About IntraWeb Technologies
IntraWeb Technologies is a technology firm organized around two distinct operational tracks: AI Transformation and AI Engineering.
This structure exists because organizational AI problems and technical AI problems require different decision-making frameworks, different engagement models, and different expertise.
We do not blend these tracks. Organizations either need transformation work or they need engineering work. This structure exists to prevent misalignment at the outset of engagement.
This is not a service menu. It is an operational filter.
Structural Logic
Why the fork exists:
Most firms treat AI as a capability layer that can be applied uniformly across strategy and implementation. This creates misalignment.
Client Problem Space
AI Transformation
- •Decision-making frameworks
- •Organizational readiness
- •Governance structure
AI Engineering
- •System architecture
- •Production implementation
- •Performance optimization
Structurally different problems requiring different expertise
These are not sequential phases of the same engagement. They are structurally different problems.
Organizations that attempt to solve transformation problems with engineering resources fail. Organizations that attempt to solve engineering problems with transformation consulting fail.
The fork prevents both failure modes.
How they relate:
Transformation engagements may identify engineering requirements. Engineering engagements may surface transformation gaps. When this occurs, we refer to the appropriate track or disengage.
We do not expand scope to capture both. The structural separation is the point.
Belief System
What most firms misunderstand about AI work:
AI is not a feature set. It is not a technology stack. It is not a consulting practice area.
AI work is systems work. It requires operational discipline, technical precision, and institutional patience.
The firms that fail do so because they:
- Treat AI adoption as a technology decision rather than an organizational design problem
- Assume engineering capability implies transformation readiness
- Conflate proof-of-concept success with production viability
- Underestimate the compliance, governance, and ethical frameworks required for institutional AI
What we consider non-negotiable:
- AI systems must be auditable
- AI work must include failure mode analysis
- AI implementations must respect regulatory boundaries
- AI transformation must account for organizational resistance
We do not pitch AI as efficiency magic. We do not promise transformation without redesign. We do not build systems that cannot be maintained.
Engagement Boundary
Good Fit
- Organizations seeking architectural judgment, not execution capacity
- Willingness to redesign processes, not just automate existing ones
- Executive sponsorship and cross-functional participation available
- Commitment to governance frameworks and institutional durability
Not For Us
- Vendor execution of predefined requirements without strategic input
- Expectation of AI adoption without operational change
- Fixed timelines treating AI as cost-reduction initiative
- Capability demonstrations requested before problem definition
Transformation requires redesign. Engineering requires institutional commitment to maintenance and evolution.
AI systems require iterative development, failure analysis, and governance overhead that cannot be compressed.
Mismatch signals we decline:
- -Requests for capability demonstrations before problem definition
- -Expectation that AI will eliminate existing process friction without process redesign
- -Unwillingness to assign internal decision-making authority to the engagement
- -Treating compliance and governance as post-deployment considerations
Organizations that bifurcate responsibility, assigning strategy internally while outsourcing implementation, create unresolvable gaps. We do not accept engagements structured this way.
Organizations that expect AI systems to operate autonomously without human oversight frameworks are building liability, not capability. We do not build systems designed to avoid accountability.
We disengage when misalignment becomes structural. This is not a negotiation position. It is operational necessity.
John Schibelli
Founder & Principal
John founded IntraWeb Technologies to address the structural gap between AI strategy and AI implementation. With expertise spanning organizational transformation and systems architecture, he works directly with clients to ensure AI adoption aligns with institutional realities.
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