Data-driven decisions
Make hiring decisions on verified identity, employment history and clearance results instead of self-declared CVs.
← Use cases / Recruitment & HR
Background checks in minutes, not weeks.
Request identity, employment history, and criminal clearance checks in one call — candidates connect their accounts directly, and results land on your HR dashboard as fast as 10 minutes later.
The shape · Ascending levels — a candidate verified step by step as they move toward an offer.
The problem
A traditional background check means a recruiter chasing references by phone and waiting on a candidate to scan and email documents, while a 10-to-14-day turnaround gives strong candidates time to accept a competing offer. Manual document review also leaves room for fraud that account-based verification would catch immediately.
The solution
Smile Checks lets a candidate connect their employment, ID, or financial accounts directly instead of uploading paperwork, and processes multiple check types in parallel — turning a two-week manual process into results delivered to an HR dashboard in minutes.
What changes
How it works
From trigger to decision, in four steps.
STEP 1
Trigger the event
An HR team submits a background check request for a candidate through your recruitment platform.
STEP 2
Call the API endpoint
Your backend submits a check request with the candidate's name, email, and the check types required for the role — for example idCheck, employmentCheck, and clearanceCheck.
STEP 3
Real-time processing & webhooks
The candidate is prompted to connect the relevant accounts, and Smile processes each check type asynchronously — firing a webhook to your server the moment results are ready.
STEP 4
Deliver the output
Your HR dashboard receives structured, per-check results as they complete, so a recruiter can act on identity and employment results without waiting on a slower check to finish.
The request
What you send, and what you can check.
Security & reliability
Built to be trusted in production.
Data privacy & compliance
Data is encrypted in transit and at rest, sensitive fields are tokenized rather than stored in the clear, and consent is captured at the point of collection — adapted to the data privacy regulations applicable in each market you operate.
Reliability
Idempotent requests, built-in retry support, and published rate limits mean a dropped connection or a retried request never produces a duplicate check. Each client can make up to 20 requests per second per IP address across all endpoints; anything above that returns HTTP 429.
Graceful failure
Every error returns a descriptive status code and a structured JSON body with a machine-readable code and a request ID — never a bare 500 — so a failed check is something your application can handle, not just log.
Under the hood
Relevant products
AI & data-driven decisions
How does Smile API power AI and data-driven decisions?
Smile API turns background and identity checks into structured results. Hiring teams and AI recruiting agents can use them to pre-screen candidates quickly and consistently.
Features for your models
Clean, labelled and consented attributes — identity, income, device and watchlist outcomes — ready to use as inputs for credit and fraud scoring models.
Context for AI agents
A recruiting agent can run checks, summarise results for the hiring manager and highlight anything that needs a human look.
How can AI recruiting agents use Smile API?
- The agent triggers identity and background checks, receives structured results and summarises them for hiring managers, flagging items that need human review rather than making the final hiring decision itself.
Is Smile API data structured for machine use?
- Yes. Results come back as consistent, typed JSON fields — verification statuses, scores and flags — so rules engines, machine learning models and large language model agents can read them without manual cleanup.
How is consent handled when AI uses Smile API data?
- Every request carries the consent record captured from the end user, and Smile returns only the signals that consent covers. Your models and agents work with permissioned data, never scraped or self-reported guesses.
{
"profile": "prf_8f21",
"identity": "verified",
"income": { "status": "verified", "source": "payroll" },
"device": "trusted",
"fraudScore": 0.07,
"watchlists": "cleared",
"suggestedAction": "ready_for_offer"
}