Oops! Something went wrong while submitting the form.
Not another database. Sai checks each field against its source today, shows you where the value came from, and re-checks on a schedule so the record stops decaying.
You point at the list. Sai does the other three steps, and does them again next month.
A CRM view, a spreadsheet, or a list — tell Sai which fields matter.
Sai looks it up today rather than reading it out of a stored dataset.
Every value arrives with the page it came from and the date it was checked.
The same run repeats monthly, so the record stays current instead of decaying.
The question isn't how many fields got filled. It's how old each one is.
Sai verifies each field against a live source at the moment it runs, and shows how recently every value was confirmed — so a fresh field and a nine-month-old one never look the same.
Each value arrives with the source it came from and the date it was checked. A field Sai could not confirm is left open rather than filled with a plausible guess.
The hundredth enrichment run works exactly like the first, with no retraining — and every repetition makes it cheaper and more reliable.
Sai proposes each change side by side with the current value and writes nothing until you accept. Fields it could not verify stay empty instead of being filled in.
B2B data enrichment fills in the details a record is missing: headcount, industry, location, funding stage, job title, contact details. Every provider competes on the same two numbers — how many fields they can fill and how accurate those fields are on the day they are supplied. Both numbers are real, and neither is the number that hurts. What hurts is that a field which was correct when it was written is presented, six months later, with exactly the same confidence as one confirmed this morning. Nothing in the record tells you which is which.
Sai treats enrichment as something that happens now rather than something looked up in a stored dataset. For each record it checks the fields you care about against a live source — the company's own site, its careers page, its announcements — and returns each value with the page it came from and the date it was confirmed. Where a value cannot be verified, the field is left open rather than filled with a plausible guess. The same run is then scheduled to repeat, because the point is not to enrich the list once but to stop it decaying between quarters.
CRM data enrichment usually stalls on plumbing. The record lives in a CRM that may be an internal build or a heavily customised older system; the truth lives on a company website, a careers page or a portal; and neither end offers a convenient public API. Tools that depend on integrations either skip those sources or ask someone to copy values across by hand — which is where the manual review that teams fall back on comes from. Sai operates a real browser inside your own logged-in sessions instead, reading the source and updating the record the same way a person would. If a person can click it, Sai can run it.
The instinct when enrichment disappoints is to buy a larger dataset. It rarely helps, because coverage and freshness are different problems: a vendor with more records still supplies each one as a snapshot, and no single provider covers every industry and region equally well. Checking a smaller number of fields, at their source, on a repeating schedule, produces a record you can act on — and it makes the fields that feed scoring, routing and outreach reliable enough to automate downstream.
Free your hands from the computer.
Try Sai
