HR integration guide
Ashby + UbiGrowth workflows
Ashby is a recruiting platform combining applicant tracking with hiring analytics. This guide covers the records that matter, how the connection should be scoped, and what the first bounded workflow should be.
Introduction
Make Ashby part of the workflow, not another silo.
Validate connector availability for your workspace
This guide covers how a team designs a HR workflow around Ashby with UbiGrowth: which records stay authoritative, how the connection should be scoped, what the first bounded workflow should be, and how to tell whether it worked.
The records that matter are Application, Candidate, Job, Interview Schedule, Feedback, Offer, and Source. Ashby models the funnel with explicit stage-transition history rather than only current state, which means cohort analysis is available from the data instead of requiring reconstruction.
Ashby is not currently on UbiVibe's verified connector list. This page is an implementation design reference: use it to specify the workflow, then validate whether the connection is available and correctly scoped for your workspace before you make it a dependency. The verified UbiVibe connections today are Salesforce, HubSpot, Gmail, Google Drive, Slack, and GitHub.
The platform layer is the usual destination for this connection, because the value shows up as governed context and execution shared across more than one team.
Why teams evaluate this connection
Integrations create value when they remove operating friction.
The first design decision is not which API endpoint to call; it is which system owns the record, what event should trigger work, who owns the exception path, and what successful completion means.
HR systems hold the most sensitive data in the company and are usually the least connected, for good reason. Anything reading Ashby is reading personal data, and the default answer to a casual integration request should be no.
That caution has a cost, though. People-operations work is full of repetitive coordination — onboarding checklists, access requests, candidate scheduling, document collection — that stays manual because the systems that could automate it are the ones nobody wants to connect broadly.
You're likely here because
- Onboarding and offboarding run on a manual checklist
- Candidate or employee coordination consumes disproportionate time
- People data cannot be connected without a privacy review nobody has scheduled
Record model
What a Ashby integration actually reads and writes.
Integration design starts from the objects the system really exposes, not from a generic connector diagram. These are Ashby's.
Identity and matching
Ashby models the funnel with explicit stage-transition history rather than only current state, which means cohort analysis is available from the data instead of requiring reconstruction.
Start here
Read the stage transitions for one Job and report where candidates actually drop, rather than where the team believes they do — Ashby records the transitions, so this needs no reconstruction.
What this will not do
It will not fix sourcing. Better funnel visibility shows where in the process candidates are lost; filling the top of the funnel is separate work with separate constraints.
The constraint to plan around
The API is GraphQL-shaped with per-object permissions, and interview feedback is restricted by design. Access to the funnel does not imply access to what interviewers wrote.
Build notes
What you actually have to reason about in Ashby.
The fields that carry meaning, how the connection authenticates, and whether the event surface can be trusted. This is the part that decides whether the integration works in month three.
| Field | Why it matters |
|---|---|
| stage transition history | recorded explicitly, so cohort analysis needs no reconstruction |
| application status | the current funnel position, alongside the transition history that explains how it got there |
| interview schedule state | scheduled versus completed, which is where scheduling delays surface |
| feedback visibility | restricted by design, so funnel access does not imply reading what interviewers wrote |
| source | channel attribution captured at application |
Authentication
API key with granular per-endpoint permissions, org-scoped. Interview feedback is separately gated, which is appropriate and surprises integrations that assume full read.
Events and delivery
Webhooks cover application and interview events with signature verification. Given the explicit transition history, batch analysis is also well supported.
Workflow
How the Ashby workflow runs.
The operating sequence, from reading the source system through to the result landing back where it belongs.
Step 01
Complete the privacy review
The data-protection assessment happens before the connection exists, and defines what is in and out of scope.
Step 02
Connect at the narrowest scope
Only the Ashby fields the named workflow requires are connected, with sensitive categories excluded by explicit decision.
Step 03
Coordinate, do not decide
The workflow tracks tasks, sends reminders, and schedules, while every decision affecting an individual stays with a person.
Step 04
Surface status without exposing detail
Progress is visible to the people who need it without opening the underlying personal records more widely than the workflow requires.
Design decisions
The HR decisions this connection forces.
Each of these has to be settled before the Ashby workflow is allowed to write anything.
Step 01
Scope to one workflow, not to the system
Connect Ashby for a specific workflow with the narrowest access that supports it. Employee data should never be connected broadly because it might be useful later.
Step 02
Exclude sensitive categories deliberately
Compensation, health, performance, and disciplinary data should be excluded by explicit decision rather than by assuming a scope will not reach them.
Implementation path
How to implement the Ashby workflow.
- 01
Complete the privacy and data-protection review before connecting anything; this is a prerequisite, not a follow-up task.
- 02
Pick one workflow — coordination, scheduling, or status — and scope the Ashby connection to exactly what it needs.
- 03
Document which data categories are explicitly excluded and confirm the scope cannot reach them.
- 04
Once drop-off analysis works, add interviewer-calibration reporting, which the structured feedback model supports and most ATSs cannot.
Governance
Controls that matter.
Control 01
Employment decisions stay with people. Automation coordinates and prepares; it does not evaluate or decide.
Control 02
Access is scoped to a named workflow, reviewed on a schedule, and revoked when the workflow ends.
Control 03
Compensation, health, performance, and disciplinary data are excluded unless there is a specific, reviewed reason to include them.
Failure modes
How a Ashby integration breaks in production.
Not generic integration advice. These follow from how this system actually behaves, which is why they look nothing like the list on the next guide over.
Symptom 01
Interview feedback is unavailable to the integration.
Cause
Feedback is separately gated by design.
Fix
Design around the funnel data, and request feedback access only with an explicit reason.
Symptom 02
Transition history has gaps after a bulk operation.
Cause
A write corrupted the recorded transitions the analysis depends on.
Fix
Avoid bulk stage writes; the transition history is the asset here.
Symptom 03
A GraphQL query returns partial data.
Cause
Pagination was not followed.
Fix
Follow pagination on every connection.
What changes at scale
Volume scales with hiring activity. The explicit transition history makes batch analysis cheap, which is unusual for an ATS and worth using.
Examples
What a working Ashby workflow looks like.
Bounded scenarios rather than a feature list. Each one can be verified against work the team already does.
Recruiting operations
With Ashby connected at a narrow scope, a coordination workflow can run from real records instead of a manually-maintained checklist.
Onboarding coordination
Tasks, access requests, and document collection are tracked as records with owners and status, so the checklist is not held in one person's head.
Limitations and considerations
What to validate before you depend on this.
- The API is GraphQL-shaped with per-object permissions, and interview feedback is restricted by design. Access to the funnel does not imply access to what interviewers wrote.
- As with any ATS, writes affect candidates. Ashby's richer data model also means a wrong write corrupts the transition history that made the analysis possible.
- When the constraint is candidate supply. Funnel visibility shows where candidates are lost; filling the top is separate work.
- Ashby holds personal data. Connecting it requires a privacy and data-protection assessment before, not after, implementation.
- Employment decisions are legally regulated in most jurisdictions. Automated assistance must keep the human decision explicit and documented.
FAQ
Ashby integration questions.
What records does a Ashby integration actually work with?
The primary records are Application, Candidate, Job, Interview Schedule, Feedback, Offer, and Source. Ashby models the funnel with explicit stage-transition history rather than only current state, which means cohort analysis is available from the data instead of requiring reconstruction.
What should the first Ashby workflow be?
Read the stage transitions for one Job and report where candidates actually drop, rather than where the team believes they do — Ashby records the transitions, so this needs no reconstruction.
What will a Ashby integration not do?
It will not fix sourcing. Better funnel visibility shows where in the process candidates are lost; filling the top of the funnel is separate work with separate constraints.
What is the main constraint to plan around?
The API is GraphQL-shaped with per-object permissions, and interview feedback is restricted by design. Access to the funnel does not imply access to what interviewers wrote.
What changes about a Ashby integration at scale?
Volume scales with hiring activity. The explicit transition history makes batch analysis cheap, which is unusual for an ATS and worth using.
How does authentication work for Ashby?
API key with granular per-endpoint permissions, org-scoped. Interview feedback is separately gated, which is appropriate and surprises integrations that assume full read.
Does Ashby support webhooks, and can they be trusted?
Webhooks cover application and interview events with signature verification. Given the explicit transition history, batch analysis is also well supported.
What is the risk of writing to Ashby?
As with any ATS, writes affect candidates. Ashby's richer data model also means a wrong write corrupts the transition history that made the analysis possible.
When is connecting Ashby the wrong call?
When the constraint is candidate supply. Funnel visibility shows where candidates are lost; filling the top is separate work.
What should a Ashby integration automate first?
Start with one bounded workflow that removes a measurable handoff, duplicate-entry step, reporting delay, or follow-up gap. Expand only after the first workflow is reliable.
Does UbiGrowth require Ashby to be replaced?
No. The operating model is designed around connecting to systems that should remain authoritative and building workflows around them rather than forcing a wholesale replacement.
Is connector availability identical for every workspace?
No. Availability can depend on provider configuration, authentication, scopes, workspace setup, and deployment state. Validate the required connection before treating it as an operational dependency.
Is it safe to connect Ashby?
Only at a narrow, reviewed scope for a specific workflow, after your own privacy assessment. Employee data should not be connected broadly on the chance it becomes useful.
Can this screen or evaluate people?
No. Employment decisions stay with people. These workflows coordinate, schedule, and track — they do not evaluate individuals.
What data should be excluded?
Compensation, health, performance, and disciplinary data unless there is a specific, reviewed reason to include it. Exclusion should be an explicit decision.
How this access is governed
What ARIA is allowed to do in Ashby, and who decides.
Connecting Ashby is a permission decision, not just a setup step. These are the controls that decide what ARIA can reach, what it can change, what gets recorded, and how you take the access back.
Required permissions
ARIA works through the scopes the connection was granted, and no others. Authorization happens at the provider, so the permissions being requested are shown by the system itself before anything is connected.
What it can reach
Reachable systems are the intersection of what your organization approved in the connector registry and what the requesting identity is permitted to use. Identity resolves before execution, not after.
What it can do
Actions run through explicit execution paths with state, spend, and failure boundaries — a bounded worker path rather than an open-ended agent loop with a credential.
Credential handling
Credentials live in the governed connection layer and are resolved through canonical connection identity. They are not pasted into individual workflows, prompts, or generated artifacts.
Action logging
Execution carries state and traces: what triggered the work, which connection it used, and what came back — including an explicit failure when something did not run.
Approval and revocation
Consequential actions can be made to require a person to approve them. Access can be changed or revoked at the connection, and ARIA loses that reach without unpicking the work already completed.
Start with ARIA
Ask ARIA to run this integration.
Describe the outcome you need across this system. ARIA works out the scopes, data, and actions the job requires, and operates inside the access you grant — which you can change or revoke.
- ARIA acts only through the systems and permissions you connect.
- Connections use scoped credentials you can change or revoke.
- Actions are recorded, and consequential ones can require approval.
Start here
Turn the integration into a working business outcome.
Start with ARIA to describe the outcome, then continue into the product path that fits the workflow. Connector availability and required scopes should be validated for the specific workspace before production use.