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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM Optim

    Score7.2 out of 10
    N/AIBM Optim™ solutions manage data from requirements to retirement, to improve governance across applications, databases and platforms by managing data properly, enabling organizations to support business goals with less risk.N/A

    Untraceable

    N/A
    Small Businesses (1-50 employees)
    Untraceable (developed by Intraable AI Inc.) is an artificial intelligence (AI) workspace and Data Masking Software platform engineered for confidential and contractually restricted workflows. Designed for consultants, biostatisticians, and regulated-industry analysts operating across Health Economics and Outcomes Research (HEOR), pharmaceuticals, legal, and regulatory affairs, the software applies client-side semantic cloaking to text prior to model transmission. Architectural…

    $25

    per year per organization

    Pricing
    IBM OptimUntraceable
    Editions & Modules
    No answers on this topic
    Pay as you go
    $25
    per year per organization
    Lite+
    $99
    per month per seat
    Offerings
    Pricing Offerings
    IBM OptimUntraceable
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoYes
    Entry-level Setup FeeNo setup feeOptional
    Additional Details——
    More Pricing Information
    Best Alternatives
    IBM OptimUntraceable
    Small Businesses
    No answers on this topic
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    Medium-sized Companies
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    Enterprises
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    Perforce Delphix
    Score6 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM OptimUntraceable
    Likelihood to Recommend
    8.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    IBM OptimUntraceable
    Likelihood to Recommend
    IBM
    Optim is especially recommended for those companies that have their own or third-party development teams that must be provided with a complete data environment to test the applications. Optim is not recommended for use as an ETL or Data Integration tool.
    Incentivized
    Read full review
    Untraceable
    No answers on this topic
    Pros
    IBM
    • Easily extract data from production environments with no-coding
    • Intelligent filtering capabilities using physical or logical relationships.
    • Advanced data masking funtions
    Incentivized
    Read full review
    Untraceable
    No answers on this topic
    Cons
    IBM
    • Poor UI
    • Complex architecture
    Incentivized
    Read full review
    Untraceable
    No answers on this topic
    Return on Investment
    IBM
    • Optim can save companies a lot of money by protecting the sensitive data found in their databases, thus avoiding the theft of real customer information with the economic and media impact that this has.
    • Accelerate the generation of non-productive environments saving time from human resources.
    Incentivized
    Read full review
    Untraceable
    No answers on this topic
    ScreenShots

    Untraceable Screenshots

    Screenshot of Screening every detected value before anything is sent. Each candidate is listed with its surrounding sentence so the user can confirm it in context, accept the proposed sequential cloak, or clear it as non-confidential. The counter tracks progress against the full set, here 81 of 81 value groups reviewed across sequential, semantic and name categories. Nothing is auto-cleared; the decision stays with the operator.Screenshot of A project folder holding original and cloaked  documents side by side. Word, Excel and PowerPoint files are cloaked in place and versioned, so the cloaked copy that was sent to the AI remains available next to the original for audit. The confidentiality suite across the top covers cloaking, the Untraceable Protocol audit and certification view, and VaultScribe for AI drafting.Screenshot of What the user sees versus what the AI receives. The same cost-effectiveness table, shown twice: on the left the real values and the Ontario Case Costing Initiative source, on the right the version transmitted to the AI model, where every figure has been replaced with a semantic cloak such as SLC_0234 and the location with NAME_LOCATION_1. Structure, units, footnotes and the relationships between cells are preserved, which is why the model can still reason about the table.Screenshot of A VaultDelphi report. Several frontier models answer the same question blind, self-score their own evidence, and the panel runs further rounds until convergence or a documented failure to converge. Here the panel did not reach consensus on an incidence figure, and the report says so, naming the methodological split and the sources behind each position rather than returning a single confident number.