TL;DR
Choose AI proofreading for high-volume, low-risk documents, and choose manual proofreading where one error can cost money or trust. AI is faster and more consistent than people on spelling, grammar and house style. People, on the other hand, are better at meaning, intent and owning the result. For client work, let AI make the first pass and have a person sign off. Reports full of figures also need a numeric check on totals, charts and repeated values. Grammar tools and chatbots do not run that check reliably, so plan for it.
Key Takeaways
- AI proofreading wins on speed and consistency for spelling, grammar and style, while manual proofreading wins on meaning, intent and accountability.
- Pick by document risk: AI alone for internal drafts, AI plus human sign-off for client work, and human-led review for contracts and filings.
- Language checks and number checks are separate jobs, because grammar tools and general LLMs check sentences, not sums.
- Large language models tend to rewrite more than a human proofreader would, so lock names, figures, dates and defined terms in the prompt.
- Test any tool on 50 to 100 of your own documents and count missed errors, false flags and meaning changes before you trust it.
- Ask every vendor about training opt-outs, retention and storage before confidential documents go anywhere near the tool.
The Board Pack That Passed Every Check
Picture the Thursday before a quarterly board meeting. The finance team runs the board pack through a grammar checker, and it comes back clean. A senior analyst reads all 48 pages that evening and signs off.
On page 2, the summary says revenue grew 12% on the prior quarter. The regional table on page 31 totals $43.6 million against $40.0 million, which is growth of 9%. The 12% came from an earlier draft that still showed $44.8 million before a late adjustment.
Nobody on the team saw it. A board member did, though, ten minutes into the meeting.
The grammar checker did its job, and so did the analyst. Neither was built to check that a sentence on page 2 agrees with a table on page 31. That blind spot is why choosing between AI proofreading and manual review starts with what each can catch, then with speed or cost.
What Is AI Proofreading and How Does It Work?
AI proofreading is software that finds and fixes surface errors in finished text while keeping the author’s meaning. Those errors include spelling, grammar and punctuation, as well as word choice and breaks from house style. The check runs at the last stage of writing, once the content and structure are settled.
Three kinds of technology do the work under the hood, and each catches a different slice of errors. So knowing which one a tool relies on tells you a lot about what it will miss.
- Rules engines. Hand-written patterns flag known problems such as doubled words, agreement errors and banned terms. They are predictable and easy to explain, but they only find what someone wrote a rule for.
- Machine-learning grammar models. Trained on large sets of corrected text, they catch errors that rules miss and also suggest fluent fixes. They still read sentence by sentence, so a contradiction between pages is invisible to them.
- Large language models (LLMs). General-purpose generative AI models such as ChatGPT read whole passages and can explain each change. They also tend to rewrite more than you asked, and they can state a wrong fix with full confidence.
Many commercial tools blend all three. For example, a writing assistant might use rules for style-guide terms, a trained model for grammar and an LLM for tone suggestions. None of those layers re-adds a table unless someone built it for that job.
AI Proofreading vs AI Editing vs Grammar Checking
These three labels get used as if they meant the same thing, so the confusion leads teams to buy the wrong tool. The real difference is how far the software may change your text.
| Question | Grammar checking | AI proofreading | AI editing |
|---|
| Goal | Fix rule-level errors | Fix every surface error and inconsistency, keep the meaning | Improve clarity, tone and structure |
| What it may change | Single words and punctuation | Words, punctuation, terms and formatting | Whole sentences and paragraphs |
| Meaning | Untouched | Preserved by design | Can shift, since rewriting is the goal |
| Best stage | While drafting | Final pass before release | Early and middle drafts |
| Typical risk | Misses errors when they need context | Misses issues that span the document | Meaning drift and a lost voice |
If a tool rewrites your sentences, it is editing, even when the vendor calls it proofreading. That distinction matters especially for any document where the exact wording carries legal or financial weight.
Language Errors vs Quantitative Errors: What Each Approach Catches
Business documents carry two kinds of errors, but the tools on the market mostly address one of them. Language errors live inside a sentence, while quantitative errors live between numbers, often pages apart.
Take a 60-page annual operating report, for example. Three errors are hiding in it, and each one reads as perfectly good English.
- A cost table lists $6.1 million, $4.4 million and $2.3 million, then shows a total of $13.2 million. The rows add up to $12.8 million.
- A bar chart labels headcount as 412, while the table beside it says 421, a simple transposition.
- Page 3 reports customer retention of 94%, while page 40 reports 91% for the same period.
A grammar tool passes all three, because every sentence is well formed. A general LLM may catch the transposition only if both numbers sit in the same chunk of text. It may never re-add the table, and it cannot compare page 3 with page 40 unless the whole file is in view at once.
A careful human reviewer can catch all three, though fatigue makes that unreliable late at night on page 40. A quantitative checker is software that parses tables, recalculates totals and matches every repeated figure across the file. It is built to catch all three, but it says nothing about grammar.
Which Reviewer Catches Which Error
Read down the last two columns. A human reviewer catches number errors only when they stop to re-add or compare. A quantitative checker catches them every time but ignores grammar.
| Error type | Grammar tool | General LLM | Human reviewer | Quantitative checker |
|---|
| Spelling and grammar | Strong | Strong | Good, but tires at volume | Not its job |
| Tone and clarity | Limited | Strong, but may overreach | Strong | Not its job |
| Correctly spelled wrong word (“form” for “from”) | Partial | Good | Good | Not its job |
| Table rows that do not sum | Misses | Unreliable | Catches only when they re-add | Catches |
| Chart label vs table value | Misses | Sometimes | Catches only when they compare | Catches |
| Same figure cited differently on two pages | Misses | Only if both are in view | Often missed, especially in long files | Catches |
| Meaning and intent | Misses | Partial | Strong | Not its job |
Language quality and number accuracy need different checks, so a board pack, investor report or regulatory filing needs both.
Case Study
85% More Accurate Invoice Processing for Trax
Automated matching and reconciliation removed duplicate entries from a high-volume freight invoice flow. The outcome was 85% more accurate invoice processing, 41% less processing time and 17% higher cost savings.
Read the Case Study →
AI vs Manual Proofreading: Side-by-Side Comparison
Once you separate the error types, the comparison between AI and manual proofreading gets more honest. Each approach wins on different measures, so the table sets them side by side.
| Factor | AI proofreading | Manual proofreading |
|---|
| Speed | Seconds to minutes per document | Hours, especially for a long document |
| Consistency | Applies the same rules every time | Varies by reviewer, so results shift with fatigue |
| Context and intent | Weak, because it reads patterns rather than purpose | Strong, since reviewers understand audience and stakes |
| Terminology | Overcorrects product names and jargon unless it has a glossary | Knows the domain when the reviewer does |
| Numbers and cross-references | Grammar tools miss them, but specialist tools check them | Caught only when the reviewer has time and focus |
| Cost model | Fees that grow more slowly than volume | Labor cost that grows with every page |
| Accountability | Nobody signs for the software’s output | A named person owns the sign-off |
| Data exposure | Text goes to a vendor unless deployed privately | Stays with your reviewers |
Read the table as two sets of strengths rather than one winner. AI gives you throughput and sameness, which matters most when a team ships hundreds of documents a week. Manual review instead gives you judgment and a person who answers for the result.
Cost deserves a closer look. Software fees stay fairly flat as volume grows, while a reviewer’s hours rise with every page. That gap favors AI for volume work. It flips for a 20-page contract, though, where one missed word can cost more than years of licenses.
Where Manual Proofreading Breaks Down at Volume
Human reviewers do excellent work on the first ten pages and less reliable work by page sixty. The cause is attention: when a task repeats and errors are rare, focus fades.
- Fatigue. Error spotting drops as a session runs long, so late pages get a lighter read.
- Cross-reference limits. Nobody holds 200 figures in memory, so a number on page 3 rarely gets checked against page 40.
- Turnaround. A long report can wait days for a free reviewer, so release dates slip.
- Inconsistency. Two reviewers apply the same style guide differently, so one document can come back with two sets of fixes.
The answer is to move the mechanical part of the job to software, which stays steadier at it, and keep people on the judgment calls.
Where AI Proofreading Falls Short
AI fails in different ways, and those failures are harder to spot because the output looks polished. The biggest risk is meaning drift, where a fix reads better but says something else.
Research supports the concern. A study presented at BEA 2025 compared human proofreaders with ChatGPT-4o, Llama3.1-8b and Deepseek-r1-8b on texts by second-language writers. The models reworked vocabulary and sentence structure more extensively than the human proofreaders did, and the pattern held across all three models.
That finding matters for teams with many second-language writers. An LLM pass can make their text more fluent while moving it further from their own words. So when a reviewer checks an LLM-proofread draft, they should compare it with the original instead of reading only the clean version.
Two other failures turn up often in business documents. Tools overcorrect technical terms, so a product called “DataSync” becomes “data sync” across a whole manual. They also suggest confident wrong fixes, such as swapping a precise legal phrase for a more common one.
The third gap is privacy. Every cloud tool sends your text outside the network unless the vendor supports a private deployment, so treat data handling as a selection criterion.
How Accurate Is AI Proofreading?
Accuracy is the first question every buyer asks, but a vendor’s headline percentage cannot answer it for your documents. A tool tuned on student essays can behave very differently on a drug label or a bond prospectus.
The useful answer, then, is a measurement you run yourself. Five measures cover what matters.
| Measure | What it tells you | How to count it |
|---|
| Precision | How many flags are real errors | Real errors flagged divided by all flags, as a share |
| Recall | How many real errors the tool finds | Real errors flagged divided by all real errors in the sample |
| False positives | Noise your reviewers must clear | Flags that were not real errors |
| False negatives | Errors that would have shipped | Known errors the tool missed |
| Meaning preserved | Whether fixes changed what the text says | Accepted edits that altered meaning |
To run the test, gather 50 to 100 of your own recent documents across the types you publish. Before the tool runs, have a senior reviewer mark every real error, or plant known errors in clean copies. Then run the tool and compare its flags with that answer key.
Here is the arithmetic on a made-up test run. Suppose the sample holds 200 known errors and the tool raises 180 flags, of which 150 are real. Recall is 150 out of 200, or 75%, and precision is 150 out of 180, or about 83%.
Those two numbers mean different things in practice. Low recall means errors ship, so the human pass has to stay thorough. Low precision means reviewers waste time on false alarms and soon start ignoring flags. Count meaning changes on their own, because one altered clause in a contract outweighs dozens of fixed commas.
Which Approach Should You Choose? A Risk-Tiered Framework
The choice between AI and manual proofreading comes down to one question: what does a single error in this document cost? Once you match the depth of human review to that cost, the decision becomes routine.
| Tier | Who proofreads | Typical documents | Who signs off |
|---|
| Tier 1: AI wins | AI only, with spot checks | Internal notes, drafts, meeting summaries, chat replies | The author |
| Tier 2: AI leads, manual signs | AI first pass, then a human review | Client reports, proposals, marketing copy, web pages | An editor or team lead |
| Tier 3: Manual wins | Human-led review, with AI as a second reader | Contracts, regulatory filings, clinical documents, financial statements | A named subject expert, often with a second reviewer |
AI earns its keep in Tier 1 with no debate. An error costs a little embarrassment, and speed matters more than polish.
Tier 2 covers most external business writing. AI clears the mechanical errors first, so the human reviewer spends their time on accuracy, tone and claims. The person still owns the sign-off.
People stay in charge at Tier 3, because accountability cannot be handed to software. AI still helps here as a second reader that catches what a tired reviewer missed, and it never has the final word.
When a document does not fit neatly, ask two questions in order. If it stays inside the company, then use Tier 1. If it goes outside and an error could create legal, financial or safety exposure, use Tier 3. Everything else that leaves the company is Tier 2. Any document with tables, charts or repeated figures also gets a numeric check, whatever its tier.
How to Set Up a Hybrid AI and Human Proofreading Workflow
A hybrid workflow puts the tiers into daily practice. It hands the repetitive work to software while saving human attention for the calls that need judgment. Seven steps cover most teams.
- Write the style guide and glossary. List product names, defined terms, preferred spellings and banned phrases. This one document does more for AI accuracy than any tool setting.
- Run the AI pass with terms locked. Load the glossary into the tool, or into the prompt for an LLM, so it never “fixes” approved terms.
- Triage the flags. Accept mechanical fixes in bulk, but route anything that touches meaning, numbers or names to a person.
- Run a numeric check on data documents. Recalculate tables, match chart labels to source values and then compare repeated figures across the file.
- Do the human pass by tier. A Tier 2 review reads for accuracy and tone. A Tier 3 review reads every line and then signs.
- Sample for quality. Pull a few finished documents each week, then have a second reviewer check them in full.
- Log errors and update the glossary. Every miss or bad fix becomes a new rule, so the next document gets a better first pass.
Routing matters as much as the checks themselves. Teams that already move documents between systems can reuse the patterns from combining AI with RPA. In practice, a bot sends each file to the right reviewer queue based on its tier.
Reviewer roles shift too. Proofreaders spend less time hunting commas, so more of their day goes to judging meaning and approving releases. Plan that shift the way you would any other AI change management effort.
Prompt Guardrails for LLM Proofreading
When your team uses a general LLM for the AI pass, the prompt is your main control. Without guardrails, the model treats proofreading as an invitation to rewrite.
You are proofreading a business document. Fix only spelling,
grammar, punctuation and clear typos. Follow the attached style guide.
Rules:
1. Preserve the meaning of every sentence. Do not rewrite for style.
2. Do not change names, figures, dates, URLs, citations or defined
terms. The glossary below lists terms that must stay as written.
3. For each change, show the original text, the correction and a
one-line reason.
4. If you are unsure whether something is an error, label it UNSURE
and leave the text unchanged.
5. Do not add, remove or invent facts, sources or numbers.
Glossary: [paste approved terms]
Text: [paste one section]
Rule 3 does the most for review speed, because a person can accept or reject each change in seconds. Rule 4 also turns guesses into questions for a reviewer.
Send long documents in sections, and include the glossary in every call so the terms stay locked, a basic form of context engineering. An LLM proofreads one section more reliably than a whole report.
What to Measure After Rollout
Record a baseline before the AI pass goes live, so you can measure its return later. Five measures show whether the hybrid workflow is working.
| KPI | What it shows | Warning sign |
|---|
| AI-introduced errors | Fixes that made the text wrong | Any rise after a tool or prompt change |
| Missed errors in the QA sample | What both passes let through | The same error type showing up twice |
| Human minutes per document | Where reviewer time goes | No drop even after the first month |
| Turnaround time | Time from draft to approved release | Queues forming at the human pass |
| Rework rate | Documents corrected after release | Rework rising, especially on Tier 2 documents |
Review these monthly for the first quarter. The error log from step 7 will then explain most of the movement.
How to Choose an AI Proofreading Tool for Business Use
Proofreading tools fall into five categories, and the category tells you more than the brand. It shows what gets checked, where your text goes and how much control you keep.
| Category | Best for | Watch for |
|---|
| Built-in checkers in Word and Google Docs | Everyday spelling and grammar while drafting | Little style-guide control and no number checks |
| Writing assistants such as Grammarly | Tone, clarity and shared style guides across apps | Rewrite suggestions that drift from meaning; data settings to review |
| API checkers such as LanguageTool | Building checks into your own apps in more than 30 languages | Needs developer setup; checks grammar and style only |
| General LLMs such as ChatGPT | Explained changes and flexible prompts | Meaning drift, invented fixes and data terms that vary by plan |
| Custom or quantitative agents such as Mike | Tables, charts and figures across long documents | Narrow scope, so pair it with a language tool |
A common setup pairs a language checker for every document with a quantitative agent such as Mike for reports full of figures. In a monthly investor update, the language checker cleans the commentary while Mike checks the tables and cross-page figures.
The API route suits teams that want checks inside their own systems. LanguageTool’s proofreading API returns results as JSON over HTTP, so a document portal can check text on upload.
Check the usage rules before a portal goes live. As of October 2026, LanguageTool’s public API page limits each IP to 20 requests and 75KB of text per minute, with 20KB per request. For automated requests such as upload checks, it asks teams to self-host LanguageTool or buy an Enterprise account.
How to Score an AI Proofreading Shortlist
Once you know the category, score the shortlist against criteria that match your risk tiers.
- Accuracy on your documents, measured with the 50-to-100 document test above.
- Glossary and style-guide control, so approved terms never change.
- An explanation for each change, which speeds up review and builds trust.
- Number and cross-reference checks, especially if you publish data-heavy documents.
- Integration with the editors, document systems and approval flows your team already uses.
- Data handling, covered in the questions below.
Data Privacy and Governance Questions to Ask
Proofreading tools see your most sensitive text before anyone else does, from draft earnings to contract terms. Ask these questions before any confidential document goes in.
- Is our content used to train the vendor’s models, and can we switch that off? As of October 2026, Grammarly’s trust center says it does not sell or monetize user and customer content. Users can also turn off product improvement and training.
- How long is text retained, and where is it stored?
- Can the tool run in our own cloud tenant or on premises?
- Which business terms apply to our plan? For general chatbots, read the vendor’s business terms instead of assuming consumer settings carry over.
- Who at the vendor can access our documents, and is that access logged?
Wider risks of sending documents to language models, such as data leakage and prompt injection, are covered in our LLM security guide.
Two frameworks help structure the AI governance side. The NIST AI Risk Management Framework, released January 26, 2023, is intended for voluntary use and gives teams a shared way to manage AI risk. NIST later added a generative AI profile, NIST-AI-600-1, on July 26, 2024.
ISO/IEC 42001:2023 sets requirements for an AI management system. The standard covers how an organization governs its AI systems, so a certificate against it says nothing about any single tool’s accuracy.
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AI Proofreading Across Industries
The same tiers apply in every sector, but the errors that hurt most differ. The table maps each industry to its riskiest errors and a sensible starting tier.
| Industry | Errors that hurt most | Recommended tier |
|---|
| Finance and banking | Totals, rates and figures that disagree across a report | Tier 3 for statements and filings; Tier 2 for client updates |
| Legal | A changed defined term or clause that shifts obligations | Tier 3 for contracts; Tier 2 for internal memos |
| Healthcare and life sciences | Doses, units and labeling text | Tier 3 for clinical and patient-facing material |
| Research and publishing | Citations, data tables and author names | Tier 2, then Tier 3 for final proofs |
| Government | Figures and terms in public notices and reports | Tier 3 for anything legally binding |
| Corporate reporting | Repeated KPIs across board packs and annual reports | Tier 2 plus a numeric check; Tier 3 for audited sections |
How the Tiers Play Out by Sector
Legal teams often run proofreading next to other document processing tools. An AI legal document summarizer condenses long agreements, although that is a different job from checking final wording. Wider legal process automation can then route each contract to the right review tier.
Healthcare, research and government face a quieter risk: errors that read well. Picture a dosing table that reads “mg” where the source says “mcg”. The word is spelled correctly and the sentence is grammatical, so only a Tier 3 reviewer or a numeric check will stop it.
Research teams meet the same problem in citations and data tables, where a transposed figure still looks like good English. Binding government notices start at Tier 3 for the same reason.
Finance and corporate reporting stand apart because their documents are dense with numbers. In those teams, the numeric check often prevents more costly errors than the language check does.
How Kanerika Automates Quantitative Proofreading
Kanerika builds AI agents for document-heavy work, and one of them handles the numeric check this guide keeps returning to. Our agent Mike is built for the errors that grammar tools pass.
Mike checks table arithmetic, chart alignment and document-wide consistency in PDF and image files. Each flagged issue comes with an explanation, and the report groups issues by tables, charts and document-wide checks. The free version takes PDFs of up to 10 pages, the Pro version has no page cap, and documents are processed without being stored.
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Mike Demo | AI Agent for Quantitative Proofreader
See Mike, Kanerika’s quantitative proofreading agent, check a document’s table arithmetic, chart alignment and consistency, with an explanation for each flagged issue.
In client work, we pair agents like Mike with the review tiers described above. The agent clears the arithmetic and consistency checks first, while a language tool handles the mechanics. A named reviewer then signs off at the depth the document’s tier requires.
When the source files are scanned forms or invoices, data extraction comes first, so the checks run on clean, structured values.
Automated checking of number-heavy documents holds up at volume. Trax, a spend management company, handles high volumes of freight invoices for multinational clients. Kanerika automated its invoice matching and reconciliation and eliminated duplicate entries. Trax saw an 85% increase in invoice processing accuracy, as well as a 41% reduction in processing time and a 17% increase in cost savings.
That project was invoice validation rather than proofreading. Still, it follows the same principle as this guide: let software check numbers at volume, and let people judge what it flags. Teams that want help designing a tiered review process, or building checks for their own document types, can work with our AI consulting services team.
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Kanerika designs and builds AI agents and review workflows for document-heavy teams, from tool selection and risk tiers to numeric checks and governance.
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AI or Manual Proofreading: The Bottom Line
Use AI when volume is high and an error costs little. Use people when meaning, money or compliance is at stake, and give them AI as a second reader. Add a numeric check to any document full of figures, because neither grammar tools nor tired reviewers reliably catch bad totals.
Start with a style guide, then test tools on your own documents and measure what each pass catches. So, AI proofreading or manual proofreading? Choose AI proofreading for speed on low-risk text, and choose manual proofreading wherever a single error has a price.
Frequently Asked Questions
What is an AI proofreader?
An AI proofreader is software that checks finished text for spelling, grammar, punctuation, word choice and style errors and suggests fixes that keep the meaning intact. Options range from built-in checkers in Word and Google Docs to writing assistants, API services and large language models. Specialist agents also check numbers, tables and charts in long reports.
Which is the best AI for proofreading?
The best choice depends on the document. Built-in checkers suit everyday drafting, and writing assistants such as Grammarly suit tone and team style guides. API checkers such as LanguageTool fit custom apps, and general LLMs explain their changes well. Reports full of figures need a quantitative checker that recalculates tables and compares repeated values across pages.
Is AI proofreading safe for confidential business documents?
It can be, if the vendor’s terms and settings fit your risk. Check whether your content trains the vendor’s models and whether you can opt out, how long text is retained and where it is stored. Prefer business plans or private deployments for sensitive files, and keep contracts, filings and unreleased financials under human-led review.
Can AI proofreading check numbers and calculations?
Standard grammar tools do not check numbers, and general LLMs check them unreliably, especially across long files. Quantitative checkers are built for the job. They recalculate table totals, compare chart labels with source values and match figures repeated on different pages. Kanerika’s Mike agent, for example, flags table arithmetic, chart alignment and consistency issues with an explanation for each.
Can AI proofreading change the meaning of a sentence?
Yes, and that is the main risk with large language models. A 2025 study found that LLMs reworked vocabulary and sentence structure more extensively than human proofreaders did. Reduce the risk by telling the tool to preserve meaning, locking key terms, asking for each change next to its original text and having a person review edits in important documents.
Can I use AI to proofread a document?
Yes. Paste the text into a writing assistant or a general LLM, or run the file through a dedicated checker, then review each suggested change before you accept it. Give the tool your glossary so it leaves names and defined terms alone. For client-facing or regulated documents, a person should still read the final version and sign off.
Is AI any good at proofreading?
AI is very good at spelling, grammar, punctuation and consistent style, and it works in seconds without tiring. It is weaker at judging meaning, intent and audience, and general models sometimes rewrite more than they should. Results vary by document type, so test any tool on 50 to 100 of your own files before relying on it.
Can AI proofread legal documents?
AI can make a useful first pass on legal documents, catching typos, inconsistent defined terms and formatting slips. Contracts and filings still belong in human-led review, because one changed word can shift an obligation and software cannot take responsibility for the result. Lock defined terms in the tool’s glossary or prompt, and have a qualified reviewer sign off.
Is AI making proofreaders obsolete?
AI is reshaping the proofreader’s job. Software takes over the mechanical checks, so proofreaders spend more of their time on meaning, accuracy, tone and final approval. Regulated and high-stakes documents still need a named person to sign off. Teams that plan for this new reviewer role tend to get better results from both their people and their tools.
Can ChatGPT do proofreading?
Yes, ChatGPT can proofread text and explain each change, which makes review faster. It also tends to rewrite more than a human proofreader would, so tell it to preserve meaning and leave names, figures, dates and defined terms unchanged. Ask it to list each original and correction, and check its business data terms before sharing confidential documents.
Is Grammarly or ChatGPT better for proofreading?
Grammarly suits steady, in-line checking across email, documents and browsers, with shared style guides for teams. ChatGPT suits flexible requests and explained changes on a pasted section, and it needs a careful prompt to avoid rewriting. Many teams use a writing assistant for daily work and an LLM for occasional deeper passes, with a human review for anything external.
What is the best method to proofread a document?
Work in layers. Run an AI pass with your glossary locked, triage the flags, then check every table, chart and repeated figure if the document has numbers. Finish with a human read scaled to the document’s risk, from a quick scan for internal notes to a line-by-line review and formal sign-off for contracts and financial statements.
How accurate is AI proofreading?
Accuracy depends on the tool and the documents, so published percentages rarely transfer to your files. Measure it yourself on 50 to 100 recent documents. Count precision, the share of flags that are real errors, and recall, the share of real errors the tool finds. Also track false alarms, missed errors and any accepted fix that changed the meaning.
