HRMS migration
Field-level mapping, validation, and rollback between Kula and Crelate. We move data and schema; workflows are rebuilt natively in Crelate.
Kula
Source
Crelate
Destination
Compatibility
10 of 12
objects map 1:1 between Kula and Crelate.
Complexity
BStandard
Timeline
4-8 weeks
Overview
Moving from Kula to Crelate is an ATS-native migration with specific object-level and activity-level considerations. Kula organizes recruiting data around Candidates, Jobs, Applications, Interviews, and Scorecards, while Crelate uses People, Jobs, Applications, and Tasks/Activities. We map Kula's active requisitions and stage history into Crelate's pipeline configuration, preserve candidate activity timelines, and flag which custom fields and tags exist in the source instance for destination mapping. Kula's in-house AI resume scores and interview summaries import as read-only text fields; we do not carry over live AI metrics. Workflow builders, email templates, and automated sequences do not migrate as code; we deliver a written inventory for the customer's admin to rebuild in Crelate.
Every standard and custom field arrives verified.
AI proposes the map; you confirm before any record moves.
Parent–child, lookups, and ownership stay linked.
Calls, emails, meetings — with original timestamps.
Documents, uploads, and inline notes move with the record.
Why teams make this switch
Leaving
What's pushing teams away
Choosing
What's pulling them in
Object mapping
Each row shows how a Kula object lands in Crelate, including any object-level transformations, lookup resolution, or schema-design dependencies.
Typical mapping — final map is confirmed during the sample migration step.
Kula
Candidate
Crelate
Person
1:1Kula Candidate records map directly to Crelate Person records. The Candidate's contact information, work history, sourced profile data, and AI-generated scores migrate as Person fields. We run deduplication by email before inserting, flagging duplicate Person records for admin review. The full candidate activity timeline migrates as Tasks and Activities linked to the Person record.
Kula
Job (Requisition)
Crelate
Job
1:1Kula Job records map to Crelate Job records with pipeline stages and active versus closed status preserved. Stage names and ordering extract from the source instance and are recreated as Crelate pipeline configurations before Job records are imported. We validate stage count against Crelate's Business plan limit of 20 custom recruiting workflows and flag any excess stages requiring consolidation.
Kula
Application
Crelate
Application
1:1Kula Application records link a Candidate to a Job and track stage progression, source attribution, and submission date. These map to Crelate Application records with current stage, rejection or offer outcome, and submission timestamp preserved. We resolve the parent Person and Job references at migration time to satisfy Crelate's required lookups.
Kula
Interview
Crelate
Task/Activity
1:1Kula Interview records store scheduled rounds, interviewer assignments, and reviewer notes. We map these to Crelate Tasks and Activities with the interviewer assignment, scheduled date/time, and reviewer feedback preserved. The interview sequence (round ordering) is maintained via the Activity date ordering on the parent Person and Job Application records.
Kula
Scorecard and AI Summary
Crelate
Task Notes and Custom Fields
1:1Kula generates AI-powered interview summaries and candidate scores as structured fields on the interview record. These import into Crelate as read-only text fields on the Task record and custom fields on the Person record. We preserve the original scores as reference data but flag that live re-scoring requires running Crelate's AI Co-Pilot or a fresh manual evaluation on the destination platform.
Kula
Pipeline Stage
Crelate
Pipeline Stage Configuration
lossyKula's customizable pipeline stages per job extract as stage names, ordering, and probability values. We recreate these as Crelate pipeline stage configurations, mapping stage probability percentages to Crelate's stage probability fields. Non-standard stage types (assessment, background check, offer) may require custom field creation in Crelate to preserve all stage metadata.
Kula
Custom Field
Crelate
Custom Field
1:1Both Kula and Crelate support custom fields on Candidates, Jobs, and Applications. We extract all custom field definitions and values from Kula, then create matching custom fields in Crelate using the Crelate field API before record import begins. Field data types are mapped (text to text, number to number, date to date) and validated during the sandbox migration phase.
Kula
Tag and Source Attribution
Crelate
Tag or Custom Property
1:1Tags applied to candidates in Kula (e.g., referral, sourced-linkedin) carry sourcing context and are preserved as Crelate tags or custom text properties depending on the tag's purpose. Tag limits on Crelate's Business plan (tag count varies by plan) are validated during scoping, and any overflow tags are mapped to custom multi-select fields.
Kula
Owner and Team Assignment
Crelate
User Assignment
1:1Recruiter owners, hiring managers, and interviewers assigned to Jobs and Applications in Kula are mapped by email to Crelate User records. We run email-based matching against Crelate's User table before migration and flag any Kula owners without a matching Crelate User for admin provisioning. Unresolved assignments are held in a reconciliation queue until the admin completes User provisioning.
Kula
Email and SMS Template
Crelate
Email Template (Documented)
lossyKula outreach templates used in automated candidate communication are documented as a text export with field mapping notes. Rich formatting and conditional logic from Kula templates may require manual reconstruction in Crelate's template builder. We deliver a template inventory document that lists each Kula template, its merge fields, and the recommended Crelate equivalent for the customer's admin to rebuild.
Kula
Interviewer Pool
Crelate
User Pool Configuration
1:1Kula's interviewer pool feature balances scheduling load across team members. Pool membership records migrate as Crelate User assignments to the Job, but scheduling rules and availability settings are destination-dependent and do not carry over. We document the original pool structure so the admin can recreate interviewer availability settings in Crelate's scheduling tool post-migration.
Kula
Engagement Activity
Crelate
Task and Activity
1:1Kula's candidate activity timeline (calls, emails, meetings, notes) maps to Crelate Tasks and Activities linked to the Person record. Call duration, email content, meeting attendee lists, and note body text migrate directly. Activity timestamps preserve ordering so the candidate's engagement history is complete in Crelate's activity feed. Large engagement histories use Crelate's API with batch chunking and rate-limit handling.
| Kula | Crelate | Compatibility | |
|---|---|---|---|
| Candidate | Person1:1 | Fully supported | |
| Job (Requisition) | Job1:1 | Fully supported | |
| Application | Application1:1 | Fully supported | |
| Interview | Task/Activity1:1 | Fully supported | |
| Scorecard and AI Summary | Task Notes and Custom Fields1:1 | Fully supported | |
| Pipeline Stage | Pipeline Stage Configurationlossy | Fully supported | |
| Custom Field | Custom Field1:1 | Fully supported | |
| Tag and Source Attribution | Tag or Custom Property1:1 | Fully supported | |
| Owner and Team Assignment | User Assignment1:1 | Fully supported | |
| Email and SMS Template | Email Template (Documented)lossy | Fully supported | |
| Interviewer Pool | User Pool Configuration1:1 | Fully supported | |
| Engagement Activity | Task and Activity1:1 | Fully supported |
Gotchas + challenges
Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.
Kula gotchas
AI-generated scores do not carry over as live metrics
Reporting exports require a separate manual step
Frequent platform updates can change field behavior
Crelate gotchas
120 req/min API rate limit throttles bulk migrations
20 custom field per-entity cap forces data model decisions
15,000-record export ceiling on single operations
Sequences and automation workflows do not migrate
API key is a querystring parameter, not a header
Pair-specific challenges
Migration approach
Discovery and scoping
We audit the source Kula instance across record volume (Candidates, Jobs, Applications, Interviews), custom field definitions, pipeline stage configurations, AI score fields, tag inventory, and active user count. We map the output to Crelate's People, Job, Application, and Activity objects and deliver a written migration scope that includes a data inventory, a field mapping table, and a Crelate plan recommendation (Business at $119/user with 5-seat minimum, Business Plus for AI Co-Pilot and Agents, or Enterprise for custom pricing).
Schema design and pipeline configuration
We design the destination schema in Crelate. This includes creating custom fields on Person, Job, and Application to match Kula's custom field definitions, recreating Kula pipeline stages as Crelate pipeline configurations (with stage name, ordering, and probability), and mapping Kula's AI score fields to Crelate custom text fields. Schema creation happens in Crelate's sandbox or test environment first for validation before any production data moves.
Sandbox migration and reconciliation
We run a full migration into a Crelate test environment using production-like data volume. The customer's recruiting operations lead reconciles record counts (People in, Jobs in, Applications in, Interviews in, Activities in), spot-checks 25-50 random records against the Kula source, and validates pipeline stage ordering. Any mapping corrections are documented and applied before the production migration begins.
Owner reconciliation and User provisioning
We extract every distinct Kula Owner referenced on Candidate, Job, Application, and Interview records and match by email against Crelate's User table. Owners without a matching Crelate User go to a reconciliation queue. The customer's Crelate admin provisions any missing Users (active or inactive depending on whether the original Kula user is still active). Migration cannot proceed past this step because interviewer and recruiter assignments are required on most Crelate records.
Production migration in dependency order
We run production migration in record-dependency order: People (with email deduplication and AI score fields mapped to text), Jobs (with pipeline stage configuration resolved), Applications (with Person and Job lookups resolved), Interviews and Scorecards (mapped to Tasks and Activities), Engagement history (calls, emails, meetings, notes as Tasks and Activities via API with batch chunking), Custom fields (mapped last, after parent records are confirmed). Each phase emits a row-count reconciliation report before the next phase begins.
Cutover, validation, and automation inventory handoff
We freeze Kula writes during cutover, run a final delta migration of any records modified during the migration window, then enable Crelate as the system of record. We deliver the email template inventory and automation notes document to the customer's admin team for rebuild in Crelate. We support a one-week hypercare window where we resolve any reconciliation issues raised by the recruiting team. We do not provide ongoing admin support, training, or workflow rebuild as standard scope; these are separate engagements.
Platform deep dives
Kula
Source
Strengths
Weaknesses
Crelate
Destination
Strengths
Weaknesses
Complexity grading
Standard HRMS migration. 1 of 7 objects need a mapping; the rest are 1:1.
Overall complexity
Standard migration
Derived from compatibility, mapping clarity, API constraints, and data volume across Kula and Crelate.
Object compatibility
1 of 7 objects need a mapping; the rest are 1:1.
Field mapping clarity
Field mapping is derived from defaults — final spec confirmed during the sample migration.
Timeline complexity
7-object category — typical timelines run 2–7 days end-to-end.
API constraints
Kula: Not publicly documented.
Data volume sensitivity
Kula exposes a bulk API — large-volume migrations stream efficiently.
Estimator
Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.
Step 1
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FAQ
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