HRMS migration

Migrate from Kula to Crelate

Field-level mapping, validation, and rollback between Kula and Crelate. We move data and schema; workflows are rebuilt natively in Crelate.

Kula logo

Kula

Source

Crelate

Destination

Crelate logo

Compatibility

83%

10 of 12

objects map 1:1 between Kula and Crelate.

Complexity

BStandard

Timeline

4-8 weeks

Rollback included Accuracy guarantee Field-level validation

Overview

What this migration involves

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.

Field-level fidelity

Every standard and custom field arrives verified.

Schema-aware mapping

AI proposes the map; you confirm before any record moves.

Relationships preserved

Parent–child, lookups, and ownership stay linked.

Full activity history

Calls, emails, meetings — with original timestamps.

Attachments & notes

Documents, uploads, and inline notes move with the record.

Why teams make this switch

Two sides of the same decision

Leaving

Kula logo

Kula

What's pushing teams away

  • Reporting is consistently described as the weakest feature — dashboards lack depth, customization options are limited, and historical analytics require manual exports to fill gaps.
  • The platform attempts to cover too many recruiting scenarios at once, adding workflow complexity that teams with simple hiring processes find unnecessary.
  • As a newer ATS, Kula ships frequent updates that occasionally introduce bugs, slow screen loads, or sync issues between features that require workarounds.

Choosing

Crelate logo

Crelate

What's pulling them in

  • Affordable per-seat pricing with transparent tiers makes Crelate accessible for small-to-mid staffing firms evaluating ATS platforms for the first time.
  • Fast implementation reported by customers—some describe getting live in a matter of minutes with support team assistance.
  • Unified ATS + CRM in a single product eliminates the need to buy and synchronize separate recruiting and sales tools.
  • Flexible custom fields across Contacts, Companies, and Opportunities allow recruiting teams to capture firm-specific data without developer involvement.
  • Positive reviews highlight the product's intuitive interface and functional breadth for teams that need recruiting workflows without enterprise overhead.

Object mapping

How Kula objects map to Crelate

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

maps to

Crelate

Person

1:1
Fully supported

Kula 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)

maps to

Crelate

Job

1:1
Fully supported

Kula 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

maps to

Crelate

Application

1:1
Fully supported

Kula 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

maps to

Crelate

Task/Activity

1:1
Fully supported

Kula 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

maps to

Crelate

Task Notes and Custom Fields

1:1
Fully supported

Kula 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

maps to

Crelate

Pipeline Stage Configuration

lossy
Fully supported

Kula'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

maps to

Crelate

Custom Field

1:1
Fully supported

Both 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

maps to

Crelate

Tag or Custom Property

1:1
Fully supported

Tags 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

maps to

Crelate

User Assignment

1:1
Fully supported

Recruiter 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

maps to

Crelate

Email Template (Documented)

lossy
Fully supported

Kula 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

maps to

Crelate

User Pool Configuration

1:1
Fully supported

Kula'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

maps to

Crelate

Task and Activity

1:1
Fully supported

Kula'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.

Gotchas + challenges

What specifically takes care here

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 logo

Kula gotchas

Medium

AI-generated scores do not carry over as live metrics

Medium

Reporting exports require a separate manual step

Low

Frequent platform updates can change field behavior

Crelate logo

Crelate gotchas

High

120 req/min API rate limit throttles bulk migrations

High

20 custom field per-entity cap forces data model decisions

Medium

15,000-record export ceiling on single operations

Medium

Sequences and automation workflows do not migrate

Low

API key is a querystring parameter, not a header

Pair-specific challenges

  • Kula AI-generated scores do not transfer as live metrics

    Kula computes resume scores and interview summaries using its in-house AI models. These scores import into Crelate as static text fields rather than live metrics. We flag every AI-score field during the mapping phase, preserve the original scores as read-only reference data, and advise customers to plan for a fresh AI pass on Crelate (using Crelate's AI Co-Pilot and Agents on Business Plus or Enterprise plans) or manual re-scoring after cutover.

  • Reporting exports require a separate manual step

    Kula's native reporting exports limited historical analytics data. We pull what is available via the API, but aggregate reporting data (pipeline velocity trends, recruiter activity heat maps) must be manually exported from Kula before migration. We include a reporting export checklist in our pre-migration checklist so customers capture these numbers before the cutover date. Failure to export reporting data before the migration window results in permanent loss of historical analytics.

  • Crelate interface requires onboarding investment

    Multiple reviews note that Crelate's interface is dated and less user-friendly compared to newer ATS competitors. Teams migrating from Kula's clean, modern UI should plan for an onboarding period where recruiters learn Crelate's navigation patterns. We do not provide post-migration training as standard scope, but we do deliver a configuration guide that covers the migrated data structure and Crelate's equivalent feature locations.

  • Auto-tagging in Kula creates tag cleanup work

    Kula's auto-tagging feature can generate junk tags over time (parsed work and education fields, duplicate source attributions). We recommend disabling auto-tagging before migration to prevent importing unnecessary tags into Crelate. During scoping, we audit the tag inventory and flag high-volume auto-generated tags for exclusion. Any tag cleanup in Crelate post-migration is an admin task documented in our handoff guide.

  • Frequent Kula updates can change field behavior

    Kula ships updates regularly, which occasionally rename or re-categorize fields. We run a schema validation pass within 48 hours of migration to catch any field discrepancies introduced by recent platform updates. If a field has been renamed or deprecated, we update the mapping and re-run validation before confirming the migration is complete.

Migration approach

Six steps for a successful Kula to Crelate data migration

  1. 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).

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

Context on both ends of the pair

Kula logo

Kula

Source

Strengths

  • Built-in AI for resume scoring, interview summarization, and candidate notetaking without third-party LLM dependencies.
  • Active sourcing across LinkedIn and GitHub integrated directly into the candidate discovery workflow.
  • Clean, modern UI that hiring managers with no recruiting-tool background can navigate without training.
  • Automated interview scheduling aligned with interviewer availability and workload balancing.
  • Structured migration program with a dedicated implementation manager and a 4–6 week migration timeline.

Weaknesses

  • Reporting and analytics lag behind competitors — dashboards lack depth, customization is limited, and historical reporting requires manual workarounds.
  • Frequent feature updates occasionally introduce bugs, slow screen loads, or sync inconsistencies between modules.
  • Attempting to cover many recruiting scenarios adds workflow complexity that smaller teams with simple hiring needs may find excessive.
  • As a younger product, Kula lacks the long track record and ecosystem depth of established ATS platforms like Greenhouse or Lever.
Crelate logo

Crelate

Destination

Strengths

  • Unified ATS and CRM in a single platform reduces data synchronization overhead for recruiting teams.
  • Fast setup with guided implementation reported as a significant time saver for small teams.
  • Transparent per-seat pricing without surprise fees at the base tier.
  • Flexible custom field configuration across core objects without developer dependency.
  • Export capability supports up to 15,000 records per operation for Contacts, Companies, and Opportunities.

Weaknesses

  • API rate limit of 120 requests per minute restricts bulk migration throughput.
  • Custom field cap of 20 per entity requires field consolidation for complex recruiting schemas.
  • All advanced features (Activities, Activity Forms, Core Record Field customization) are tier-gated add-ons.
  • Customer service responsiveness receives consistent negative feedback in reviews.
  • Resume parsing quality trails competitors and generates support requests.

Complexity grading

How hard is this migration?

Standard HRMS migration. 1 of 7 objects need a mapping; the rest are 1:1.

B

Overall complexity

Standard migration

Derived from compatibility, mapping clarity, API constraints, and data volume across Kula and Crelate.

  • Object compatibility

    B

    1 of 7 objects need a mapping; the rest are 1:1.

  • Field mapping clarity

    C

    Field mapping is derived from defaults — final spec confirmed during the sample migration.

  • Timeline complexity

    B

    7-object category — typical timelines run 2–7 days end-to-end.

  • API constraints

    B

    Kula: Not publicly documented.

  • Data volume sensitivity

    A

    Kula exposes a bulk API — large-volume migrations stream efficiently.

Estimator

Estimate your Kula to Crelate migration cost

Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.

Step 1

What are you migrating?

Pick a category, then your source and destination platforms.

Category

FAQ

Frequently asked questions about Kula to Crelate data migrations

Answers to the questions buyers ask most during Kula to Crelate migration scoping. Not seeing yours? Book a call.

Can't find your answer?

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Most migrations land between four and eight weeks for accounts under 3,000 Candidates and 150 Jobs with a clean custom field set and no AI score history. Migrations with complex custom fields, large AI score histories, auto-tag cleanup, or multi-stage pipeline structures move to ten to sixteen weeks because of deduplication logic, pipeline recreation validation, and Crelate API rate-limit handling at scale.

Adjacent paths

Related migrations to explore

Ready when you are

Move from Kula.
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