CleanSmart provides an automated cleaning pipeline for CRM data, whose core consists of four AI processing stages: SmartMatch, AutoFormat, SmartFill and LogicGuard. The four stages run in sequence on every dataset the user connects, but the user controls what is actually applied, and no change happens without confirmation.
SmartMatch is mainly used for AI deduplication and can identify duplicate records that exact matching misses. It uses entity resolution and learned similarity scoring to find records that point to the same person, company or account, even when there are spelling differences, abbreviations and name variants. For example, "Bob Smith / Acme Corp" and "Robert Smith / Acme Inc." are recognized as the same record. It can handle nickname variants, inverted names and transposed digits in phone numbers; every merge has a confidence score, and users can set the threshold; and it provides a full audit log recording who merged what, when, and why.
AutoFormat is used to standardize formats without writing regular expressions. It covers field types such as phone numbers, email addresses, names, URLs, dates, currency and more, and each field type has its common variants. A single pass can convert each field to the canonical format specified by the user: phone supports 14 input variants converted to one clean format (+1 (555) 123-4567); names support title case, first/last name splitting and nickname handling; email supports lowercase normalization and spelling correction (such as yaho.com → yahoo.com); addresses support USPS standard street, city, state and ZIP formats.
SmartFill is used to fill missing values, based on confidence rather than guesses. It learns patterns within the dataset to predict missing values, using field co-occurrence, geographic lookup and structural rules. Predictions below the confidence threshold are flagged for review and are never applied automatically. Examples include: inferring city from ZIP code, inferring state from city, inferring country from phone prefix, and inferring company name from the domain in an email address. Every prediction shows a confidence percentage before user approval, and low-confidence fills go into a review queue instead of being silently written to the data.
LogicGuard is used to find records that look problematic, and never deletes silently. It runs rule-based and statistical anomaly detection on every field, such as age 250, future birthdays, six-digit phone numbers, income amounts deviating from the mean by three standard deviations, etc. Every flag goes into a review queue and is never silently removed. Detectionscope includes out-of-range ages, future birthdays, impossible dates, phone numbers with the wrong number of digits by country, income, transaction size and numeric outliers (a combination of statistics and rules). Every flag shows which rule was triggered and why.
The entire process revolves around the pipeline: connect → clean → review → export, a complete step-by-step flow from messy data to a clean CRM. On data security, it provides AES-256 encryption at rest, TLS 1.2 in transit, per-customer isolation and cryptographically verified deletion. Users can upload a CSV or connect a CRM, and most datasets are cleaned within minutes, with no credit card required.