AI CRM & Record Enrichment

Gartner research puts the average cost of poor data quality at $12.9 million a year per organisation, through wasted spend, lost deals, and rep time.

Sales reps lose an average of 27% of their time, roughly 550 hours a year, dealing with bad CRM data — about $640,000 in lost productivity for a 20-person team.

Having a valid phone number on a lead increases the probability of closing that deal by 30-50%, meaning contact-data enrichment often pays for itself with a single extra closed deal per year.

37% of CRM users report losing revenue directly because of poor data quality, and roughly 1 in 4 organisations say bad data has cost them 20% or more of annual revenue.

B2B contact data decays at roughly 2.1% a month, compounding to about 22.5% a year. Job changes drive most of it — average tenure is 2.8 years, so 30–40% of your contacts move role annually.

The 1-10-100 rule of data quality: about $1 per record to get it right at entry, $10 to fix it later, and $100 if you leave it alone. Continuous cleaning is the cheap end of that curve.

Automatic detection and merging of duplicate contacts and companies, running continuously in the background — no manual data hygiene projects.

Every new and existing record is filled in with verified emails, phone numbers, job titles, and firmographic data from trusted data providers.

Emails, calls, and meetings are captured and logged against the right record automatically, so activity history is always complete.

A weekly data health report flags stale records, missing fields, and decay trends, plus real-time alerts when critical data goes bad.

Job titles, company names, phone formats and country codes are all rewritten to one convention, so your filters, segments and reports actually return what you expect.

Email validity is re-checked on a schedule and contacts who have changed employer are flagged before your next send, not after it bounces.