GITNUXSOFTWARE ADVICE
General KnowledgeTop 10 Best Pkms Software of 2026
Ranking roundup of Top 10 Pkms Software for PKM, with comparisons of Notion, Confluence, and Jira Software for team workflows and notes.
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
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Notion
Notion API database query and block update operations with relational properties.
Built for fits when teams need visual knowledge authoring plus API-driven updates..
Atlassian Confluence
Editor pickPage templates with macros and structured editing to enforce repeatable documentation layouts.
Built for fits when regulated teams need governed docs with Atlassian integrations and API-driven automation..
Atlassian Jira Software
Editor pickAutomation for Jira ties workflow transitions to conditional rules and scheduled actions.
Built for fits when teams need API-backed workflow control across many projects and stakeholders..
Related reading
Comparison Table
This comparison table evaluates PKMS tools by integration depth, including how each product connects to identity, content, and work-tracking systems through APIs and extensibility points. It also compares each tool’s data model and schema, plus automation and the API surface for provisioning, workflow changes, and extensible configuration. Admin and governance controls are covered through RBAC controls, audit log coverage, and limits that affect throughput under real usage.
Notion
document DBProvides a document and database data model with page-level permissions, change history, and an API for syncing and automation.
Notion API database query and block update operations with relational properties.
Notion’s data model separates content blocks from database rows and uses relationships and properties to define schema-like structure across knowledge artifacts. The API exposes content operations at page and block granularity, including database queries and updates that support sync jobs and event-driven patterns. Integration depth is strongest when knowledge is expressed as structured databases that can be queried and written repeatedly.
A practical tradeoff is that Notion’s block-based structure can make long-running migrations and high-throughput ingestion harder than row-only stores. Notion fits situations where knowledge structures need visual authoring in the UI and then repeatable programmatic updates through the API. It is also a good fit when teams want RBAC-aligned access control to be consistently applied to nested content and database views.
- +Database schema plus relationships supports structured PKMS modeling
- +API supports page and block CRUD for detailed content synchronization
- +Automation via API enables scheduled exports and external workflow writes
- +RBAC and workspace permissions govern access across nested content
- –Block hierarchy increases complexity for large-scale migrations
- –High-throughput ingestion can require batching and careful throttling
- –Consistency checks require extra tooling for complex relational models
Product and engineering enablement teams
Maintain release notes and linked specs
Faster publishing with consistent structure
Knowledge operations in mid-size teams
Sync onboarding guides to internal systems
Reduced manual documentation churn
Show 2 more scenarios
IT and platform governance teams
Enforce access rules for internal docs
Controlled access to sensitive knowledge
Apply workspace and database permissions so RBAC remains consistent during updates.
Customer success teams
Curate playbooks from CRM triggers
Timely guidance for each segment
Query and update structured content based on account lifecycle events via API automation.
Best for: Fits when teams need visual knowledge authoring plus API-driven updates.
Atlassian Confluence
enterprise wikiSupports structured knowledge spaces with granular permissions, audit logs, and automation via REST APIs and Atlassian Connect and Forge.
Page templates with macros and structured editing to enforce repeatable documentation layouts.
Atlassian Confluence fits teams that need documentation and collaboration with controlled data flows between tools. Space-level RBAC, granular page restrictions, and audit log coverage support governance for compliance reviews and change tracing. The data model relies on a hierarchical page tree plus macro and template constructs, so schema is mostly document-embedded rather than table-based. Integration depth is strong because Jira issues, assets, and related Atlassian contexts can be linked and kept discoverable through cross-product search.
A tradeoff appears in content automation scope. Confluence automation is strongest for content lifecycle and navigation patterns, while large-scale data normalization needs external systems because the core model is document-centric. Confluence fits when teams want consistent documentation scaffolding, Atlassian-native linking, and an API surface for provisioning, migration, and content governance.
- +Space RBAC plus per-page restrictions support governed documentation
- +REST API covers content management, search, and space configuration
- +Webhooks and app extensibility support automation around document events
- +Strong Jira linking and cross-product context improve traceability
- –Document-centric data model limits normalized schema and reporting
- –Automation throughput can be constrained by page-render and macro complexity
- –Complex macro and template stacks raise migration and maintenance effort
Program management offices
Maintain release runbooks across teams
Fewer runbook drift incidents
Platform engineering teams
Provision spaces and seed documentation
Automated onboarding and migrations
Show 2 more scenarios
IT operations teams
Track changes with governance controls
Improved compliance traceability
Audit log records and RBAC restrictions support review trails for policy and documentation changes.
Internal developer relations
Coordinate docs with linked issue context
Faster closure of doc gaps
Macros and Jira issue links keep documentation aligned with backlog work and incident follow-ups.
Best for: Fits when regulated teams need governed docs with Atlassian integrations and API-driven automation.
Atlassian Jira Software
knowledge workflowsImplements issue-centric workflows with configurable schemas, automation rules, REST APIs, and governance controls for auditability and traceable updates.
Automation for Jira ties workflow transitions to conditional rules and scheduled actions.
Jira Software models work as issues with a schema of custom fields, screens, and workflow transitions, then uses that schema in automation rules and reporting. Extensibility reaches beyond apps into API access through REST endpoints and webhook events, which supports bi-directional syncing with external systems. Admin control includes project permission schemes, role-based access patterns, and granular workflow permissions per transition. Audit visibility covers key configuration and admin changes that affect data and process execution.
A core tradeoff is that complex governance across many projects can increase configuration surface, especially when multiple workflows and screens must stay consistent. Jira fits usage situations where throughput depends on predictable state transitions, like incident triage with SLAs and post-incident follow-ups. It also fits teams that need integrations that touch issue lifecycle, because webhooks and REST calls align with status, field edits, and workflow outcomes.
- +Issue schema drives workflows, permissions, and automation decisions
- +REST APIs and webhooks support lifecycle syncing with external systems
- +Connect and Forge extensibility add UI modules and custom behaviors
- +Project-level RBAC and audit log cover governance and admin changes
- –Multi-workflow governance can add configuration overhead
- –Keeping custom field schemas consistent across projects takes process discipline
- –High-automation setups can become harder to reason about over time
IT service management teams
Automate incident and request routing
Reduced triage cycle time
Platform engineering teams
Sync deployments to issue states
Fewer manual status updates
Show 2 more scenarios
Program managers
Govern intake and execution visibility
Predictable handoffs and approvals
Permission schemes and workflow constraints keep approvals consistent across portfolios.
Security and compliance teams
Audit admin changes and access
Stronger configuration accountability
Audit logs record configuration actions while RBAC limits who can alter schemas.
Best for: Fits when teams need API-backed workflow control across many projects and stakeholders.
Microsoft OneNote
note captureSupports hierarchical notebooks and page content with Microsoft account permissions and integration through Microsoft Graph for automation.
Section and page organization with multi-format capture and search across notebooks.
Microsoft OneNote is a notebook-centric PKMS tool built around a section and page data model with rich text and embedded media. Integration depth comes through Microsoft 365 and Office file handling, plus collaboration features tied to identity and sharing permissions.
Automation and extensibility rely primarily on the Microsoft ecosystem rather than a public OneNote-specific developer API for direct notebook schema manipulation. Governance depends on Microsoft 365 controls that govern access and retention for the underlying service storage.
- +Notebook, section, and page hierarchy maps cleanly to a stable data model
- +Strong Microsoft 365 integration for identity-based sharing and collaboration
- +Rich capture supports mixed content types like text, ink, and attachments
- +Versioning and audit trails inherit Microsoft 365 governance controls
- –Limited OneNote-specific API surface constrains custom schema and automation
- –Notebook content is not exposed as a fully scriptable, normalized data model
- –Bulk operations across notebooks require manual workflows or tenant-level tooling
- –Extensibility depends on Microsoft ecosystem rather than dedicated OneNote add-ons
Best for: Fits when teams need structured personal and group notes with Microsoft 365 identity controls.
Google Workspace Google Drive
managed documentsProvides shared drives, granular sharing controls, metadata via application properties, and APIs for indexing and automation.
Granular Drive permissions plus revision history exposed through the Drive API.
Google Workspace Google Drive provides a managed Drive workspace with policy-backed storage, RBAC-aligned sharing, and admin-controlled device access for file lifecycle operations. Its data model exposes files, folders, and permissions through the Google Drive API, including revision history and granular permission grants.
Automation and integration are handled via Drive API and Apps Script plus Workspace admin reports for audit visibility. Configuration for governance spans content restrictions, access controls, and security settings managed through the Google Workspace Admin console.
- +Drive API exposes files, revisions, and permissions for automation at scale
- +Apps Script supports document workflows tied to Drive resources
- +Workspace Admin console enforces sharing, device, and access policies
- +Admin audit reports capture file and permission change events
- –Permission model complexity increases integration and migration effort
- –Folder-level operations require careful handling to avoid unintended exposure
- –Custom automation often needs additional glue for indexing and search results
- –Cross-system consistency depends on client-side reconciliation logic
Best for: Fits when governance-heavy teams need Drive automation with a documented API and audit trails.
Tana
knowledge graphUses a linked knowledge graph data model with import and API endpoints for automation and external synchronization.
Typed pages with link-based graph semantics that remain addressable through the API.
Tana fits teams that need a graph-backed knowledge workspace with automation and strong schema control across projects. Tana’s core data model treats pages as typed nodes with links, which supports cross-document navigation and predictable structure.
Automation hinges on Tana’s workflow and triggered actions, while integration relies on an API surface designed for syncing and schema-aware provisioning. Admin and governance are driven by workspace settings, RBAC-based access boundaries, and audit logging for traceability.
- +Graph data model links pages via typed relationships for consistent structure
- +API supports programmatic creation, updates, and linking of schema-bound content
- +Automation rules can run off event triggers and structured fields
- +RBAC scopes access by workspace and role, limiting accidental data exposure
- –Schema evolution requires careful planning to avoid orphaned or mis-typed links
- –Bulk migrations can be throughput sensitive when updating large linked sets
- –Cross-workspace integration needs explicit provisioning logic and naming conventions
- –Governance controls are less granular than enterprise policy engines
Best for: Fits when teams need linked knowledge, schema control, and API-driven automation.
Obsidian
local markdownStores notes as local markdown in a filesystem folder with plugin APIs for automation, schema via templates, and offline-first extensibility.
Frontmatter plus template workflows for repeatable page structure inside the markdown vault.
Obsidian distinguishes itself with a local-first markdown data model and an application layer built around a vault folder. It supports deep knowledge-linking via backlinks, graph views, and consistent page identifiers stored in text files.
Automation and extensibility come from a rich plugin API with local filesystem access, plus optional sync and mobile support for vault sharing. Integration depth is mostly local, with API surface centered on editor hooks, command registration, and plugin settings.
- +Local-first markdown vault keeps content in plain text files
- +Backlinks and graph views derive relationships without external metadata
- +Plugin API exposes editor events, commands, and UI extension points
- +Schema-like structure can be enforced with templates and frontmatter
- –No native enterprise admin layer with RBAC and audit logs
- –Automation throughput depends on single-device workflows and filesystem latency
- –Cross-system governance is limited because data remains outside a controlled datastore
- –Plugin ecosystem varies in maintenance quality and security posture
Best for: Fits when teams need local markdown knowledge capture with plugin-driven automation and minimal governance overhead.
Logseq
block graphUses a block-based graph data model with local storage options and automation through plugins and export pipelines.
Graph-based block querying and transformations driven by plugins and API calls.
Logseq is a PKMS built around a graph-first note system with pages, blocks, and relationships stored as text files. It provides a documented integration surface through local indexing, plugins, and APIs for automations that operate on blocks, pages, and queries.
The data model favors plain-text and a deterministic schema for block properties, tags, and graph links. Governance relies on workspace-level configuration controls and plugin permission boundaries rather than centralized RBAC.
- +Block-level model with plain-text storage and explicit link semantics
- +Plugin system enables workflow automation without modifying the core schema
- +Local indexing supports fast full-text search and graph traversal
- +Extensibility via API and command hooks for block and page operations
- –Graph consistency depends on local file operations and sync workflows
- –Admin governance for org-wide RBAC and audit logs is limited
- –Automation throughput can degrade with large graphs and frequent re-indexing
- –API surface is narrower for cross-workspace admin and provisioning
Best for: Fits when knowledge teams need block-level automation and a text-first data model.
Roam Research
graph notesProvides bi-directional linking over a graph database data model with API access for programmatic export and automation.
Block references plus query-driven rollups for turning notes into structured, navigable data.
Roam Research creates and links notes in a graph-style data model with bi-directional backlinks. Core capabilities include block-level database entries, rollups through queries, and page-based knowledge spaces.
Automation support centers on templates and community-built extensions that interact with Roam content. Integration depth depends on the available API and the stability of block and page identifiers used for automation.
- +Graph data model uses block links and bi-directional references
- +Block-level database and query features reduce manual rollup work
- +Extensibility via community scripts supports custom workflows
- +Templates enable repeatable page and block scaffolding
- –API and automation surface are constrained for complex admin workflows
- –Data model mapping for exports can be tricky for external systems
- –RBAC and governance controls are limited for multi-team administration
- –Audit logging for automated changes is not granular enough for review
Best for: Fits when individuals or small teams need graph-first knowledge capture with light automation.
Craft
docs and tablesUses docs and databases with page templates, role-based access for teams, and an API surface for integration and automation.
Schema and relations on pages for structured knowledge and template-based provisioning.
Craft positions itself as a PKMS workflow for building knowledge spaces and structured documents with a configurable data model. It provides schema-driven pages, relations, and templates to keep content consistent across projects.
Craft adds automation through webhooks, integrations, and an API surface designed for provisioning, synchronization, and controlled publishing. Governance relies on permissions, workspace boundaries, and audit visibility for administrative operations.
- +Schema-driven pages keep knowledge structure consistent across workspaces
- +Relations and templates reduce manual normalization work for shared PKMS content
- +API and webhooks support automation for sync, provisioning, and publishing
- +RBAC-style permissions map access to spaces, pages, and editing actions
- +Audit visibility supports traceability for key administrative and content changes
- –Automation requires careful schema planning to avoid migration bottlenecks
- –Data modeling for complex graphs can increase configuration overhead
- –API workflows need throttling and retry logic for higher throughput imports
- –Cross-workspace governance may require additional operational discipline
- –Document lifecycle controls are limited compared with dedicated content governance suites
Best for: Fits when teams need controlled schema, document relations, and automation via API for PKMS ops.
How to Choose the Right Pkms Software
This guide covers Notion, Atlassian Confluence, Atlassian Jira Software, Microsoft OneNote, Google Workspace Google Drive, Tana, Obsidian, Logseq, Roam Research, and Craft for PKMS workflows.
Each section connects integration depth, data model shape, automation and API surface, and admin governance controls to concrete build and migration choices using the capabilities described in the tool reviews.
PKMS tools that treat knowledge as a governed, automatable data model
PKMS software manages knowledge as structured content, like pages, blocks, issues, files, or graph nodes, and it keeps relationships queryable for retrieval and operations. These tools reduce knowledge sprawl by enforcing consistent schemas with templates, relations, and typed links.
Notion shows this as a page and database data model with relational properties plus an API for page and block CRUD. Atlassian Confluence and Atlassian Jira Software map governance into their content models using space or project RBAC, audit logs, and automation tied to document or workflow events.
Evaluation criteria for PKMS integration, schema control, and governed automation
Integration depth matters when PKMS must sync with external systems using REST APIs, webhooks, or app frameworks. Notion’s API supports database queries and block updates for structured sync, while Atlassian Confluence uses REST APIs and app frameworks for content and space operations.
A usable PKMS data model also determines what automation can safely provision, how schema evolution behaves, and how governance can restrict changes using RBAC and audit logs.
API surface for content CRUD and schema-aware updates
Notion supports database query and block update operations using relational properties, which enables precise synchronization of structured knowledge. Craft also provides an API and webhooks designed for provisioning and synchronization so automation can create and publish structured content with controlled workflows.
Data model fit for PKMS structure and relationships
Notion models knowledge with pages, blocks, and database schemas that map to relational properties for structured PKMS modeling. Tana uses typed pages with link-based graph semantics that remain addressable through the API, which supports predictable schema control across linked content.
Automation triggers and throughput control
Atlassian Jira Software ties workflow transitions to conditional rules and scheduled actions, which makes lifecycle syncing rule-driven instead of manual. Logseq and Obsidian can automate via plugin and block operations, but large graphs can degrade throughput because local indexing and re-indexing can dominate automation time.
Governance controls using RBAC and audit log coverage
Atlassian Confluence combines space RBAC with per-page restrictions and it uses audit logs for traceability of administrative actions. Google Workspace Google Drive adds admin governance through the Workspace Admin console and includes admin audit reports for file and permission change events.
Extensibility through webhooks, app frameworks, and plugin ecosystems
Atlassian Confluence supports app extensibility through Atlassian Connect and Forge plus webhooks for document events. Obsidian and Logseq rely on plugin APIs and command hooks to automate editor or block operations, which can support advanced behavior without changing the core schema.
Consistency and migration safety for large structured changes
Notion can require extra tooling for consistency checks when complex relational models are involved, and high-throughput ingestion may need batching and throttling. Tana also requires careful planning for schema evolution to avoid orphaned or mis-typed links during migrations.
A decision framework for choosing PKMS based on integration depth and governance depth
The first decision is where the PKMS system must integrate. Notion, Atlassian Confluence, Atlassian Jira Software, and Craft expose clearer API and automation surfaces for programmatic content and event-driven workflows than local-first systems like Obsidian and Logseq.
The second decision is how strict governance must be for multi-team administration. Google Workspace Google Drive, Atlassian Confluence, and Atlassian Jira Software place RBAC and audit coverage into their operational control planes more directly than graph-first tools with limited org-wide admin layers.
Map the required integration pattern to an API-backed content model
If the integration needs structured sync and precise updates, Notion fits because it supports database query and block update operations using relational properties. If the integration needs schema-driven provisioning and controlled publishing, Craft fits because it provides an API and webhooks for sync plus automation around publishing behavior.
Choose a data model that matches how relationships must be stored and queried
If structured entities and relationships must be normalized for querying, Notion supports database schemas and relationships for PKMS modeling. If the knowledge graph requires typed relationships that remain addressable for automation, Tana’s typed pages and link semantics map cleanly to API workflows.
Set expectations for automation throughput and operational complexity
If automation is tied to workflow transitions, Atlassian Jira Software provides automation rules that link status changes to conditional logic and scheduled actions. If automation must update rich document structures like macros, Atlassian Confluence’s throughput can be constrained by page-render and macro complexity, so planning for macro behavior reduces operational surprises.
Verify governance depth for RBAC and audit log requirements
For governed documentation with auditability, Atlassian Confluence provides space RBAC, per-page restrictions, and audit logs for administrative actions. For file and permission governance driven by admin controls, Google Workspace Google Drive exposes granular permissions through the Drive API and includes admin audit reports for permission change events.
Decide whether local-first automation is acceptable or centralized controls are required
If the requirement is offline-first capture with text-file control, Obsidian fits because it stores notes as local markdown and provides a plugin API for editor events and command registration. If the requirement includes org-wide RBAC and audit logging, Obsidian and Logseq can fall short because they have no native enterprise admin layer with RBAC and audit logs.
Plan migration and consistency checks for nested structure and typed links
For block-heavy migrations, Notion’s block hierarchy can increase complexity, so batching and throttling matter for ingestion at scale. For graph migrations using typed semantics, Tana requires careful schema evolution planning to avoid orphaned or mis-typed links.
Which teams get the most control from a PKMS tool
The strongest fit depends on whether PKMS must provide governed administration and a stable integration surface, or whether personal capture and graph exploration outweigh centralized controls. The best_for placements below connect those needs to specific tools.
Teams focused on API-driven updates and structured PKMS operations tend to converge on Notion, Craft, and Atlassian Confluence. Teams focused on file and permission lifecycle often converge on Google Workspace Google Drive, while local-first capture converges on Obsidian and Logseq.
Teams needing API-driven structured updates with relational modeling
Notion fits because it combines a database schema with relational properties plus an API that supports database query and block updates for sync. Craft fits because it combines schema-driven pages and relations with an API and webhooks for provisioning and controlled publishing.
Regulated teams that need governed documentation with audit trails
Atlassian Confluence fits because it provides space RBAC, per-page restrictions, REST APIs for content and space configuration, and audit logs for administrative actions. Atlassian Jira Software fits when governance must tie into workflow lifecycle changes with auditability and conditional automation rules.
Microsoft 365-driven teams that want identity-based note collaboration
Microsoft OneNote fits when structure should follow notebook and page hierarchy and when sharing and retention governance must rely on Microsoft 365 controls. Its Microsoft Graph integration supports automation around notebook collaboration tied to identity and permissions.
Governance-heavy teams that must manage file lifecycle, permissions, and audit events
Google Workspace Google Drive fits because the Drive API exposes files, revisions, and granular permissions for automation at scale. Admin audit reports provide traceability for file and permission change events tied to the Workspace Admin console controls.
Knowledge teams prioritizing graph semantics and API-driven linking
Tana fits when typed pages and link-based graph semantics must remain addressable through the API for schema-aware automation. Logseq fits when block-level operations and plain-text graph data models are the primary unit of automation, with plugin APIs enabling transformations.
Common PKMS selection mistakes tied to schema, governance, and automation realities
PKMS rollouts fail most often when the chosen data model cannot support the required integration or when governance controls do not cover the needed administrative actions. Other failures come from assuming automation throughput stays stable during large structured migrations.
The pitfalls below map to concrete limitations across the reviewed tools and indicate which tools avoid those specific failure modes.
Choosing a local-first PKMS when org-wide RBAC and audit logs are required
Obsidian and Logseq focus on local markdown or text-file graphs and they do not provide a native enterprise admin layer with RBAC and audit logs. Atlassian Confluence and Google Workspace Google Drive cover governance and audit reporting with space or admin controls plus audit log visibility.
Underestimating nested content complexity during high-throughput ingestion
Notion can require batching and throttling for high-throughput ingestion because block hierarchy increases migration complexity and can require consistency checks for complex relational models. Planning ingestion with throttling and implementing validation tooling helps prevent partial relational updates in Notion.
Treating document macros as static when automation must run at scale
Atlassian Confluence can see automation throughput constrained by page-render and macro complexity, which increases the effort to maintain template and macro stacks. Keeping a smaller macro surface or validating macro behavior reduces rework when automations update structured templates in Confluence.
Assuming typed graph schemas will evolve without a provisioning plan
Tana requires careful planning for schema evolution to avoid orphaned or mis-typed links during migrations. Defining schema evolution steps and link validation logic before bulk updates prevents broken graph semantics in Tana.
How We Selected and Ranked These Tools
We evaluated Notion, Atlassian Confluence, Atlassian Jira Software, Microsoft OneNote, Google Workspace Google Drive, Tana, Obsidian, Logseq, Roam Research, and Craft using features, ease of use, and value. Features carried the most weight at 40%, while ease of use and value each accounted for 30% in the overall score. Scoring focused on how each tool’s documented API and automation surface, data model structure, and governance controls affect real PKMS implementation choices.
Notion separated itself by combining a database schema with relational properties and an API that supports database query and block update operations, which directly strengthened the integration and automation capabilities used in the scoring and helped raise the overall position through higher practical control over structured updates.
Frequently Asked Questions About Pkms Software
Which PKMS option is most appropriate when the knowledge base must be updated through a public API?
How do these PKMS tools handle SSO and identity-based access control for team workspaces?
What migration paths work when moving existing documentation from a wiki or issue tracker into a PKMS?
Which tool provides the strongest admin controls and audit visibility for document and file operations?
What are the main differences between page-centric and graph-centric data models across PKMS tools?
Which PKMS option supports schema-driven content structures for predictable documentation layouts?
How do integration and automation capabilities differ between Atlassian Confluence and Atlassian Jira Software?
When direct schema manipulation is a priority, which approach is more suitable for engineering teams?
Which PKMS tool fits when the workflow must operate at block level rather than just whole pages?
Conclusion
After evaluating 10 general knowledge, Notion 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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