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Data Science AnalyticsTop 10 Best Indexing Services of 2026
Top 10 Indexing Services providers ranked by technical fit and cost signals for enterprises, with a comparison of Accenture, Capgemini, and KPMG.
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.
Accenture
RBAC-scoped index and pipeline provisioning with audit logs for configuration change traceability.
Built for fits when multiple systems, strict governance, and controlled schema evolution are required..
Capgemini
Editor pickEnd-to-end indexing delivery with governed data model mapping and reindex orchestration controls
Built for fits when enterprises need governed indexing integrations with strict schema control and automation..
KPMG
Editor pickRBAC and audit log alignment around indexing definition provisioning and configuration changes.
Built for fits when enterprises need governed indexing pipelines with strong schema control and auditability..
Related reading
Comparison Table
The comparison table profiles indexing service providers such as Accenture, Capgemini, KPMG, Sopra Steria, and Cognizant across integration depth, data model, automation and API surface, and admin and governance controls. Readers can compare how each vendor handles schema alignment, provisioning, RBAC, and audit log coverage, plus extensibility and configuration options that affect throughput and operational consistency. The goal is to map tradeoffs between platform integration effort and the governance controls needed for repeatable indexing at scale.
Accenture
enterprise_vendorProvides indexing design and data search performance engineering within analytics, data platform, and customer intelligence implementations.
RBAC-scoped index and pipeline provisioning with audit logs for configuration change traceability.
Accenture’s indexing delivery starts with integration depth across upstream systems like databases, document stores, event streams, and ETL or ELT jobs. The work is usually organized around a concrete data model and schema mapping so fields land in the correct index types, analyzers, and normalization rules. Automation and API surface show up in provisioning workflows for index creation, pipeline rollout, and reindex operations across environments.
A notable tradeoff is the need for clear schema ownership since indexing outcomes depend on stable contracts for field semantics and identifier strategy. Accenture fits situations where throughput requirements and governance controls matter, such as regulated ingestion, multi-team access, and frequent index versioning or backfills.
- +Integration-first indexing that spans pipelines, connectors, and downstream schema mapping
- +Data model and schema governance tied to controlled provisioning workflows
- +Automation via API-driven index and pipeline rollout with repeatable reindex operations
- +RBAC plus audit log trails for index changes across environments
- +Extensibility for schema evolution and mapping rule updates without full redesign
- –Schema contract clarity is required to avoid mapping drift and rework
- –Heavier governance processes can add overhead for small, ad hoc indexing tasks
- –Complex setups may take longer to stabilize end-to-end throughput and relevance
Best for: Fits when multiple systems, strict governance, and controlled schema evolution are required.
More related reading
Capgemini
enterprise_vendorBuilds and operationalizes indexing strategies for analytics workloads across large-scale data platforms and search pipelines.
End-to-end indexing delivery with governed data model mapping and reindex orchestration controls
Capgemini works well for indexing programs where multiple source systems must share a consistent schema, including field mapping, normalization, and entity relationships. The delivery approach typically includes integration depth across pipelines, such as ingest connectors, transformation logic, and downstream indexing services, with configuration that can be versioned and reproduced across environments. For governance, Capgemini engagements commonly include RBAC-aligned role separation, audit trail practices, and operational controls around reindex runs and data refresh windows.
A concrete tradeoff is that integration depth often requires more upfront discovery and schema alignment work than lighter-weight indexing services. Capgemini is a strong usage situation when multiple teams need controlled provisioning and repeatable automation, such as onboarding new data domains, enforcing schema evolution, or coordinating reindex events with change management.
- +Deep enterprise integration across ingest, transform, and index publishing
- +Governed schema mapping with controlled provisioning workflows
- +Operational automation support for scheduled refresh and reindex orchestration
- +RBAC-aligned access patterns and audit-friendly operational controls
- –Schema alignment work can add upfront discovery and integration time
- –Automation surface depends on engagement scope and target index runtime
- –Complex multi-system setups can require ongoing pipeline tuning
Best for: Fits when enterprises need governed indexing integrations with strict schema control and automation.
KPMG
enterprise_vendorDelivers data engineering and analytics platform services that include indexing design for performant retrieval and downstream analytics.
RBAC and audit log alignment around indexing definition provisioning and configuration changes.
KPMG engagement practices typically start with a documented data model and schema mapping plan that clarifies index fields, normalization rules, and source-to-target transformations. Delivery commonly includes orchestration patterns for provisioning indexing workflows, plus configuration controls for environments such as development, staging, and production. Admin and governance controls tend to center on RBAC and audit log capture, which supports controlled access and traceable changes to indexing definitions.
A key tradeoff is that customization depth often requires more upfront design work to lock down schema, mapping contracts, and operational runbooks. Teams see the best outcomes when the indexing scope spans multiple datasets or rapidly evolving schemas, where governance and controlled change frequency matter more than one-off indexing speed.
- +Governance-first delivery with RBAC and audit log practices for index definition changes.
- +Schema mapping and data model alignment reduce rework during source onboarding.
- +Provisioning and orchestration patterns support repeatable indexing across environments.
- +Extensibility planning helps manage index lifecycle updates with controlled configuration.
- –Requires upfront schema and contract work for consistent integration behavior.
- –Automation depth depends on the defined integration contract and change control process.
- –Complex multi-source indexing can lengthen initial setup due to governance checks.
Best for: Fits when enterprises need governed indexing pipelines with strong schema control and auditability.
Sopra Steria
enterprise_vendorDelivers enterprise data engineering and information retrieval indexing programs, including search index design, ingestion architectures, and governance for analytics platforms.
Governed indexing pipeline provisioning with RBAC-aligned access and audit-tracked configuration changes.
For indexing services, Sopra Steria fits teams that need integration depth across enterprise systems, not just ingestion. Its delivery approach emphasizes configurable data model mapping, governed provisioning, and controlled automation for index updates.
Operational controls focus on RBAC-aligned access boundaries and auditability for change tracking during indexing pipelines. Integration breadth and extensibility are delivered through documented interfaces and managed workflows that support schema evolution and repeatable throughput.
- +Strong integration depth across enterprise application and data sources
- +Configurable data model mapping supports schema evolution across indexes
- +Governed provisioning with controlled automation for index update flows
- +RBAC-aligned access boundaries support admin segregation in indexing ops
- +Audit log oriented change tracking for indexing configurations and deployments
- –Automation depends on predefined pipeline patterns and integration contracts
- –Schema changes require explicit governance to prevent mapping drift
- –Throughput tuning may need dedicated engineering time for peak loads
Best for: Fits when regulated enterprises need governed indexing integrations with audit-ready operations.
Cognizant
enterprise_vendorRuns enterprise data engineering and search indexing initiatives, including information retrieval pipelines, indexing workflows, and analytics integration across large-scale systems.
API-controlled indexing run orchestration with audit logging and RBAC governed access
Cognizant delivers indexing services that plug into enterprise data pipelines for schema mapping, ingestion scheduling, and downstream search readiness. Integration depth shows up through managed connectors, data model alignment across source and index schemas, and orchestration that supports high-throughput reindex and incremental updates.
Automation depends on its API surface for provisioning, job control, and extensibility for custom transformation rules. Admin and governance controls focus on RBAC, audit logging, and configuration management to track indexing runs across environments.
- +Supports schema mapping between source records and indexing data model
- +Provides API-driven job control for provisioning and reindex scheduling
- +Offers automation for incremental updates and controlled throughput
- +Includes RBAC and audit log coverage for indexing operations
- –Extensibility often requires custom transformation and workflow configuration
- –API surface complexity can slow onboarding for narrow indexing needs
- –Cross-team governance depends on consistent RBAC and environment separation
Best for: Fits when enterprises need API-driven indexing automation with strong RBAC and auditability.
Devoteam
enterprise_vendorArchitects and implements data and AI indexing capabilities, including retrieval indexes, ingestion to analytics data models, and operational monitoring for production search.
Governed integration delivery that ties indexing pipeline provisioning to RBAC and auditable operations.
Devoteam fits enterprise teams that need indexing integrations tightly aligned to governance, RBAC, and change control. The delivery model emphasizes system integration work across data sources, schema mapping, and provisioning flows tied to indexing pipelines.
Integration depth and extensibility show up through configuration-driven ingestion, API-led automation hooks, and repeatable deployment patterns for higher throughput. Admin and governance controls are treated as part of the integration scope, including auditability, controlled access, and operational monitoring for pipeline changes.
- +Integration-led indexing work across heterogeneous source systems and data formats
- +API-driven automation patterns for provisioning and pipeline configuration
- +Governance focus with RBAC-aligned access controls and audit-ready operations
- +Schema mapping support to align source models to indexing requirements
- +Extensibility through configurable ingestion steps and transformation rules
- –Indexing outcomes depend on integration scoping and schema correctness
- –API and automation depth can require internal engineering time
- –Change management for schemas may slow iterative index refinements
- –Throughput tuning is implementation-dependent on target system constraints
Best for: Fits when enterprise indexing needs strong governance, RBAC controls, and integration-led automation.
Nextech Systems
agencyDelivers custom indexing services for analytics and information retrieval workloads, including data ingestion, index lifecycle management, and query performance tuning.
Provisioning and automation via API for indexing runs tied to schema and trigger configuration.
Nextech Systems targets indexing integration work that ties ingestion schedules, schema mapping, and update triggers to downstream search infrastructure. Its core delivery emphasis centers on configurable indexing workflows, data model normalization for consistent document fields, and extensibility for adding new sources or schemas.
Integration depth is driven through an API and automation surface that supports provisioning, repeatable runs, and operational controls around throughput and failure handling. Admin governance is oriented around role-based access, change controls for configuration, and auditability for indexing actions.
- +API-driven indexing workflows with repeatable automation runs
- +Schema and field mapping for consistent document structure across sources
- +Extensibility for adding sources and updating indexing configurations
- +Admin controls that support RBAC and controlled configuration changes
- +Operational focus on throughput management and failure handling
- –Documentation depth for full automation coverage needs validation per workflow
- –Complex custom data models can increase integration effort
- –Governance features may require configuration to match audit needs
- –Multi-system indexing can demand careful runbook alignment
Best for: Fits when teams need controlled, API-integrated indexing with schema mapping and governance.
Synechron
enterprise_vendorExecutes data engineering and search indexing programs for analytics platforms, including indexing strategy, integration with data lakes, and operational controls.
Configuration and provisioning workflows that coordinate schema, connectors, and reindex operations via API.
Indexing work at Synechron is delivered with integration-first delivery patterns that map ingest, enrichment, and indexing into a controllable data model. Its engineering engagement emphasizes automation via provisioning workflows and API-based orchestration for schema, throughput, and reindex cycles.
Governance tends to center on admin controls like RBAC-aligned access patterns and audit-oriented change tracking around pipeline configuration and index definitions. For teams that need extensibility across multiple sources and target schemas, Synechron’s integration depth and API surface reduce handoffs between data teams and search engineers.
- +API-driven orchestration for schema registration and pipeline lifecycle management
- +Integration depth across data ingestion, enrichment, and index rebuild workflows
- +Automation support for recurring reindex and backfill runbooks
- +Configuration-driven schema mapping with defined extensibility points
- –Governance specifics depend on engagement scope and target stack selection
- –Index data model decisions can require significant design effort up front
- –Automation maturity may lag for edge-case sources without custom adapters
- –Throughput tuning often needs ongoing engineering involvement during rollout
Best for: Fits when teams need controlled indexing integrations with strong automation and RBAC-friendly governance.
Globant
enterprise_vendorBuilds indexing and information retrieval services for analytics and AI products, including ingestion pipelines, indexing architectures, and observability for retrieval quality.
RBAC-oriented operational governance tied to indexing job orchestration and audit logging.
Globant delivers integration and indexing service work that connects search targets to enterprise systems through defined data pipelines. Delivery focuses on data model design for documents and metadata, schema mapping, and provisioning for repeatable ingestion and re-index operations.
The integration depth is expressed through API-driven automation hooks for connectors, transformations, and job orchestration. Admin and governance coverage is typically handled via RBAC-aligned access, configuration control, and auditable operational workflows across environments.
- +End-to-end indexing integration with documented API and connector orchestration
- +Clear data model work for document schema, mappings, and metadata normalization
- +Automation support for scheduled re-index and controlled backfills
- +Governance via RBAC-oriented access and environment separation
- –Extensibility depends on provided schemas and connector readiness
- –Automation coverage varies by source system and required throughput
- –Admin control depth requires upfront governance and configuration design
- –Sandboxing and replay tooling may lag for uncommon ingestion patterns
Best for: Fits when enterprise teams need controlled indexing pipelines with API-driven automation and governance.
Razorcat
agencyProvides applied indexing engineering for knowledge and analytics use cases, including structured ingestion, searchable indexes, and analytics-ready retrieval datasets.
API-based URL ingestion with schema-aware request mapping for repeatable automation.
Razorcat is a fit for teams that need indexing automation with controlled integration boundaries. Its value centers on documented ingestion workflows, schema-aware URL handling, and an API surface that supports provisioning and bulk throughput management.
Governance depth shows up through admin configuration controls and operational visibility patterns like logs and status tracking across submitted URLs. The integration model works best when indexing requests can be mapped into a consistent data model for repeatable automation.
- +API-focused integration for automated URL submission workflows
- +Schema-driven URL and metadata handling for consistent indexing inputs
- +Automation patterns support bulk throughput and scheduled replays
- +Admin configuration options support controlled provisioning across services
- –Limited public detail on RBAC granularity and permission scoping
- –Automation depth depends on how well sites map to a fixed schema
- –Operational controls rely on external orchestration for retry logic
- –Debugging submitted-result mismatches can require log correlation work
Best for: Fits when engineering teams need API-led indexing automation with controlled configuration and governance.
How to Choose the Right Indexing Services
This buyer's guide narrows the decision for Indexing Services providers by focusing on integration depth, data model control, automation and API surface, and admin governance controls across Accenture, Capgemini, KPMG, Sopra Steria, Cognizant, Devoteam, Nextech Systems, Synechron, Globant, and Razorcat.
The guide maps which provider fits which indexing delivery pattern, including RBAC-scoped provisioning with audit logs, end-to-end governed schema mapping and reindex orchestration, and API-led URL or job ingestion workflows.
Indexing services that convert ingest pipelines into governed index-ready schemas
Indexing services design and operationalize the path from source ingestion into search-ready index structures by enforcing a specific data model, schema mapping rules, and repeatable provisioning workflows.
These services solve retrieval and analytics bottlenecks caused by mapping drift, inconsistent document fields, and manual reindex operations by adding automation and an API-driven control surface for provisioning and run orchestration. Accenture and Capgemini exemplify this pattern with data-model mapping governance and reindex orchestration controls built into their indexing delivery.
Evaluation criteria for integration depth, data-model governance, and automation control
Integration depth determines whether indexing delivery can span connectors, ingest pipelines, enrichment steps, and publishing into index schemas without handoffs between teams.
Admin and governance controls determine whether index configuration changes can be traced and restricted through RBAC and audit logs, while the data model defines what can be safely evolved with controlled provisioning.
RBAC-scoped provisioning plus audit log traceability
Providers like Accenture, KPMG, and Sopra Steria tie indexing definition changes to RBAC-scoped provisioning and audit logs so configuration changes are attributable across environments. This control model reduces risk when schema updates require controlled deployments and reindex cycles.
Governed schema mapping and data model alignment
Capgemini and KPMG emphasize governed schema mapping tied to a defined data model so onboarding new sources does not create mapping drift. Sopra Steria and Globant extend this approach with configurable mapping interfaces that support schema evolution through managed workflows.
API-led automation surface for job orchestration and provisioning
Cognizant, Synechron, Nextech Systems, and Razorcat build indexing automation around API-driven orchestration for provisioning, incremental updates, reindex operations, and recurring runbooks. This matters because indexing programs usually need repeatable triggers, not manual run initiation.
Reindex orchestration controls and repeatable backfill runbooks
Accenture, Capgemini, and Globant focus on controlled reindex operations that can be scheduled and replayed with configuration consistency. This reduces downtime risk when index rebuilds are required after schema updates or throughput tuning.
Extensibility points for schema evolution and connector growth
Accenture and Sopra Steria support schema evolution and mapping rule updates without redoing full pipelines, which reduces total integration effort after early design iterations. Synechron and Globant also document extensibility points for new sources and target schemas that plug into the provisioning workflow.
Operational monitoring, failure handling, and throughput tuning hooks
Nextech Systems highlights operational controls for throughput management and failure handling tied to indexing triggers. Razorcat pairs bulk throughput management with logs and status tracking for submitted URL workflows, which helps diagnose retry and mismatch issues.
Decision framework for selecting an indexing provider with the right control depth
Start by matching the delivery model to how indexing changes will be governed, because teams that update schemas and reindex frequently need RBAC and audit log traceability integrated into provisioning.
Then validate the automation and API surface so the provider can operate indexing workflows through configuration and job control rather than relying on bespoke manual steps.
Map governance requirements to RBAC and audit log controls
If indexing changes must be restricted by role and fully traceable, prioritize Accenture, KPMG, and Sopra Steria since they explicitly use RBAC-scoped provisioning with audit logs for indexing definition and configuration changes. If governance must be operational-ready, Devoteam and Globant also align access boundaries with auditable workflows across environments.
Lock the data model contract before committing to schema mapping work
Choose providers that tie indexing delivery to a defined data model and governed schema mapping, including Capgemini and Accenture. If schema contract clarity is missing, Accenture flags that mapping drift can create rework, so teams should define field mapping rules and mapping ownership early.
Confirm automation scope through the API surface and provisioning workflows
Select Cognizant, Synechron, and Nextech Systems when orchestration must be driven by API-controlled provisioning, job control, and reindex scheduling for incremental updates. Select Razorcat when the indexing workflow begins with API-based URL ingestion that feeds schema-aware request mapping and bulk throughput management.
Evaluate reindex and backfill controls for repeatability under change
For teams that expect frequent rebuilds after schema evolution, Accenture, Capgemini, and Globant provide controlled automation patterns for scheduled re-index and controlled backfills. This reduces reliance on ad hoc reindex steps and supports consistent pipeline configuration across environments.
Test extensibility mechanics for new sources and schema evolution
Extensibility should be delivered through configurable ingestion and schema evolution pathways, which Accenture and Sopra Steria emphasize with mapping rule updates without full pipeline redesign. For multi-source growth, Synechron and Globant document extensibility points tied to provisioning workflows and connector lifecycle changes.
Validate operational controls for throughput, retries, and mismatch debugging
When indexing throughput and failure handling are recurring concerns, Nextech Systems centers operational focus on throughput management and failure handling tied to run triggers. When workflows are URL-driven, Razorcat relies on logs and status tracking for submitted-result mismatches, which reduces time spent correlating external orchestration retries.
Which teams should shortlist which indexing services providers
Indexing services benefit teams that treat indexing as an operational pipeline with controlled changes, not a one-time ingestion script.
The provider shortlist should match the governance model and how automation will be executed through APIs or configurable workflows.
Enterprises with strict schema evolution governance across multiple systems
Accenture fits teams that need multiple systems integrated with strict governance and controlled schema evolution through RBAC-scoped provisioning and audit logs for index and pipeline changes. KPMG and Sopra Steria also match this governance-heavy, audit-aligned operating model.
Organizations that need end-to-end governed mapping plus reindex orchestration
Capgemini is a strong fit for indexing programs that must connect ingest, transformation, and publishing to a defined data model with reindex orchestration controls. Synechron complements this with API-based coordination workflows that coordinate schema registration, connectors, and reindex operations.
Teams building API-driven automation for incremental updates and job control
Cognizant works for indexing automation that requires API-driven job control for provisioning and reindex scheduling with RBAC and audit logging around runs. Nextech Systems also supports API-driven indexing workflows tied to schema and trigger configuration with operational throughput management and failure handling.
Search and analytics platforms that require extensibility for new sources and schema lifecycle
Devoteam supports configuration-driven ingestion, schema mapping, and API-led automation hooks with governance and auditable operations, which fits teams iterating on indexing pipelines in production. Globant and Sopra Steria support extensibility mechanics through configurable mappings and repeatable ingestion and re-index operations.
Engineering teams that index via structured URL and metadata request workflows
Razorcat fits teams that need API-focused integration for automated URL submission workflows with schema-driven URL and metadata handling. This approach is less dependent on multi-system connector onboarding and more dependent on consistent schema-aware request mapping and bulk throughput replays.
Common indexing service selection pitfalls tied to schema drift and automation gaps
Many indexing program failures are caused by weak schema contract discipline, insufficient automation scope, and governance that is bolted on after pipeline design.
The reviewed providers show patterns for where these gaps happen and which providers handle them through explicit provisioning and operational controls.
Choosing a provider without a clear schema contract to prevent mapping drift
Accenture flags that schema contract clarity is required to avoid mapping drift and rework, so teams should require explicit mapping ownership and field-level rules before onboarding sources. Capgemini and KPMG also structure governed schema mapping to reduce drift caused by inconsistent mapping rules.
Assuming API-led automation exists for every indexing workflow variant
Cognizant, Synechron, and Nextech Systems offer API-driven job control, but Nextech Systems notes that documentation depth for full automation coverage needs validation per workflow. Razorcat’s automation depth depends on how well sites map to a fixed schema, so teams should confirm how edge-case sources are handled before rollout.
Treating governance as a process instead of an integrated provisioning control
Sopra Steria and KPMG emphasize governed provisioning and RBAC-aligned access boundaries with audit-tracked configuration changes, so governance should be enforced through provisioning workflows. Devoteam ties governance to pipeline provisioning and auditable operations, which reduces the risk of changes happening outside controlled workflows.
Underestimating the upfront design work needed for governed throughput and relevance
Sopra Steria warns that schema changes require explicit governance to prevent mapping drift and that throughput tuning may need dedicated engineering time for peak loads. Capgemini also notes that schema alignment work can add upfront integration time, so teams should plan engineering cycles for throughput and mapping correctness.
Skipping operational runbook alignment for multi-system indexing triggers
Nextech Systems requires careful runbook alignment for multi-system indexing because operational controls rely on consistent trigger configuration. Synechron highlights that automation maturity can lag for edge-case sources without custom adapters, so runbook and adapter coverage should be validated per source category.
How We Selected and Ranked These Providers
We evaluated Accenture, Capgemini, KPMG, Sopra Steria, Cognizant, Devoteam, Nextech Systems, Synechron, Globant, and Razorcat using criteria grounded in indexing delivery mechanics, including integration depth, data model and schema governance, automation and API surface, and admin control patterns. We rated each provider for capabilities, ease of use, and value, and capabilities carried the most weight in the overall rating at forty percent while ease of use and value each counted for thirty percent.
The editorial scoring focused on how directly each provider ties provisioning, orchestration, and governance to index and pipeline lifecycle operations instead of describing indexing as a generic engineering activity. Accenture set itself apart by combining RBAC-scoped index and pipeline provisioning with audit logs for configuration change traceability and by pairing that governance model with API-driven rollout and repeatable reindex operations, which lifted the capabilities factor through concrete control depth and operational automation.
Frequently Asked Questions About Indexing Services
Which indexing service provider is best for schema mapping that stays consistent across multiple sources?
How do indexing services handle API-driven provisioning and operational automation for indexing runs?
Which provider is the strongest choice when role-based access control and audit logging are required for configuration changes?
What delivery model works best when indexing needs tight orchestration between ingest, transformation, and publishing?
How is extensibility handled when index schema evolves without redoing entire pipelines?
Which provider best supports reindex orchestration and throughput tuning for production pipelines?
What indexing services are suitable when data migration and schema normalization must produce consistent document fields?
How do providers handle onboarding when indexing pipelines must connect to many enterprise systems with governed workflows?
Which provider is a fit when indexing failures must be handled with operational visibility tied to run status and logs?
Conclusion
After evaluating 10 data science analytics, Accenture 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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