
Most AI systems are built to always give you an answer. CAM is built around a different question: “Is there enough evidence to assert this conclusion reliably?” When the answer is no — because evaluators disagreed, or the clause is too context-dependent to characterize definitively — CAM withholds the finding rather than guessing. A withheld finding is not a failure. It is the system being honest about the limits of what it can confidently conclude.
How the pipeline works:
Unlike single-AI tools, CAM uses a multi-stage evaluation pipeline designed for accuracy and transparency:
Multiple AI evaluation roles independently analyze each provision without seeing each other’s conclusions — producing genuinely independent assessments, not a consensus.
An automated rules engine checks for specific deviation patterns: exception clauses, definition changes, numerical differences, obligation shifts, and more.
A challenger role probes flagged findings to test whether the deviation is substantive or cosmetic, and to surface hidden dependencies.
Severity is assessed based on legal and financial impact, so you know what to address first.
Each finding shows you exactly what each evaluator concluded, which rules triggered, and how the final verdict was reached. Full transparency, not a black box.
Confidence indicators
After evaluating each clause, CAM assigns a confidence label. These appear in the sidebar and on every finding card:
●●●● Verified — All reviewers agreed. The finding is reliable and well-grounded in the clause text.
●●●○ Impact Unclear — Finding is confirmed, but the practical impact depends on how specific terms or cross-references are interpreted. Read the interpretation note.
●○○○ Inconclusive — Insufficient basis to assert a finding. Treat as a flag, not a confirmed deviation.
How the confidence scores work
Each provision receives three diagnostic scores, visible in the expanded audit detail:
Finding reliability
How much can you rely on this finding? Calculated as A × E × R × 100 where:
A (Agreement) — Did all three reviewers reach the same conclusion? Unanimous (3–0) = 1.0, majority (2–1) = 0.75, no consensus = 0.5.
E (Evidence) — Is the conclusion grounded in explicit clause text, or inferred? E ranges from ~0.65 (weak, inferred) to ~0.95 (strong, grounded in direct text). If the challenger confirms the deviation as substantive, E rises; if it finds the difference cosmetic, E falls. This is why two findings with identical reviewer agreement can have very different reliability scores — one may be anchored in exact clause language while the other depends on interpretation.
R (Reasoning) — Did the finding go through every stage of the pipeline? R ranges from ~0.80 (challenge skipped) to 1.00 (full chain: extraction → evaluation → challenge → severity). Hidden dependencies — cross-referenced sections, cascading definitions — also reduce R because they make the reasoning path harder to verify independently. Most DEVIATES findings score 0.90 or above; lower values flag provisions where the pipeline had to make assumptions.
A score of 80+ is strong. Below 50, the finding is withheld rather than surfaced.
Stricter review (drop score)
How does this hold up under the most conservative reading? CAM recalculates the score with stricter evidence standards and applies a structural fragility penalty. The “drop” is the difference — called the Assertion Sensitivity Gap (ASG).
A small drop (ASG < 15) means the finding is stable regardless of how strictly you read it.
A large drop (ASG > 28) means the finding looks persuasive but weakens under scrutiny — this is what triggers “Impact Unclear.”
This is the key innovation of CAM. Traditional AI tools report only one confidence score. CAM reports two — exposing findings that appear reliable but aren’t.
Clause complexity
How tied is this clause to other parts of the lease? Factors include:
Internal cross-references (e.g. “subject to Section 9.1”)
Definition overrides (a defined term was changed elsewhere)
Negation or conditional scope changes
High complexity doesn’t mean the finding is wrong — it means the clause’s meaning depends on context that may require expert interpretation.
Privacy & Data Handling
AI Providers: Document text is processed by the following AI providers for analysis:
Anthropic (Claude) — API data never used for training. 7-day abuse monitoring retention.
OpenAI (GPT) — Opted out of training by default. 30-day retention.
Google (Gemini) — Opt-in only for data sharing. 55-day retention.
xAI (Grok) — API data siloed from training.
None of these providers train their models on data submitted through their APIs.
App-Level Retention:
Uploaded documents: Retained for 7 days to support adding provisions or leases. Immediately deleted on user request or after 7-day expiry.
Analysis results: Retained for 7 days via your unique link. Immediately deleted on user request or after 7-day expiry.
Anonymized metadata: Provision types analyzed, severity distributions, and processing metrics are retained (with no client content) to improve detection accuracy.
Create an Evaluation Rule
This rule will be applied to future runs. Edit the suggestion below to be as specific as helpful.
My Evaluation Rules
These rules are injected into the AI evaluation prompt on future runs. Rules apply to all future analyses using this access code.