AI developers are licensing internal records from established companies to train their models. Process manuals, help desk history, CRM notes, project archives, QA logs and team chat all qualify. It is a license, so you keep your data and keep using it. Interested in selling? See if your business fits the program in under a minute.
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See how accurate your CRM really is and what the stale records are costing your sales team.
A CRM can look healthy on the surface while a quarter of its contacts quietly go bad. People switch jobs, companies rebrand and phone numbers change, and nobody updates the record. This calculator estimates how far your database has drifted. Enter the number of records, a monthly decay rate, the months since your last full cleanup and how many new contacts you add each month. New records are treated as accurate when they arrive, while older ones decay month by month. You see the likely number of bad records, your current accuracy percentage and the cost of cleaning now versus leaving the problem alone. A twelve month forecast shows how quickly it gets worse if nothing changes.
| Month | Total records | Out of date | Accuracy | Cost if ignored |
|---|
At 2 percent a month, a fully clean database is only about 78 percent accurate a year later. Two years without a cleanup leaves close to 40 percent of records wrong. The forecast table makes that curve visible.
Focus on records your team will actually contact in the next quarter: open deals, active accounts and top prospects. Verify emails, update titles and merge duplicates there first, then work outward.
Years of logged calls, emails, deal stages and notes show how your company sells. That history helps train new reps, improves forecasts and is one of the data types AI companies look for when licensing business records.
For B2B contact data, about 2 percent per month is the most quoted average, which works out to roughly 22 to 25 percent per year. Fast moving sectors such as tech and staffing can see 3 to 4 percent per month.
Track email bounce rate, the share of records with missing key fields, duplicate counts and the age of the last activity on each record. This calculator gives you an estimate when you do not have those reports yet.
The 1-10-100 rule says preventing a bad record costs about $1, fixing it later costs $10 and leaving it costs $100 in wasted outreach, missed deals and wrong reporting. You can change these values in the form.
Run light checks such as bounce cleanup monthly, review active segments quarterly and do a full deduplication and verification once a year. Validate emails at entry to slow decay from the start.
Archive instead of deleting where you can. Outdated contact details lose value, but the notes, deal history and outcomes stay useful for forecasting, training and AI projects.