Large diffs hide the primary edit
A navigation reorder, repeated footer update or generated markup block can surround the one sentence, price or policy clause that actually changed the decision.
AI context grounded in stored change evidence
Use AI to explain the change, not replace the evidence.
Start with an exact diff or screenshot, then add a concise summary, category, severity, confidence and bounded follow-up answers that help a reviewer decide what deserves attention.
- Input
- Stored before + after
- Context
- Summary + severity
- Triage
- Review recommendation
Sample evidence review
acme.com/terms · change 0192
EXACT DIFFMain content
− Cancellation requests must be received within 30 days.
+ Cancellation requests must be received within 14 days.
AI SUMMARY
The cancellation window was shortened from 30 days to 14 days, reducing the time customers have to submit a request.
- Category
- Legal
- Severity
- High
- Confidence
- 94%
Summary is attached to the preserved before-and-after clause.
A raw diff can be exact and still be difficult to prioritize.
Reviewers need a fast explanation of consequence without losing the source material that makes the explanation auditable. The sequence matters: evidence first, interpretation second.
Urgency is not the same as diff size
A one-word eligibility change may matter more than an entire rewritten landing page, while a high pixel percentage may simply reflect a breakpoint or campaign refresh.
Ungrounded summaries are difficult to trust
A confident paragraph without the before-and-after record creates a new claim to verify. The reviewer should always be able to return to the exact captured source.
Interpretation stays connected to proof.
AI output is an additional review layer on top of deterministic monitoring, not a hidden replacement for the comparison or the human decision.
Concise change summaries
Turn a long text, structural or field-level diff into a short account of the primary movement and why a reviewer may care.
Category and severity
Attach bounded labels such as pricing, legal, product or security context and an urgency level that can support inbox filtering.
Attention recommendation
Mark changes as review recommended, no immediate review or review manually, with a concrete reason and confidence attached.
Semantic alert decisions
Retain meaning-equivalent wording in history while allowing changes to obligations, values or availability to continue through the alert policy.
Change-scoped questions
Ask what changed, what a customer would notice or whether the stored evidence suggests material impact without opening an unrestricted web chat.
Deterministic fallback
If the model or usage allowance is unavailable, exact text, page-area, screenshot, JSON patch and metadata evidence still render and the change stays in manual review.
Evidence enters the model only after the change is recorded.
This ordering keeps detection reproducible and makes every summary traceable to a timestamped state transition.
- 01
Capture and compare
Create the source-specific diff from the prior successful baseline without relying on a language model to detect movement.
- 02
Bound the context
Select the stored summary inputs, changed sections and structured fields relevant to this one change record.
- 03
Generate review context
Return a concise summary, category, severity and attention recommendation while retaining confidence and the underlying evidence in the same workflow.
- 04
Apply human or routing policy
Let a reviewer inspect the source or allow a configured semantic rule to decide whether the event should notify or stay quiet.
AI context supports review; it does not certify the conclusion.
Material legal, security, accessibility and commercial decisions should be made from the captured evidence and the responsible team's domain judgment.
- Treat the exact diff, screenshot or structured operation as the review source whenever the wording of an obligation, price or API contract matters.
- AI summaries describe the stored comparison; they do not independently verify the live site's intent, author or business approval state.
- A low-confidence or unusually broad change should be opened for direct evidence review instead of routed only from its summary label.
- Usage limits and model failures degrade to deterministic evidence rather than stopping monitor checks or erasing the recorded change.
Continue with the related workflow.
Questions about ai website monitoring
Before you add the first monitor
- Does AI decide whether a website changed?
- No. OnChange first performs a deterministic comparison of the relevant text, HTML, screenshot, JSON, sitemap, robots.txt or accessibility evidence. AI is applied afterward to summarize and classify the recorded difference, so a model response is never the only proof that a change occurred.
- What information can an AI change summary include?
- A review can describe the primary edit, identify the affected header, main content or footer, attach a category and severity, and recommend whether a person should inspect it. The dashboard still exposes the exact before-and-after evidence and a reviewer can ask bounded follow-up questions about that specific record.
- How does semantic-only alerting reduce noise?
- Semantic mode can keep meaning-equivalent wording changes in history without notifying as if the underlying obligation, price or availability changed. Deterministic thresholds and source filters still run first, and the original diff remains available for review.
- Which plans include AI-assisted change review?
- AI summaries, semantic alerts and change chat are included on Pro and higher plans, subject to the plan's AI usage allowance. When the allowance is unavailable or a model call fails, the monitor still records and displays its deterministic evidence.
Give one noisy change stream a clearer review layer.
Start with deterministic monitoring for free, then use Pro when the team needs AI summaries, semantic alerting and change-scoped questions.