Negative Keyword Automation With AI (Find + Apply via Claude)

Published May 22, 2026 by Ahmed Ashraf · Founder, PaidSync.ai

Most AI tools for negative keyword management stop at the list. They analyze your search terms, identify the wasted ones, and export a CSV for you to paste into Google Ads. PaidSync runs three composite tools (get_wasted_spend_report, analyze_search_term_ngrams, add_negative_keywords) end-to-end in a single Claude conversation. Claude finds the candidates, you review and approve, Claude applies them. The manual step after the AI is gone.

This guide shows the exact 7-step workflow, the real Claude prompts, and what distinguishes this approach from tools that only analyze without executing.

Why most AI negative keyword tools fall short

The analysis-only approach to AI negative keywords is the industry standard right now. Ryze AI, for example, flags wasted spend as part of its optimization cycle and can surface negative keyword recommendations. The limitation is that the recommendations either apply autonomously (no per-decision approval) or generate a list you still have to implement manually.

Neither outcome is ideal for account managers who need a clear record of what changed and why. Autonomous application means changes happened without your review. Manual export means the AI conversation was just the research step, not the work itself.

PaidSync sits in a different position. The AI identifies candidates, presents them for your review in the same conversation, and executes only on what you confirm. Every negative has your sign-off. The record of what was applied and why lives in the conversation history.

The 3 tools that make this work

get_wasted_spend_report

Returns search terms with spend above a threshold and zero or near-zero conversions, broken out by campaign, ad group, match type, impressions, clicks, and cost per acquisition. The date range is configurable. A 60 to 90-day window gives enough data to distinguish genuinely irrelevant terms from low-volume converters that just haven't triggered yet.

analyze_search_term_ngrams

Breaks your search term history into 1-gram, 2-gram, and 3-gram patterns and scores each by frequency and conversion rate. A pattern appearing in 30 queries with zero conversions and $200 spend is a clear campaign-level negative. Single-term analysis misses these systematic patterns. N-gram analysis surfaces them regardless of how many unique queries they appear in.

add_negative_keywords

Accepts a list of terms, match types, and target scopes (campaign-level, ad group-level, or shared negative keyword list), then writes each negative to Google Ads via the API. Changes propagate within approximately 30 seconds. The tool returns confirmation with the negative keyword ID and the entity it was applied to.

The 7-step workflow

Using PaidSync, pull a wasted spend report for all Search campaigns over the last 90 days. Show terms with more than $30 spend and a conversion rate below 0.5%. Sort by spend descending. Include the campaign name, ad group name, and match type for each term.

Claude calls get_wasted_spend_report and returns a table. Typical output for a mid-size account running $5,000 to $15,000/month in Google Ads: 40 to 80 terms meeting the threshold. The top 10 by spend usually account for 60 to 70% of the total wasted budget identified.

Now run n-gram analysis on the same 90-day window across all Search campaigns. I want 1-gram, 2-gram, and 3-gram patterns sorted by total spend in non-converting queries. Flag any pattern that also appears frequently in converting queries so I don't accidentally block good traffic.

Claude calls analyze_search_term_ngrams. The cross-reference against converting queries is important. A term like "review" appears in both irrelevant queries ("software review" where someone is researching) and high-intent queries ("client review service" where someone is buying). The n-gram analysis flags this ambiguity so you can decide on match type and scope rather than blocking blindly.

Combine the wasted spend terms and the n-gram patterns into one candidate list. For each candidate, note: (1) total spend in non-converting queries, (2) whether it appears in any converting queries, and (3) your recommendation for match type and scope. Flag anything where blocking it might reduce converting traffic.

This is your main review step. Spend two to five minutes on the output. Remove any term where the converting-query risk is unclear. Conservative is correct here. You can always add more negatives next month. You cannot easily recover lost conversions from an overly aggressive block.

For the confirmed candidates, I want:
- Terms that are clearly off-topic for the whole account (like "free", "jobs", "diy"): add as campaign-level broad match negatives across all Search campaigns.
- Terms that are specific to one ad group's mismatch: ad group-level exact match only.
Present the final action plan as a table with: term, match type, target (campaign or ad group name), and estimated monthly spend recovered.

This prompt produces the approval table. Review it once more. The "estimated monthly spend recovered" column gives the business case for each negative in concrete terms.

This plan looks correct. Please apply all the confirmed negatives from the table above. For any that you flag as ambiguous or high-risk, pause and ask me before applying.

A typical session applying 15 to 25 negatives takes about 30 to 60 seconds to execute. Changes appear in Google Ads within one minute. You can verify in the UI by navigating to Keywords > Negative Keywords in the affected campaigns.

Pull the negative keyword list for the campaigns we just updated to confirm the additions appear. Then summarize: total negatives applied today, estimated monthly spend recovered, and the date I should run the next review.

How this differs from Ryze AI

Ryze AI approaches negative keywords through its autonomous optimization loop. The platform analyzes search terms continuously and can flag or block irrelevant patterns as part of its budget protection logic. For advertisers who want hands-off management, this works.

The practical difference is the conversation. PaidSync's workflow gives you a specific candidate list with spend figures, a clear scope recommendation, and the ability to override any individual term before it applies. You see exactly what is being blocked and why before it happens. Ryze's autonomous mode makes these decisions on its own schedule using its own criteria.

For agencies that need to show clients a record of every change and its rationale, the PaidSync approach produces that record as a natural byproduct of the conversation. For advertisers who genuinely want zero involvement in daily optimization, Ryze's autonomous model is the right fit.

Common patterns to block first

Accounts that have never run a systematic negative keyword audit typically have predictable waste patterns:

• Informational intent modifiers: "how to", "what is", "tutorial", "guide", "example"
• Price-sensitivity signals: "free", "cheap", "affordable", "discount", "coupon"
• Job seeker traffic: "jobs", "careers", "salary", "resume", "hiring"
• Competitor research: "alternative", "vs", "compare", "review" (context-dependent)
• DIY intent: "yourself", "diy", "homemade", "self-service" (if you sell professional services)

N-gram analysis surfaces account-specific versions of these patterns that generic lists miss. A B2B software account selling contract management tools might have "contract template free" appearing in 25 queries per week. No generic negative keyword list catches that. N-gram analysis does.

Connecting PaidSync for this workflow

You need a PaidSync account connected to Google Ads with write scope. Sign up at paidsync.ai/signup, complete the Google Ads OAuth flow with read and write permissions, and add the MCP endpoint to your AI assistant. The free tier's 15 monthly calls covers one full negative keyword session end-to-end.

See how this workflow fits into a broader AI-managed Google Ads approach, and read the negative keyword automation overview for additional context on how different AI tools handle this workflow.

Frequently Asked Questions