GITNUXSOFTWARE ADVICE
Employment CareerTop 10 Best Cv Search Software of 2026
Ranked roundup of cv search software for recruiters, comparing SeekOut, LinkedIn Recruiter, Workable, and AmazingHiring with selection criteria and tradeoffs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
LinkedIn Recruiter is the best pick for teams that source from LinkedIn and need quick CV and profile search for repeated shortlist building, whereas Workable fits when you want CV search tightly connected to an ATS workflow and candidate rediscovery.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
LinkedIn Recruiter
Talent pool management paired with saved searches supports systematic candidate rediscovery across hiring rounds.
Built for fits when recruiters rely on LinkedIn-sourced signals for repeated sourcing and quick shortlist building..
Workable
Editor pickCandidate profile extraction powers consistent searching across previously ingested CVs inside Workable’s recruiting workflow.
Built for fits when recruiting teams want CV search tightly connected to their ATS workflow and candidate rediscovery..
AmazingHiring
Editor pickNormalized candidate profiles let recruiters search across extracted attributes and document content together.
Built for fits when recruiters need a fast CV search layer over an existing talent pool..
Comparison Table
LinkedIn Recruiter
enterpriseRecruiting software with searchable professional profiles, candidate filters, and outreach workflows.
Talent pool management paired with saved searches supports systematic candidate rediscovery across hiring rounds.
LinkedIn Recruiter is built around searching candidate profiles already indexed on LinkedIn, with filters that narrow by current role, past experience, and education signals. Search behavior supports keyword matching plus boolean logic in query strings, and it returns ranked results with recruiter-facing attributes for quick triage. Talent pool workflows let recruiters maintain lists for later outreach and re-searching, which fits ongoing hiring cycles.
A key tradeoff is that the search corpus is LinkedIn profile data, so resume parsing quality for non-profile documents does not drive matching relevance. The best fit is repeat hiring for the same function where saved queries, consistent filter usage, and talent pool rediscovery matter more than full document-level analysis.
- +Boolean search with recruiter-friendly filters for fast narrowing
- +Saved searches and talent pool lists for candidate rediscovery
- +Ranked results with rich role and experience context for triage
- +Workflow stays inside LinkedIn, reducing handoffs to external tools
- –Matching depends on profile completeness rather than document parsing
- –Advanced automation and API control are limited compared with recruiting data products
In-house recruiters
Recurring searches for target job families
Shortlists built faster each cycle
Agency recruiters
Client-specific sourcing with reusable filters
Lower query churn
Show 2 more scenarios
Talent operations teams
Centralized sourcing workflow for multiple recruiters
More consistent candidate review
Shared recruiter workflows within LinkedIn support consistent triage from ranked search results.
Hiring managers in recruiting
Spot-checking candidates by role signals
Faster screening approvals
Search results present current role and experience context for quick decision support.
Best for: Fits when recruiters rely on LinkedIn-sourced signals for repeated sourcing and quick shortlist building.
Workable
SMBHiring platform with resume search, candidate profiles, applicant tracking, and sourcing tools.
Candidate profile extraction powers consistent searching across previously ingested CVs inside Workable’s recruiting workflow.
Recruiters use Workable’s resume indexing and candidate profile extraction to run fast searches across previously ingested CVs, then apply filters to narrow down matching candidates. Workable also fits teams that manage talent pools inside a recruiting system because candidate records stay connected to job context and outreach workflows. The strongest fit appears when search is used as an ongoing rediscovery workflow rather than a one-time scan of a single file set.
A key tradeoff is that Workable’s search experience is strongest within Workable’s own candidate database and workflows. Teams that need deep extraction into custom schemas, or that rely on external talent graph models, often find the setup effort higher than expected. Workable works best when the primary source of truth for candidate records is already the Workable ATS.
- +Search results stay connected to jobs and candidate records
- +Resume parsing feeds a usable candidate profile for quick filtering
- +Talent pool rediscovery is practical for ongoing hiring pipelines
- +Workflow continuity reduces handoffs between search and outreach
- –Best search performance depends on clean ingestion into Workable
- –Customization for external schema-driven search is limited
- –Advanced query logic can feel constrained versus custom search stacks
- –Operational tuning is needed when candidate formats vary widely
Talent acquisition teams
Rediscovering prior applicants by role
Shortlists built faster
Recruiting coordinators
Triage of large inbound CV sets
Lower manual sorting time
Show 2 more scenarios
Hiring managers
Reviewing curated candidate pools
Clearer review handoffs
Use search-driven candidate lists that remain linked to job context and stages.
Recruiting operations
Standardizing search usage across roles
More consistent relevance
Apply consistent ingestion and search routines across recurring job profiles.
Best for: Fits when recruiting teams want CV search tightly connected to their ATS workflow and candidate rediscovery.
AmazingHiring
vertical specialistTechnical recruiting search software that aggregates developer profiles from public sources.
Normalized candidate profiles let recruiters search across extracted attributes and document content together.
AmazingHiring supports resume parsing and normalization so that candidates become searchable entities rather than a set of files. Search results can be refined with attribute filters that map to extracted CV fields, which helps recruiters narrow down quickly before reviewing content. It also supports bulk resume ingestion so talent pools can be created and revisited for candidate rediscovery based on profile consistency.
A key tradeoff is that results depend on the quality of resume extraction from PDFs and DOCX files, so edge-case formatting can reduce recall on skills or titles. It fits teams that already maintain a recruiting CRM or ATS separately and want an independent search layer for talent pool search and faster shortlist iteration.
- +Bulk resume ingestion supports building talent pools for rediscovery
- +Extracted fields enable attribute filters on normalized candidate records
- +Search results return candidate-centric profiles instead of file-only hits
- +Recruiter workflow fits quick shortlist review and re-querying
- –Extraction quality drops on poorly formatted CVs
- –Advanced workflow automation options are limited without custom integration
- –Fuzzy matching behavior can be less predictable on nonstandard titles
- –Deep ATS-style governance controls are not a primary focus
Recruiting teams
Shortlist candidates from a large pool
Faster shortlist generation
Talent acquisition ops
Reuse prior resumes for new roles
Reduced sourcing cycle time
Show 2 more scenarios
Sourcers and recruiters
Investigate niche skill combinations
Higher search precision
Combine keyword intent with attribute constraints to reduce time spent reading irrelevant CVs.
Small hiring teams
Centralize CV search without custom builds
Lower implementation effort
Ingest resumes and use a unified search workflow to avoid writing resume parsing code.
Best for: Fits when recruiters need a fast CV search layer over an existing talent pool.
SeekOut
enterpriseTalent search software with AI matching, sourcing filters, and recruiting intelligence.
Configurable skills taxonomy and profile enrichment that improve search relevance across rediscovered candidates.
SeekOut pairs resume parsing with a structured candidate profile database to support recruiter search workflows. It is geared toward Boolean and skills-driven discovery using indexed signals from uploaded resumes and imported candidate data.
Search results emphasize relevance ranking across extracted attributes, then reduce manual filtering through profile normalization. Admin control centers on configuring data access and search experience for recruiting teams without building custom parsing logic.
- +Resume parsing feeds a normalized, searchable candidate profile database
- +Boolean and attribute-based search supports structured recruiting workflows
- +Relevance ranking reduces time spent re-scoping broad keyword queries
- +Recruiter search outputs stay usable for talent pool candidate rediscovery
- –High-quality results depend on consistent resume text extraction quality
- –Complex skill taxonomy maintenance requires staff time and governance discipline
Best for: Fits when recruiting teams need fast Boolean and skills-based search over a normalized candidate database.
Zoho Recruit
SMBApplicant tracking software with resume parsing, candidate search, and recruitment automation.
Recruit’s search results stay linked to candidate records inside its pipeline workflow for stage updates and history retention.
Zoho Recruit performs candidate CV search over its structured candidate database so recruiters can filter, rank, and shortlist profiles faster than manual review. It supports resume parsing with normalization into candidate fields, which improves keyword matching and reduces the need to re-enter basics like skills, titles, and employment history.
Recruit also ties search results to candidate records used in its recruiting workflow so notes, tags, and stage movement stay attached to each profile. Zoho Recruit’s automation and integration surfaces center on Zoho ecosystem connectivity and configurable rules that govern how candidate data and statuses change after import and during outreach.
- +Candidate records keep search findings connected to pipeline stages
- +Resume parsing normalizes common fields for faster re-searching
- +Filters support recruiter workflow needs with saved views
- +Zoho ecosystem integrations reduce data duplication across tools
- –Search relevance tuning depends on consistent parsing and tagging
- –Boolean query depth is limited compared with specialist CV search engines
- –Bulk ingestion workflows need more governance to maintain data quality
- –Advanced ranking behaviors require configuration rather than out-of-box controls
Best for: Fits when teams manage an internal talent pool and want search tied to pipeline actions.
Manatal
SMBRecruiting software with AI candidate recommendations, resume parsing, and applicant search.
Manatal’s candidate profile enrichment turns parsed resume fields into searchable, reusable records for talent pool rediscovery.
Manatal is a CV search and recruiting CRM system aimed at recruiters who need to move from resume ingestion to targeted candidate search and reuse. It combines candidate parsing, a structured candidate database, and search workflows that support both keyword-based retrieval and semantic matching.
Admin controls cover user access and workflow configuration, and Manatal provides automation paths for outreach and pipeline movement. Integration depth is geared toward recruiters who want ATS and recruiting CRM connectivity plus a documented API for custom processes.
- +Semantic and keyword search improves relevance across varied resume wording
- +Candidate records stay structured after parsing, which speeds recurring searches
- +Recruiting workflow automation reduces manual handoffs between sourcing and pipeline
- +API and ATS style integrations support custom search and data sync flows
- –Search tuning can take time when synonyms and skills taxonomy are incomplete
- –Complex governance needs require disciplined workspace and permission management
- –Large bulk ingestion can create cleanup work for inconsistent resume formats
- –Advanced search logic may be harder to standardize across recruiters
Best for: Fits when recruiting teams need a reusable candidate database with search plus automated pipeline steps across multiple roles.
Crelate
vertical specialistRecruiting and staffing CRM with candidate search, resume parsing, applicant tracking, and reporting.
Structured candidate profile extraction that feeds directly into attribute filters and automated search workflows.
Crelate focuses on candidate search with structured profile extraction and fast retrieval across large CV collections. It supports query-time searching that blends keyword matching with additional relevance signals derived from parsed candidate fields.
The workflow is built for recruiter use, with filtering around extracted attributes and results that are easy to scan. Integration and automation surfaces exist through import and API access for maintaining an index over time.
- +CV parsing turns unstructured resumes into searchable profile fields
- +Search workflow supports attribute filters for tighter candidate shortlists
- +Index refresh supports candidate rediscovery without re-uploading everything
- +API access supports automation for bulk ingestion and search queries
- –Boolean and complex query tuning needs iterative setup for best precision
- –Custom taxonomy mapping can require governance discipline to stay consistent
Best for: Fits when recruiting teams need structured CV search that supports repeat rediscovery and API-driven workflows.
Textkernel
API-firstTalent intelligence software providing semantic resume search, matching, parsing, and job taxonomy tools.
Search relevance tuning with term normalization and synonym handling that improves consistency across resume wording variants.
Textkernel is a CV search solution built around search relevance for recruiting workflows. It turns unstructured resumes into indexable candidate data for keyword and semantic-style retrieval, with ranking that prioritizes query match.
The product focuses on search quality controls, including synonym and term normalization behavior, and it supports bulk resume ingestion for maintaining a structured candidate database. Administration centers on configuring search behavior and managing ingestion so results stay consistent across repeated searches.
- +Relevance-focused ranking tuned for recruiter search workflows
- +Bulk resume ingestion supports maintaining a structured candidate database
- +Synonym and normalization controls improve match consistency across variants
- +Extensibility options for integrating Textkernel search into broader recruiting stacks
- –Ongoing tuning of search behavior needs governance discipline
- –UI support for complex boolean queries can lag behind power-user expectations
- –Document ingestion requires attention to resume quality and format variance
- –Admin configuration depth can slow down initial setup for small teams
Best for: Fits when recruiters need high-relevance resume retrieval across large talent pools with ongoing search tuning.
Greenhouse
enterpriseApplicant tracking platform with searchable candidate profiles, structured hiring, and talent pools.
Role-based candidate access plus recruiter activity history stored alongside search and review actions.
Greenhouse can act as a CV and resume search system inside its recruiting suite, indexing candidate profiles for recruiter workflows. It supports structured candidate records built from CV parsing, then surfaces those candidates through configurable search and filters in the recruiting pipeline.
Greenhouse’s integration focus centers on applicant tracking system workflows, so search, talent review, and hiring stages share the same operational data. Governance controls like role-based permissions and activity auditing apply to candidate access and recruiter actions.
- +Candidate search uses the same structured profile data as its ATS workflows
- +Recruiter permissions and candidate access are governed with RBAC controls
- +Parsing feeds cleaner fields for filters and shortlist review
- +Activity tracking supports review history and recruiter accountability
- –Search configuration depends heavily on how fields are mapped and standardized
- –Bulk resume ingestion and format coverage can be limited by ATS ingestion paths
- –Advanced relevance tuning requires deeper admin work than basic keyword search
- –External semantic search workflows are not the primary focus
Best for: Fits when recruiters want candidate search tightly tied to ATS stages and governed access.
JobAdder
vertical specialistRecruitment software with searchable candidate databases, resume management, CRM, and applicant tracking.
Parsing-to-profile normalization that keeps imported CVs searchable long after ingestion.
JobAdder is a CV search solution built around a structured candidate database and a recruiter-led search workflow. It supports resume parsing with normalization into reusable candidate profiles, then enables keyword-based retrieval with relevance ranking for faster shortlists.
The system also focuses on importing and maintaining candidate records for rediscovery, using controls for who can search and act on profiles. JobAdder fits teams that want ATS-adjacent searching without manually reformatting every incoming CV.
- +Candidate profiles remain queryable after parsing, so past imports stay useful
- +Search results surface relevance ordering and fast filtering for shortlist building
- +Recruiter workflows reduce manual copy-paste by tying actions to candidate records
- +Import tooling supports bulk ingestion of resumes into the same candidate database
- –More complex matching needs careful configuration of skills and field mappings
- –Advanced automation and custom integrations can require developer support
Best for: Fits when recruiters need a maintained structured CV database and consistent search workflows across roles.
Conclusion
After evaluating 10 employment career, LinkedIn Recruiter stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right cv search software
Recruiters evaluating cv search software need tools that turn incoming resumes into a searchable candidate database and keep search results tied to an existing workflow. This buyer’s guide covers LinkedIn Recruiter, Workable, AmazingHiring, SeekOut, Zoho Recruit, Manatal, Crelate, Textkernel, Greenhouse, and JobAdder.
The biggest buying differences show up in how candidate profiles get extracted from CVs, how searches get tuned for precision and recall, and how far automation and API control extend beyond manual keyword queries. Each tool below is reviewed for integration depth, search relevance mechanics, and governance controls that affect recruiter access and reuse across hiring rounds.
CV search software for boolean, semantic, and profile-based candidate retrieval
CV search software indexes resumes and extracted candidate attributes into a structured search layer so recruiters can run boolean queries, attribute filters, and profile rediscovery across a talent pool. It typically pairs parsing and normalization with relevance ranking so results stay consistent as new documents get ingested.
LinkedIn Recruiter focuses on talent pool management with saved searches that support systematic candidate rediscovery using LinkedIn-sourced profile signals, with Boolean and recruiter filters for narrowing. Workable emphasizes candidate profile extraction inside its recruiting workflow so searches remain connected to jobs and candidate records after resume parsing.
CV search features that decide precision, relevance, and rediscovery
Recruiters get better shortlists when the product turns CVs into searchable candidate profiles and keeps those profiles attached to the workflow that owns decisions. Search relevance improves when resume parsing and normalization feed filtering and ranking, not when only raw documents get keyword-matched.
Candidate rediscovery matters because teams reuse the same talent pool across rounds and roles, so saved searches and persistent talent pool records reduce repeated sourcing.
Profile extraction that feeds filtering and reuse
Workable extracts candidate profiles so search results stay connected to jobs and candidate records. Crelate and JobAdder also normalize imported CVs into structured candidate profiles that remain queryable after ingestion.
Talent pool management for systematic candidate rediscovery
LinkedIn Recruiter combines saved searches with talent pool lists so recruiters can rediscover candidates across hiring rounds. AmazingHiring and Manatal build reusable talent pools via bulk resume ingestion plus structured records that support repeat searches.
Skills taxonomy and enrichment for relevance ranking
SeekOut uses a configurable skills taxonomy and profile enrichment to improve search relevance over rediscovered candidates. Textkernel focuses on relevance tuning through term normalization and synonym handling to stabilize retrieval across resume wording variants.
Search workflow depth inside the ATS or pipeline
Greenhouse and Zoho Recruit keep search tied to pipeline actions by connecting candidate search findings to ATS stages and recruiter activity history. Workable also keeps search connected to its recruiting workflow so parsed profiles map directly into candidate records.
Bulk resume ingestion that supports building a structured database
AmazingHiring and Textkernel support bulk resume ingestion so teams can maintain a structured candidate database for ongoing search. SeekOut and JobAdder depend on consistent resume text extraction quality because matching quality depends on what gets ingested into the searchable profile layer.
Gated access and recruiter governance controls
Greenhouse includes role-based candidate access plus recruiter activity history stored alongside search and review actions. LinkedIn Recruiter and Workable are easier to use for workflow-centric search, but their automation and API control are more limited than recruiting data products.
How to choose CV search software for boolean search, profile search, and rediscovery
Start with the search philosophy the team will operate day-to-day, then validate that the profile extraction and indexing path match that workflow. Teams that depend on repeated sourcing across rounds should prioritize tools that keep candidates in persistent talent pool objects tied to saved searches and rediscovery lists.
Pick the workflow center: talent pool lists or ATS stages
Choose LinkedIn Recruiter if the recruiting motion relies on talent pool management with saved searches that support systematic candidate rediscovery. Choose Greenhouse or Zoho Recruit if the recruiting motion relies on pipeline stages, because search results stay governed by RBAC and tied to stage updates and recruiter activity history.
Match the product to the quality of CV inputs
Choose Workable or JobAdder if resume parsing into usable candidate profiles is the main requirement, since both focus on keeping search results connected to candidate records after parsing. Avoid tools like SeekOut when CV formatting is inconsistent because high-quality results depend on consistent resume text extraction quality.
Decide whether relevance comes from enrichment or tuning
Choose SeekOut when a configurable skills taxonomy and profile enrichment are needed to improve search relevance across rediscovered candidates. Choose Textkernel when term normalization and synonym handling should stabilize retrieval across varied resume wording.
Choose between normalized attribute filtering and document-first search
Choose AmazingHiring, Crelate, or SeekOut when normalized candidate profiles must support attribute filters across extracted fields and document content together. Choose LinkedIn Recruiter when profile completeness and recruiter-friendly filtering over LinkedIn-sourced signals drive candidate narrowing more than document parsing.
Stress-test rediscovery across multiple roles and rounds
Choose LinkedIn Recruiter or Manatal when candidate rediscovery must work across multiple roles using reusable records and automated pipeline steps. If the team will run ongoing searches on an expanding internal database, validate bulk resume ingestion and continued queryability such as AmazingHiring and Textkernel.
Plan governance before search becomes a critical workflow
Choose Greenhouse when recruiter access must be governed with RBAC tied to candidate access and stored activity history. If governance discipline is expected, tools like SeekOut and Crelate require consistent taxonomy mapping and search tuning to keep results precise over time.
Who CV search software fits best
CV search software fits teams that manage a structured candidate database and need repeatable retrieval with search results tied to how recruiters make decisions. The strongest fit depends on whether the team operates from a saved talent pool workflow or from an ATS pipeline workflow where access controls and stage context must persist with search actions.
Recruiting teams running repeat sourcing and candidate rediscovery
LinkedIn Recruiter supports saved searches and talent pool lists that make rediscovery systematic across hiring rounds. AmazingHiring and Manatal also support reusable candidate records from bulk ingestion that speed repeated searches.
Companies that want search results anchored to ATS or pipeline actions
Greenhouse stores recruiter permissions and candidate access governed by RBAC and ties search to review actions and candidate activity history. Zoho Recruit keeps search outcomes connected to pipeline stages so stage updates stay linked to the underlying candidate record.
Teams standardizing search behavior with normalized candidate profiles
Crelate and Workable both focus on CV parsing into structured candidate profiles that power attribute filters and faster re-searching. JobAdder also keeps imported CVs searchable long after ingestion by maintaining queryable candidate profiles.
Recruiters who need relevance quality beyond keyword matching
SeekOut improves relevance with configurable skills taxonomy and profile enrichment across rediscovered candidates. Textkernel improves consistency with term normalization and synonym handling that reduces variance from resume wording.
Organizations managing access control and audit visibility for search users
Greenhouse provides role-based candidate access plus recruiter activity history stored alongside search and review actions. This governance model helps when multiple recruiters query the same talent pool under different permissions.
Common CV search buying mistakes
Most failure points come from mismatches between how resumes get parsed and how recruiters expect search results to behave. Another frequent issue is treating search tuning and governance as one-time setup instead of a recurring maintenance task when skills vocabularies and query patterns change.
Assuming results will work equally well on all resume formats
SeekOut depends on consistent resume text extraction quality, so poorly formatted CVs can reduce matching quality. AmazingHiring notes extraction quality drops on poorly formatted CVs, which impacts attribute filters on normalized candidate records.
Building workflows around complex boolean queries without governance ownership
Textkernel requires ongoing tuning of search behavior for stable precision and recall, which needs governance discipline. SeekOut and Crelate also need consistent taxonomy mapping or query maintenance to avoid drift in search behavior.
Ignoring access control when multiple recruiters share the same search surface
Greenhouse provides RBAC and stores recruiter activity history alongside search and review actions, which supports governed reuse across users. Without this level of control, teams risk candidates being visible to recruiters who should not access them.
Overestimating automation and API control relative to search workflow needs
LinkedIn Recruiter and Workable are strong for recruiter search workflow usage, but advanced automation and API control are limited compared with recruiting data products. JobAdder and Manatal emphasize parsing and enrichment, but advanced automation and custom integrations can require developer support.
How We Selected and Ranked These Tools
We evaluated LinkedIn Recruiter, Workable, AmazingHiring, SeekOut, Zoho Recruit, Manatal, Crelate, Textkernel, Greenhouse, and JobAdder by scoring features at 40 percent and then weighting ease and value at 30 percent each. Features scoring emphasized candidate profile extraction that produces structured, searchable records, plus talent pool and saved search support that supports candidate rediscovery.
Ease scoring emphasized how quickly recruiters can run boolean and attribute filters without getting blocked by field mapping and ingestion issues. Value scoring emphasized whether the search workflow stays connected to job or pipeline records, with LinkedIn Recruiter setting the pace through talent pool management paired with saved searches for systematic rediscovery across hiring rounds.
Frequently Asked Questions About cv search software
How do SeekOut and Textkernel differ in how resume content becomes searchable data?
Which tool is better for recruiter workflows that rely on repeated Boolean queries and saved sourcing?
How does Workable handle search once resumes are ingested into its recruiting workflow?
When teams need to search both extracted attributes and the original document content, where does the capability show up?
What breaks if a team needs search governed by RBAC and recruiter activity history, not just candidate filtering?
How do Manatal and Crelate support API-driven workflows for maintaining search indexes over time?
Which integration patterns matter most when search results must trigger pipeline actions in the same system?
How do Textkernel and SeekOut manage term variance like synonyms and wording differences?
When onboarding requires migrating an existing candidate database into a CV search system, what tends to differ?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
- Employment CareerTop 10 Best Cv Builder Software of 2026
- Employment WorkforceTop 10 Best Resume Search Software of 2026
- Employment CareerTop 10 Best Curriculum Vitae Maker Software of 2026
- Employment CareerTop 10 Best C Level Resume Writing Services of 2026
- Employment WorkforceTop 10 Best Candidate Screening Services of 2026
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