Zantaz

    Guides and Tools

    The First Conversation

    A five-part conversation structure for framing the data problem, describing the outcome, understanding what makesSmart Stack 3.0 different, quantifying the economics, and agreeing on an evidence plan.

    The Problem

    The Data Problem Behind Stalled AI

    Opening Position

    AI is only as trustworthy and economical as the data it can access. Smart Stack 3.0 addresses the condition of your data before AI, analytics, governance, or legal workflows consume it.

    What organizations experience today

    Visible symptom

    Copilot answers vary or lack confidence

    What is happening underneath

    Content lacks context, ownership, recency, or policy alignment

    Business consequence

    Your pilot stalls and executive trust erodes

    Visible symptom

    OneLake or Fabric costs grow rapidly

    What is happening underneath

    Raw estates and ROT are ingested before refinement

    Business consequence

    Storage and compute spend rise without proportional value

    Visible symptom

    Purview policies are noisy

    What is happening underneath

    Governance is applied to poorly classified active and historical data

    Business consequence

    False positives, manual work, and uneven enforcement

    Visible symptom

    Legal review begins with a data dump

    What is happening underneath

    Collection occurs before scoping, enrichment, and prioritization

    Business consequence

    Review volume, vendor spend, and exposure increase

    Questions worth answering about your data

    Blocked outcome: Which AI, governance, legal, archive, or cost initiative is not moving, and why?

    Data estate: Where does the relevant content live, how much is there, and how quickly is it growing?

    Trust condition: How do you establish ownership, sensitivity, duplication, age, policy relevance, and authoritative version?

    The Outcome

    Turn Untrusted Data into Trusted Outcomes

    Smart Stack 3.0 sits between your enterprise data and downstream Microsoft or legal workflows. It discovers and refines data where it lives, preserves communications correctly, and activates curated information through secure, auditable controls.

    A four-step path from chaos to control

    1

    Discover

    Scan repositories in place and create metadata and secure pointers.

    2

    Refine

    Enrich context, ownership, sensitivity, ROT status, and policy relevance.

    3

    Preserve

    Capture and retain communications with evidentiary context and deployment choice.

    4

    Activate

    Control intake, access, mirroring, collaboration, chain of custody, and AI use.

    Business Outcome

    Your AI and analytics platforms receive high-signal, governed information. Your legal and compliance teams receive defensible, traceable working sets. Your systems of record remain authoritative.

    The value you can validate

    Outcome

    More trusted AI

    What improves

    Higher-signal inputs, traceability, policy-aligned access

    How you validate it

    Collection precision, source traceability, policy checks

    Outcome

    Lower structural cost

    What improves

    Less ROT, duplication, premium storage, migration, and processing waste

    How you validate it

    TB avoided or reclaimed, storage and compute baseline delta

    Outcome

    Stronger governance

    What improves

    Better ownership, classification, retention, and defensible deletion

    How you validate it

    Coverage, ownership mapping, sensitivity and ROT findings

    Outcome

    Faster legal clarity

    What improves

    Smaller, better-contextualized datasets enter review

    How you validate it

    Review-volume reduction, provenance, chain-of-custody evidence

    The Real Question

    The key question is not whether Microsoft remains your platform. It is whether the data reaching your Microsoft and legal workflows is ready to produce trusted, economically productive outcomes.

    The Difference

    The Microsoft Companion That Prepares Data for Action

    Unique Position

    Microsoft provides the platform and policy authority. Smart Stack 3.0 improves the condition, context, preservation, and controlled activation of the data those services govern and use.

    Three integrated capabilities, one trust architecture

    Trusted Data Refinery

    Discover, analyze, classify, enrich, and organize unstructured data without a forklift migration.

    Know what exists, who owns it, what is sensitive, what is ROT, and what is ready for AI.

    Trusted Data Archive

    Capture and preserve communications in a complete, immutable, searchable, policy-governed form.

    Create defensible institutional memory for compliance, legal, and AI use.

    Trusted Data Portal

    Securely intake, stage, share, mirror, and activate curated data with auditable controls.

    Move the right information to the right workflow without losing provenance or control.

    Different by design

    Alternative approach

    Microsoft native services alone

    Common limitation

    Platform and policy controls depend on the condition of source data

    Smart Stack 3.0 distinction

    Refines upstream, preserves independently, and controls activation across historical and non-Microsoft sources

    Alternative approach

    Bulk migration or data lake

    Common limitation

    Copies cost and risk before value is established

    Smart Stack 3.0 distinction

    Scans in place and supports pointer-based or selective mirroring

    Alternative approach

    Point classification tools

    Common limitation

    May stop at discovery, tagging, or DLP

    Smart Stack 3.0 distinction

    Connects refinement with preservation, collections, activation, and legal workflows

    Alternative approach

    Review platforms

    Common limitation

    Begin after large volumes are collected

    Smart Stack 3.0 distinction

    Scopes, enriches, prioritizes, and stages data upstream while review systems retain control

    How to Read This

    Smart Stack 3.0 is a companion to Microsoft, not a replacement for it. Every claim about compliance, performance, and savings is confirmed against your own data before it becomes part of your business case.

    The Economics

    Make the Value Measurable

    Smart Stack 3.0 is delivered under a $1M annual enterprise license with unlimited users and unlimited data and usage, included enhancements, deployment and support, and a nine-month engaged Systems Integrator. Final commercial scope and terms are confirmed in the order form.

    Value Equation

    Addressable TB x validated ROT rate x actionable rate x fully loaded cost per retained TB-year = annual storage benefit. Add only separately measured compute, migration, archive, legal-review, and services benefits.

    Illustrative storage-only scenario

    Model input

    Addressable estate

    Illustrative value

    1,000 TB

    Treatment

    Your own inventory

    Model input

    ROT hypothesis

    Illustrative value

    55%

    Treatment

    Midpoint of the observed 50 to 60 percent range, to be validated on your data

    Model input

    Actionable after policy review

    Illustrative value

    80% of identified ROT

    Treatment

    Illustrative adoption assumption

    Model input

    Actionable capacity

    Illustrative value

    440 TB

    Treatment

    Calculated as 1,000 x 55% x 80%

    Payback and first-year ROI sensitivity

    Loaded cost per TB-year

    $2,000

    Annual storage benefit

    $880,000

    Payback period

    13.6 months

    First-year ROI

    -12%

    Loaded cost per TB-year

    $3,000

    Annual storage benefit

    $1,320,000

    Payback period

    9.1 months

    First-year ROI

    32%

    Loaded cost per TB-year

    $5,000

    Annual storage benefit

    $2,200,000

    Payback period

    5.5 months

    First-year ROI

    120%

    Payback equals $1M divided by annual benefit, multiplied by 12. First-year ROI equals annual benefit minus $1M, divided by $1M. The example excludes timing, taxes, infrastructure, third-party costs, and additional measured value categories.

    Business-Case Rule

    A credible business case never subtracts a gross ROT estimate from the purchase price. It uses a representative test, your own cost baselines, achievable action rates, realistic implementation timing, and attribution your finance team approves.

    The Next Step

    Prove the Opportunity on Your Own Data

    The first conversation should end with an agreed evidence plan, not simply interest in another demonstration. A representative dataset validates data trust, deployment fit, and economics before anyone asks for an enterprise decision.

    Recommended progression

    1

    Technical Demo

    Follow your problem from source to outcome and confirm the relevant workflows and constraints. What you walk away with: a priority use case, open questions, and the right technical stakeholders.

    2

    Deployment Review

    Review repositories, access boundaries, architecture, security, data residency, and dependencies. What you walk away with: an approved candidate dataset and access path.

    3

    Terabyte Tester

    Analyze an agreed sample and identify ownership, sensitivity, duplication, ROT, and collection opportunities. What you walk away with: measured findings, actionable TB, and validated assumptions.

    4

    Decision Review

    Compare findings with your baseline and model production scope and economics. What you walk away with: an executive decision, an implementation path, owners, and dates.

    What the proof of value should answer

    Discovery: Can Smart Stack assess your target estate without disruptive migration?

    Trust: What ownership, sensitivity, duplication, ROT, and policy signals can be established?

    Action: How much data is eligible for defensible deletion, re-tiering, preservation, or curated activation?

    Economics: What capacity, processing, migration, archive, legal-review, or services value is supported by your baseline?

    Scale: What security, operating model, and deployment dependencies must be resolved for production?

    Where to Start

    If Smart Stack can prove that it improves the trust and economics of your data before that data reaches your Microsoft or legal workflows, an enterprise decision becomes straightforward. The only question left is which dataset to test first.

    Figures shown are illustrative and are validated against your own data before use in a business case. This page is not a contract, legal opinion, regulatory certification, or guaranteed savings statement.