# Anexsys
> We provide support throughout the lifecycle of a case from pre-action through trial. Our team of eDiscovery experts and developers help clients navigate disclosure exercises using market leading technologies.

Canonical page: https://apphub.relativity.com/solutions/anexsys
Marketplace: Relativity
Last updated: 2026-08-26
Logo: https://assets.partnerfleet.app/rmw8edgzc7zwq8ymq5dcfu0zyyef

## At a glance
- Partner Type: [Custom Development Services](https://apphub.relativity.com/t/partner-app-type/custom-development-services)

## Call to actions
- [Website](https://www.anexsys.com)

## About
Solutions to enhance your review experience, built by Relativity experts. Our software team builds custom applications which expand Relativity’s functionality. We thrive on being challenged and we love creating intuitive tech solutions. We offer a bespoke service to clients who have a desire to use Relativity differently. We will sit with our clients, listen to their idea or problem and propose a custom solution that fits. We have built and implemented our custom Relativity applications for several major law firms, eDiscovery companies and professional services companies. Custom Applications built by Anexsys: RTK Redact: This application enables users to ‘find and redact’ keywords, regular expressions and personal information within their documents. Furthermore, it enables users to propagate redactions made to one copy of a document to all its duplicates. RTK Excel Redact: This enables users to redact native versions of Excel documents within Relativity. Redactions can be applied to individual Excels or applied in bulk to many Excels using custom scripts. Integration of third-party software for Relativity: We have built Relativity connectors for translation software, audio transcription software and analytics software, allowing these technologies to become part of the Relativity ecosystem. Redaction Log: We have built an application to enable users to tag, comment on and create a reference for each individual redaction made to a document within their data set.