GITNUXSOFTWARE ADVICE
Employment CareerTop 10 Best Resume Upload Software of 2026
Top 10 Resume Upload Software ranked by parsing accuracy, formatting control, and ATS checks, with tools like Jobscan and Resume Worded.
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%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Text Blaze
Template variables with conditional logic enforce repeatable resume metadata schemas during upload workflows.
Built for fits when teams automate resume form steps with controlled schema mapping and API-driven triggers..
Resume Worded
Editor pickSchema-driven resume parsing that returns structured outputs suitable for automated pipelines.
Built for fits when HR ops teams need consistent resume parsing and automations..
Jobscan
Editor pickAPI-based resume to job-description matching with extracted keyword and skills gap reporting.
Built for fits when teams need API-driven resume uploads and consistent matching across many roles..
Related reading
Comparison Table
This comparison table groups resume upload tools by integration depth, data model, and the automation and API surface used to map inputs to scoring or parsing outputs. It also highlights admin and governance controls like RBAC, provisioning workflow, and audit log coverage, plus how extensibility and configuration affect throughput and repeatability across users.
Text Blaze
browser automationText Blaze provides a browser automation editor with variables, templates, and form-field insertion that can be used to generate and upload resume text into application forms.
Template variables with conditional logic enforce repeatable resume metadata schemas during upload workflows.
Text Blaze can turn a resume upload step into a governed workflow by binding template inputs to structured fields like candidate name, job family, and document tags. The integration depth shows up through its API and programmable automation hooks that can push captured metadata to downstream systems. Conditional logic and variable typing help keep output schema consistent across multiple upload paths. Extensibility comes from building snippet libraries that standardize how resumes are labeled and routed.
A key tradeoff is that Text Blaze does not provide a full document management system with retention policies, OCR extraction, and file lifecycle controls. Use it when resumes are already uploaded through a target application or form, and the goal is to map metadata and drive submission steps through controlled automation. Text Blaze also fits when throughput matters, since snippets execute client-side with minimal manual rework and can standardize large volumes of form interactions.
- +Parameterized snippets produce consistent resume metadata fields across workflows
- +API and automation hooks enable programmatic upload-step triggering
- +Conditional logic supports schema rules for role, region, and document tags
- +Snippet libraries improve extensibility and reduce copy-paste configuration
- –No built-in document lifecycle controls like retention and deletion policies
- –File content processing like OCR extraction requires external tooling
- –Governance depends on external access controls and template change practices
Recruiting operations teams
Automate resume form metadata entry
Fewer misrouted applications
HR automation engineers
Trigger upload flows via API
More consistent submissions
Show 2 more scenarios
Talent acquisition coordinators
Handle multi-site resume intake
Faster intake processing
Applies conditional template rules to label resumes based on site and job family routing logic.
Systems integrators
Map upload metadata to downstream tools
Higher integration throughput
Transforms structured variables into required payload formats for ATS and CRM workflows.
Best for: Fits when teams automate resume form steps with controlled schema mapping and API-driven triggers.
More related reading
Resume Worded
resume analysisResume Worded offers a resume upload workflow with structured feedback output that supports repeated iteration across job applications.
Schema-driven resume parsing that returns structured outputs suitable for automated pipelines.
Resume Worded is a strong fit for teams that treat uploaded resumes as data objects that must stay consistent across attempts and reviewers. The workflow supports schema-driven parsing so that extracted fields can drive scoring, feedback generation, and reporting. Resume Worded also supports extensibility points that matter for automation and integration layers that expect predictable data contracts.
A key tradeoff is that governance controls are more limited than in enterprise document management systems that offer deep RBAC granularity, dataset-level policy, and multi-tenant audit streams. Resume Worded fits situations where resume processing volume needs consistent configuration and evaluation outputs, while admin needs center on access to the workflow rather than complex provisioning.
- +Resume file ingestion turns text into structured, integration-ready output
- +Consistent parsing supports automation across repeated uploads
- +API-style extensibility supports downstream workflows and reporting
- +Configuration patterns fit controlled throughput for candidate batches
- –RBAC depth and tenant governance are weaker than document platforms
- –Audit-log and admin policy controls are less granular for complex orgs
- –Workflow customization can lag behind fully custom resume scoring engines
Recruiting operations teams
Automated resume screening batch processing
Lower manual review workload
HR analytics teams
Standardized resume fields for dashboards
More comparable analytics
Show 2 more scenarios
System integrators
Resume evaluation embedded in pipelines
Fewer custom mapping steps
API-style integration lets upstream forms and downstream actions share the same data model.
Talent acquisition coordinators
Controlled uploads with repeatable results
Faster throughput at scale
Configuration keeps evaluation outputs stable across large candidate streams.
Best for: Fits when HR ops teams need consistent resume parsing and automations.
Jobscan
matching workflowJobscan supports resume uploads and job-description matching output that helps drive consistent resume text across applications.
API-based resume to job-description matching with extracted keyword and skills gap reporting.
Jobscan’s core capability centers on uploading a resume and comparing it against a job description to produce actionable gaps in keywords and phrasing. The data model works well for schema-based comparisons because extracted resume elements map to job-description terms consistently. Integration depth is strongest when systems can push resume and job text into repeatable evaluation workflows.
A key tradeoff is that outcomes depend on the quality of the uploaded resume text and the specificity of the job description input. Organizations get the best results when they standardize input fields for automation and limit ad hoc formatting differences between documents.
Admin and governance controls are most useful when access needs to be separated across recruiters and candidates, because review activity and evaluation scope can be constrained by configuration and role-based access patterns.
- +Job and resume comparisons use keyword and skills extraction for repeatable gap outputs
- +API-oriented evaluation workflows support higher throughput than manual uploads
- +Normalization of resume text reduces variance from minor formatting differences
- –Results can degrade when resume text extraction is incomplete or noisy
- –Job-description specificity strongly affects match quality and gap accuracy
- –Advanced governance depends on external workflow design around roles and audit needs
Recruiting operations teams
Automate applicant resume screening
More consistent resume shortlists
Career coaches
Generate targeted resume revision feedback
Faster tailored revisions
Show 2 more scenarios
Job seekers at scale
Prepare resumes for multiple postings
Fewer mismatched applications
Repeat the upload and matching loop per job description to keep coverage aligned.
HR analytics groups
Measure match trends across roles
Better workforce alignment reporting
Aggregate structured evaluation outputs to track how resumes align with role-specific term sets.
Best for: Fits when teams need API-driven resume uploads and consistent matching across many roles.
Teal
career workflowTeal provides resume upload and job-application management features that track versions and reuse resume content across applications.
Schema-based resume templates that feed automated application workflows via API-configured steps
Teal targets resume and job application workflows with structured data and automation centered on document generation. Its integration depth shows up in configurable schema fields, repeatable templates, and workflow steps that connect resume assets to applications.
Teal emphasizes an automation and API surface for provisioning and extensibility across teams that manage multiple resumes and target roles. Admin governance features like RBAC and audit visibility support controlled collaboration and change tracking.
- +Structured resume data model supports consistent template-driven generation
- +Workflow automation connects resume assets to application steps
- +API and extensibility enable provisioning and external integrations
- +RBAC supports role-scoped access for shared resume repositories
- +Audit logging improves traceability of edits and workflow runs
- –Complex schema configuration can raise setup time for new teams
- –Automation throughput can bottleneck on large batch resume updates
- –API surface requires careful mapping between schema versions
Best for: Fits when teams need resume upload control with automation and API-driven workflow integration.
Rezi
resume tailoringRezi supports resume uploads and structured rewrite generation intended for tailoring resumes to specific job postings.
Schema-based resume parsing that maps unstructured files into structured fields.
Rezi ingests a resume file or paste text and generates structured outputs aligned to job descriptions. The core distinction is schema-driven parsing that maps resume content into reusable fields for later scoring and tailoring.
Rezi also supports workflow automation through an API surface for provisioning, configuration, and document processing jobs. Admin controls and governance appear through access management, usage auditing, and repeatable run settings.
- +Resume parsing produces consistent structured fields for downstream tailoring
- +API supports resume ingestion and job-aligned output generation
- +Automation workflows enable repeatable processing runs at scale
- +Configurable run settings reduce variation across documents
- +Access controls help restrict who can run and view generated outputs
- –Schema mapping can fail or degrade when resumes use unusual layouts
- –Automation needs API familiarity to wire ingestion and outputs correctly
- –Governance relies on configured access policies and audit coverage
- –Output quality depends on job description specificity and completeness
Best for: Fits when teams need repeatable resume-to-job tailoring with API-driven automation.
Kickresume
resume builderKickresume provides resume upload, editing, and export workflows that generate ATS-friendly resume outputs for application submissions.
Template-backed section structure that maps uploaded content into predefined resume fields.
Kickresume supports resume upload and structured resume editing workflows focused on presentation-ready outputs. It pairs a resume data model with templates, enabling schema-backed sections like experience, education, and skills to map into export formats.
Integration depth is practical for CMS-style usage through data import and file handling, while automation and API surface remain limited compared with systems that offer full provisioning and programmatic control. Admin governance is centered on workspace workflows rather than enterprise RBAC and auditable administration controls.
- +Resume upload funnels files into a structured section schema for consistent outputs
- +Template-driven mapping keeps experience, education, and skills aligned to a single data model
- +Export-ready layouts reduce manual formatting after content changes
- +Import and file handling support repeatable generation cycles for large batches
- –API and automation surface are limited for provisioning and programmatic workflows
- –Admin governance lacks explicit RBAC controls and fine-grained permissions
- –Audit logging depth for administration and changes is not clearly designed for regulated operations
- –Extensibility through schema customization is constrained by template-bound models
Best for: Fits when candidates need fast resume uploads and consistent formatting without heavy integration or admin requirements.
Resume Genius
resume builderResume Genius supports resume creation and ATS-focused output generation that can be used for repeated application uploads.
Structured resume schema mapping that converts uploaded resumes into template-aligned sections.
Resume Genius turns resume creation into a structured upload and generation workflow tied to a defined resume schema. It provides content editing and versioned outputs so uploaded documents can be reformatted into consistent sections.
Integration depth is focused on resume data handling and template mapping rather than deep HRIS-style provisioning. Automation support centers on transforming uploaded content into reusable resume structures with a configuration model that controls which sections render.
- +Resume upload to structured section mapping with consistent output formatting
- +Template-driven data fields reduce manual rework after document import
- +Versioned edits support iterative resume generation from one source
- +Configuration controls which sections appear in generated outputs
- –Limited published API and sandbox details for integration automation
- –Data model visibility is constrained for schema customization needs
- –Extensibility options for custom section types are not clearly documented
- –Admin governance features like RBAC and audit log are not clearly specified
Best for: Fits when teams need controlled resume uploads and consistent formatting without custom data provisioning.
CV Builder
resume builderCV Builder offers resume generation and export workflows that support consistent resume formatting for uploads.
Resume upload-to-schema field extraction that standardizes output across templates.
CV Builder supports resume uploads that can feed structured resume data into templated CV output. Its distinct angle is turning unstructured resume files into a controlled document schema that can be reused across candidates.
The review focus favors integration depth, since automation and extensibility depend on how uploads map into fields and workflows. Governance and admin controls matter most when multiple recruiters share uploads and outputs with consistent configuration and auditability.
- +Resume upload to structured fields for consistent CV formatting
- +Reusable schema mappings across candidates with shared templates
- +Configuration-driven output reduces manual cleanup after uploads
- +Field normalization helps keep section content aligned across versions
- –Integration depth depends on external automation and data handoff
- –API and automation surface is limited without documented endpoints
- –Schema customization can be constrained by fixed template structures
- –Admin governance like RBAC and audit logs may be insufficient for teams
Best for: Fits when small teams need upload-to-CV automation with consistent formatting rules.
Canva
document editorCanva allows uploading resume assets, editing layouts, and exporting resume files for application uploads.
Reusable resume templates with style consistency and export controls for presentation-ready documents.
Canva supports resume upload workflows by letting users place uploaded files into document and design templates, then export to PDF for submission. Resumes are handled through Canva document assets and editor components rather than a structured resume schema, so downstream parsing and validation depend on manual review.
Integration depth centers on Canva's design library, sharing links, and export formats, with automation possible through API-backed asset management and web access patterns. Automation and API surface are geared toward creative asset operations and collaboration state, not HR-specific data pipelines.
- +Template-based resume layouts with consistent typography and exportable PDF output
- +Shared editing with role-controlled access on documents
- +API-based asset and metadata operations for embedding Canva content in workflows
- +Versioned document history supports traceable edits for collaborators
- –Resume data is not enforced by a structured resume schema
- –Validation for required fields relies on user discipline and template constraints
- –Automation targets design assets more than HR ingestion or ATS field mapping
- –Admin governance is lighter than dedicated document lifecycle and compliance platforms
Best for: Fits when teams need faster visual resume creation with collaboration and export, not structured ingestion.
Google Drive
file storageGoogle Drive stores resume files and provides sharing and version history that supports controlled distribution for application uploads.
Google Drive API with metadata fields and folder permissions for controlled resume uploads.
Google Drive fits organizations that need resume uploads inside an existing Google Workspace identity and storage model. It stores resume files in Drive folders and can generate metadata through Drive properties and form-based workflows.
Integration depth comes from the Google Drive API, Google Workspace Admin console controls, and support for external processing through webhooks in connected services. Data governance is enforced with RBAC via Google Groups, audit log visibility in Workspace, and retention controls for Drive content.
- +Drive API supports file upload, moves, and folder-based organization for workflow inputs
- +Google Workspace RBAC maps access to Google Groups and folder structures
- +Admin console provides audit log visibility for Drive activity and content access
- +Automation integrates with Apps Script and Workspace add-ons for metadata extraction pipelines
- +Works with Docs conversion for text extraction and downstream search indexing
- –No native resume-specific intake schema beyond custom metadata and folder conventions
- –Per-file permissions require careful folder design to prevent overexposure
- –Throttling and large-batch upload handling can require retry logic and chunking
- –Workflow orchestration depends on external automation or custom scripting
Best for: Fits when resume intake must align with existing Google Workspace RBAC and auditing requirements.
How to Choose the Right Resume Upload Software
This buyer's guide covers Resume Upload Software tools that turn resume intake into structured fields, repeatable exports, and automations across Text Blaze, Resume Worded, Jobscan, Teal, Rezi, Kickresume, Resume Genius, CV Builder, Canva, and Google Drive.
The guide focuses on integration depth, data model design, automation and API surface, and admin and governance controls that affect throughput and control when many resumes and teams are involved.
Resume intake tools that convert uploads into structured fields, exports, and workflow-ready outputs
Resume Upload Software takes resumes from files or pasted text and converts them into structured outputs for application forms, ATS-ready exports, or job-matching pipelines. These tools reduce retyping by mapping resume content into a controlled schema and by automating downstream steps with uploads, exports, and evaluation routines.
Text Blaze fits workflows that need conditional template variables to generate resume text consistently for application form steps. Resume Worded fits HR ops workflows that need resume file ingestion into structured, automation-ready outputs.
Evaluation criteria for schema control, automation plumbing, and governed access
Resume upload outcomes depend on how each tool models resume content and how it exposes that model to automation. Integration depth matters because orchestration often happens outside the resume tool itself, such as in onboarding systems, CRM pipelines, or internal workflow runners.
Admin and governance controls matter because resume content is personal data, and teams need predictable access boundaries and traceability for changes and workflow runs.
Schema-driven parsing and repeatable structured output
Resume Worded turns uploaded resumes into structured outputs that downstream automation can consume consistently. Rezi maps unstructured resumes into reusable fields for later tailoring, and Resume Genius converts uploads into template-aligned sections.
Conditional resume template variables for consistent form field generation
Text Blaze uses template variables and conditional logic to enforce repeatable resume metadata schemas during browser-driven upload steps. This approach supports predictable outputs when different role, region, or document tags change the required form fields.
API and automation hooks for programmatic ingestion and evaluation workflows
Jobscan exposes API-oriented resume to job-description matching workflows with extracted keyword and skills gap reporting for higher throughput. Text Blaze also supports an API and webhook-style triggers that can kick off upload-step automation and transform form data.
Workflow provisioning with schema version mapping and external integration steps
Teal provides an automation and API surface for provisioning and workflow integration steps that connect resume assets to application workflows. Rezi supports configurable run settings and an API surface for resume ingestion and job-aligned output generation.
Admin governance through RBAC, audit logging, and controlled collaboration
Teal includes RBAC and audit logging that improves traceability of edits and workflow runs for shared resume repositories. Resume Worded provides weaker RBAC depth and less granular audit policy controls for complex org needs.
Resume-specific lifecycle controls versus file-storage governance
Resume upload platforms focused on resume workflows often lack built-in document lifecycle controls such as retention and deletion policies, which shows up in Text Blaze. Google Drive adds retention controls, RBAC via Google Groups, and Workspace audit log visibility, but it lacks a native resume-specific intake schema beyond custom metadata and folder conventions.
A control-first decision framework for resume ingestion and governed automation
The right tool depends on whether the needed automation starts with structured resume fields or with browser form steps and output exports. The evaluation should prioritize the data model contract and the automation surface first, then validate governance and operational fit.
The steps below map selection decisions to concrete capabilities exposed in Text Blaze, Resume Worded, Jobscan, Teal, Rezi, Kickresume, Resume Genius, CV Builder, Canva, and Google Drive.
Choose the intake output you need: structured schema, ATS sections, or design exports
If structured resume fields are required for automation, prioritize Resume Worded, Rezi, and Resume Genius because they normalize uploads into reusable fields or template-aligned sections. If the workflow needs presentation and submission-ready formatting without deep integration, Kickresume and CV Builder focus on template-backed sections that feed export outputs.
Validate schema contracts and how they change across roles and job targets
If form field mapping must follow role, region, or document tags, choose Text Blaze because conditional logic and template variables enforce consistent metadata schemas. If schema mapping must align resume content to job postings, choose Jobscan or Rezi because both rely on extracted keyword and skills or schema-based field mapping tied to job descriptions.
Confirm automation and API surfaces for the orchestration style in use
If resume-to-job evaluation must run at scale through programmatic workflows, Jobscan provides API-based matching with extracted keyword and skills gap reporting. If upload steps must be driven through browser workflows with triggers, Text Blaze supports API and webhook-style triggering for upload-step automation.
Test governance needs: RBAC depth, audit visibility, and administrative controls
For multi-user editing and traceability inside the tool, Teal provides RBAC and audit logging that supports controlled collaboration and workflow run traceability. If the governance requirement is tied to enterprise identity and retention, Google Drive aligns with Workspace RBAC via Google Groups and exposes audit log visibility for Drive activity and access.
Assess operational fit for batch throughput and change management
For batch resume processing runs, choose Resume Worded and Jobscan because consistent parsing and normalization enable repeatable automation across many uploads. For schema configuration complexity and throughput risk, expect longer setup time in Teal due to schema configuration work and potential bottlenecks on large batch resume updates.
Decide what to outsource to external tooling and what must be native
If OCR or file content processing is needed, confirm whether the tool includes native extraction, because Text Blaze lists OCR extraction as requiring external tooling. If the workflow starts with file storage and later extraction, Google Drive relies on custom metadata and external processing or Docs conversion for text extraction.
Which teams benefit from resume upload automation with schema, API, and governance
Different Resume Upload Software tools fit different operating models. Some focus on schema parsing for automated pipelines, while others focus on browser-driven form completion or storage-governed intake.
The segments below map directly to where each tool is positioned as the best fit, using Text Blaze, Resume Worded, Jobscan, Teal, Rezi, Kickresume, Resume Genius, CV Builder, Canva, and Google Drive.
HR ops teams that need consistent resume parsing across repeated uploads
Resume Worded fits this segment because resume file ingestion normalizes content into consistent schema-driven outputs that automate downstream handling. Jobscan also fits teams that need consistent job matching across many roles with API-oriented evaluation workflows.
Recruiting and application teams that must automate browser form steps with controlled schema mapping
Text Blaze fits this segment because template variables and conditional logic enforce consistent resume metadata schemas during upload workflows. This works especially well when upload steps must be triggered programmatically and when form data transformation rules must be repeatable.
Teams running multi-resume application workflows with shared repositories and traceability
Teal fits this segment because schema-based resume templates feed automated application workflows and RBAC plus audit logging support controlled collaboration and traceability. Rezi also fits teams that run repeatable resume-to-job tailoring with API-driven automation and access controls for who can run and view outputs.
Candidates or small teams that want fast uploads and ATS-friendly exports without deep integrations
Kickresume fits fast resume upload funnels that map content into predefined resume fields for export-ready outputs. Resume Genius and CV Builder fit controlled resume schema mapping that converts uploads into template-aligned sections for consistent formatting.
Organizations that must align resume intake with Google Workspace identity, RBAC, and audit logging
Google Drive fits this segment because Drive API supports file upload and folder organization while Google Groups power RBAC. Workspace audit log visibility and retention controls align file governance with enterprise compliance needs.
Common resume upload selection and implementation pitfalls that break automation and governance
Resume upload tools often fail in practice when teams pick a UI workflow without validating the schema contract or the automation hooks. Governance is also frequently overlooked when collaboration spans multiple users and resume content is regulated.
The pitfalls below map to concrete cons seen across Text Blaze, Resume Worded, Jobscan, Teal, Rezi, Kickresume, Resume Genius, CV Builder, Canva, and Google Drive.
Assuming the tool enforces a resume data lifecycle with retention and deletion policies
Text Blaze provides schema and automation but does not include built-in document lifecycle controls like retention and deletion policies. For stronger lifecycle governance tied to identity, use Google Drive where retention controls and Workspace audit log visibility exist for Drive content.
Picking a resume matching workflow without validating extraction quality from the uploaded file
Jobscan matching quality can degrade when resume text extraction is incomplete or noisy, which can skew keyword and skills gap outputs. Rezi schema mapping can also fail or degrade with unusual resume layouts, so validate with representative resume formats before scaling.
Underestimating RBAC and audit depth needed for multi-tenant admin operations
Resume Worded has weaker RBAC depth and less granular audit-log and admin policy controls for complex org needs. Kickresume and CV Builder focus on resume workflows and export rather than enterprise-grade RBAC and auditable administration controls.
Assuming design-first editors provide structured schema suitable for automated ATS ingestion
Canva supports resume uploads and exports through templates and PDF output, but it does not enforce a structured resume schema. Manual validation becomes the bottleneck when downstream steps require field completeness and schema consistency.
Using storage without a resume-specific intake schema and relying on folder conventions alone
Google Drive stores resume files and allows metadata and folder permissions, but it has no native resume-specific intake schema beyond custom metadata and folder conventions. This increases the need for external orchestration with Google Drive API, Apps Script, or connected services for structured extraction.
How We Selected and Ranked These Tools
We evaluated Text Blaze, Resume Worded, Jobscan, Teal, Rezi, Kickresume, Resume Genius, CV Builder, Canva, and Google Drive by scoring features, ease of use, and value, then combined those scores into an overall rating where features carries the most weight at 40% while ease of use and value each account for 30%. Features-focused scoring favored tools that expose concrete automation and API hooks such as Text Blaze API and webhook-style triggers, Resume Worded schema-driven parsing for pipelines, and Jobscan API-based matching with extracted keyword and skills gap reporting.
Text Blaze separated itself from lower-ranked options because its template variables with conditional logic enforce repeatable resume metadata schemas during browser-driven upload workflows. That schema consistency aligns with the highest-impact scoring criteria since it directly improves integration outcomes and automation reliability more than UI-only or design-first approaches.
Frequently Asked Questions About Resume Upload Software
Which resume upload tools normalize files into a repeatable data model?
What integration and API workflows fit resume uploads triggered from other systems?
How do these tools handle SSO and access security controls?
Which tool is better for data migration from existing resume repositories?
Which options support admin-level governance for multi-user operations?
What integration depth is available for automation pipelines after upload?
Why do some tools require manual review after upload?
Which tool supports resume-to-job matching with a structured comparison workflow?
What common failure mode affects resume uploads, and how do the tools mitigate it?
What is a practical getting-started path for teams building an automated resume intake pipeline?
Conclusion
After evaluating 10 employment career, Text Blaze 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.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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