01
Governance
Teams adopt tools faster than leadership can set policies.
ITECS defines approved use cases, data rules, human review paths, and operating ownership before AI spreads.
Data Readiness Sprint
The AI Data Readiness Sprint
Prepare one department or use case for a reliable AI build with source inventory, permission review, folder or library restructuring, metadata and naming hygiene, and ingestion preparation.
Reliable AI starts with build-ready source material. The AI Data Readiness Sprint audits and cleans the document corpus for one department or use case, reviews permissions, restructures folders or libraries, improves metadata and naming hygiene, and prepares the approved sources for ingestion.
The Stakes
AI adoption fails without governance, security, and operations.
Most organizations do not need another disconnected AI experiment. They need a managed operating model that tells people what is approved, where data can go, which workflows deserve investment, and who owns reliability after launch.
ITECS Position
Managed Intelligence applies ITECS's 24-year managed IT and cybersecurity operating model to AI systems, prompts, agents, connectors, and employee adoption.
02
Security
Sensitive data moves into public prompts, unmanaged plugins, and disconnected workspaces.
ITECS brings managed-IT discipline to access, identity, data handling, and vendor selection.
03
ROI
AI experiments consume subscriptions and meetings without a measurable operating case.
ITECS starts with workflows, cost of delay, and measurable outcomes before recommending build work.
04
Integration
Useful pilots stall when they have to connect with Microsoft 365, CRM, service, or finance systems.
ITECS designs automation around the systems, permissions, approvals, and support model already in place.
Illustrative Planning Signal
1approved source set for the target workflowA planning assumption to validate during discovery, not a reported client result or performance guarantee.
Reliable AI Starts With Build-Ready Source Material
Useful business knowledge is often spread across duplicated folders, inconsistent names, stale documents, and permissions that were never designed for an AI workflow. A build cannot be reliable when its source set is unclear.
The Data Readiness Sprint concentrates on one department or use case. ITECS inventories the corpus, reviews access, organizes the relevant libraries, improves metadata, and prepares approved sources for ingestion before engineering begins.
Illustrative Planning Scenario
This modeled example shows how an engagement could be scoped; it is not presented as a reported client result.
A growing operations team: has the documents an agent needs, but the approved versions are mixed with duplicates, inconsistent permissions, and folders that reflect years of ad hoc storage.
Modeled outcome: The sprint creates a governed source set and a written readiness decision, giving the build team a dependable starting point instead of discovering data problems during implementation.
Capabilities
What the Audit Covers
Document corpus audit and source inventory for one department or use case
Permission review for the sources a proposed AI build will use
Folder or library restructuring around the target workflow
Metadata and naming hygiene for more reliable retrieval
Ingestion preparation for approved build-ready sources
Written readiness confirmation or a data-readiness line item in every build proposal
How the AI Data Readiness Sprint works
- 1
Inventory the target document corpus
We identify the approved sources for one department or use case, map ownership, and document the material the proposed AI workflow is expected to use.
- 2
Review permissions and organize the sources
We review access, restructure the relevant folders or libraries, and improve metadata and naming so the source set is consistent and governable.
- 3
Prepare the corpus for ingestion
We prepare the approved source set for ingestion and confirm in writing whether it is build-ready. Any remaining readiness work is identified before engineering begins.
Audit Process
From Source Inventory to Ingestion Preparation
Inventory
Approved sources and owners
Review
Permissions for the workflow
Organize
Folders and libraries
Normalize
Metadata and naming
Prepare
Ingestion-ready source set
Inventory
Approved sources and owners
Review
Permissions for the workflow
Organize
Folders and libraries
Normalize
Metadata and naming
Prepare
Ingestion-ready source set
Common Source Environments
- Microsoft 365
- Google Workspace
- Azure Active Directory
- Microsoft Defender
- Google Admin Console
- Microsoft Purview
Security
Enterprise-Grade Security for Business Data
Your source material stays governed throughout the engagement. ITECS AI is backed by ITECS — a Dallas-based cybersecurity MSP operating since 2002. For ongoing protection beyond the audit, the ITECS cybersecurity team delivers endpoint detection, managed firewalls, email security, and penetration testing for businesses across Dallas–Fort Worth.
Enterprise-grade, no-training platform configurations for client work
Client data remains in the client's tenant wherever the platform allows
Client-managed, least-privilege credentials for in-scope systems
No client data submitted to consumer-grade or non-contracted AI services; a DPA is available on request
Pricing
Prepare the Sources Before Engineering Begins
The sprint turns an uncertain document corpus into a defined, governed source set for one department or use case.
Planning comparison only. Scope, timing, and outcomes vary by data, workflow, approvals, adoption, and implementation conditions.
Unprepared Sources
Data Readiness Sprint
Source set
Scattered or undefined
Inventoried for one department or use case
Cost
Internal cleanup cost varies with source condition
$3,500–$8,500
Permissions
Inherited and inconsistent
Reviewed for the target workflow
Organization
Ad hoc folders and names
Restructured with naming and metadata hygiene
Build readiness
Discovered during engineering
Confirmed in writing before engineering
Deliverable
Unverified corpus
Approved sources prepared for ingestion
Outcome hypothesis to validate
Data Readiness Sprint prepares one department or use case for a reliable build before engineering begins.
This is not a reported client result or guarantee. Baseline the current workflow and measure accepted work before relying on it.
- Data Readiness Sprint: $3,500–$8,500
- Includes source inventory, permission review, folder or library restructuring, metadata and naming hygiene, and ingestion preparation
- Every build proposal includes a data-readiness line item or written confirmation that sources were verified build-ready during discovery
Transparent Pricing
Data Readiness Sprint
Prepare one department or use case for a reliable build before engineering begins.
Data Readiness Sprint
A document-corpus audit and cleanup that prepares business sources for a reliable agent or AI workflow.
$3,500–$8,500
One department or use case
- Source inventory and permission review
- Folder or library restructuring
- Metadata and naming hygiene
- Ingestion preparation
Every build proposal either includes a data-readiness line item or confirms in writing that sources were verified build-ready during discovery.
Get StartedEngagement Targets
Illustrative targets to baseline and validate during discovery — not reported client results or performance guarantees.
1
Department or Use Case
100%
Flat-Fee Pricing
5
Readiness Areas Covered
FAQ
AI Data Readiness Sprint FAQ
How much does the AI Data Readiness Sprint cost?
The Data Readiness Sprint is $3,500–$8,500 for one department or use case. It includes source inventory, permission review, folder or library restructuring, metadata and naming hygiene, and ingestion preparation.
What access does ITECS need for the sprint?
ITECS uses client-managed, least-privilege credentials for the specific sources in scope. Access and any required changes are agreed before work begins; the sprint does not require unrestricted access to the client's full environment.
How is our data handled during the sprint?
Client work is performed in enterprise-grade, no-training platform configurations, and client data remains in the client's tenant wherever the platform allows. ITECS does not submit client data to consumer-grade or non-contracted AI services. A DPA is available on request.
What if our sources are not build-ready?
Every build proposal either includes a data-readiness line item or confirms in writing that sources were verified build-ready during discovery.
Ready to see where AI moves your business forward?
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