Quality Controls
Clean, normalize, deduplicate, validate, suppress, and apply rule-based or AI-assisted review where appropriate before activation or delivery.
Clean · validate · suppress · review
Know what you're getting before you use it.
EnrichPilot applies practical quality controls, matching review, validation indicators, and delivery notes so you can understand the data before it reaches your workflow.
Trust & Quality
Practical controls before delivery.
We review the data around the requirements of the project so you can understand quality, matching, and delivery before launch.
Matching & Confidence
For enrichment and identity work, available confidence indicators and review flags can help separate stronger matches from records that may need additional review.
Match confidence where applicable
Responsible Data Handling
Audience, consumer, identity, and specialized data work is prepared around agreed project requirements, available source rights, exclusions, and permitted use.
Purpose-aware preparation
Secure & Flexible Delivery
Approved outputs can be delivered through Snowflake, Delta Sharing, Amazon S3, SFTP, encrypted files, CSV/XLSX, or another agreed secure workflow.
Flexible delivery
AI-Assisted Review
AI helps us review faster without replacing judgment.
Where appropriate, EnrichPilot uses AI-assisted workflows to help identify anomalies, classify records, surface data gaps, support matching review, and accelerate research. Final delivery rules and project decisions remain based on the agreed requirements.
AI-assisted · rule-based · reviewed
Address & Location Quality
More context where the project needs it.
Where applicable, address quality work can include standardization, postal and geographic attributes, geocoding, residential/commercial classification, and delivery-related validation indicators.
Available attributes vary by record and project.
Transparency
No dataset is perfect.
We don't pretend otherwise.
People change jobs. Companies change domains. Contact information ages. Mail systems behave differently. Matching quality can vary with the identifiers available.
That's why EnrichPilot provides available quality indicators, confidence notes, review flags, and known limitations instead of hiding uncertainty.
Review before you scale.
Start with a count, sample, or small review so you can confirm coverage, fields, format, and fit before a larger delivery.