Strategy
How to actually use the platform — where to apply which service, in what order, for what return.
New data vs. existing data
Most PII problems split cleanly into two flows. New data arrives in real time — signups, web forms, lead imports — and benefits from inline validation before it ever touches your database. Existing data is already in your CRM, ERP or data warehouse and benefits from batch processing: dedup, enrichment, quality scoring.
The right strategy almost always combines both: stop new bad data at the door, fix the existing dataset in batches.
Real-time / inline
Single-record API calls behind your signup form, KYC step or lead intake.
- · Risk Detector at form submit
- · Disposable Email check before account create
- · Name Parser for live field auto-fill
- · Genderizer for instant salutation
Batch / pipeline
Bulk processing across millions of records — runs nightly or on import.
- · Search Cluster for dedup at scale
- · Matcher for cross-system reconciliation
- · Parser for legacy free-form name cleanup
- · Risk Detector for retro-quality scoring
Combinations that work well together
Front-line gate
Risk Detector + Disposable Email at signup — rejects junk before it gets a customer ID. The cheapest win on this list.
Search-first matching
Search Cluster for retrieval, Matcher for verification on the candidate set. Scales to billions of records without comparing every pair.
Parse, then store
Name Parser converts free-text inputs to structured fields at write time — downstream systems get clean data without changing their schemas.
Hybrid scoring
Combine Matcher score, Risk Detector score and Disposable Email verdict into a single quality grade per record — route on grade, not individual signals.
Email-first identity
Email Name Parser when the only field you have is an e-mail address — recovers the owner's name for personalisation and search.
Quality grade decay
Re-score legacy records on a rolling schedule — the disposable list and risk model change over time; verdicts on old data should too.
Where to start — and what to add later
Stop the bleeding
Wire Risk Detector and Disposable Email behind your signup form. Few hours of integration, immediate quality lift on every new record.
Clean the existing
Run a one-time batch job over your existing dataset: parse names, extract from e-mails, score risk. Get a quality baseline before deduplicating.
Match & merge
Index records into the Search Cluster, run reconciliation across systems, merge the duplicates. Hardest phase — do it last, with clean data.
Right strategy for your data
Tell us what your data flows look like and what hurts most — we'll sketch which services to apply where, and in what order.