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Announcement
Introducing ContextFabric
A new kind of context - how companies actually work, is an enduring competitive advantage. Organizations that harness it will win in the AI-era.
Reading time: ~8 min
Word count: ~1,650
Summary
- Before GPT, enterprises already had documents and search. What they lacked was a way to understand and act on intent.
- Search systems matched content. GPT changed the paradigm by learning from how people perform activities.
- Enterprise AI fails when it approaches AI as search (context is treated as static documents) instead of demonstrated work (how GPT does it).
- The most valuable context lives in how teams actually work and must be learned, governed, and delivered at the moment of action. It's the largest source of untapped context for AI in the enterprise.
- A single, shared context backbone can power every agent in the enterprise. ContextFabric is that backbone
- The world already had documents
Before GPT, enterprises already had no shortage of information. Wikis, intranets, PDFs, tickets, policies, playbooks. Entire companies were digitized long before generative AI arrived.
Search engines were built for this world. Their job was matching: index content, rank relevance, return documents. Google became one of the most powerful companies in history by doing this exceptionally well.
So if documents, ranking, and retrieval already existed, why did GPT feel like a discontinuity?
The answer is: it was never about the documents.
- Why GPT changed everything
GPT did not win by retrieving better information. It won by learning how people perform activities.Trained on people-generated interactions, GPT learned how people ask questions, clarify intent, adapt explanations, handle ambiguity, and adjust responses based on feedback. It behaves conversationally, not mechanically.
In doing so, it learned something search never could: how to infer intent.
It does not just answer what was asked. It adapts how the answer should be delivered, what level of detail is appropriate, and what the user is actually trying to accomplish.
Analogy: Treat documents as context and you are in the search era. Learn from how people work and you enter the AI era.
- The enterprise made the same mistake
When AI entered the enterprise, we brought search era thinking with us.
We treated context as documents to retrieve: policies, runbooks, tickets, emails, PDFs, and reference material fed into prompts and retrieval pipelines.
This works for demos. It fails in production.
Because enterprise work is not only about recalling information. It is about executing intent under constraints: approvals, risk thresholds, escalation norms, judgment calls, and team-specific ways of working.
Context in the enterprise is not knowledge. It is operating intent.
- Intelligence is rented, context is owned
Models are quickly becoming abundant. Every team will have access to powerful general intelligence, much like every team today has access to cloud compute.
In that world, the enduring advantage is not which model you pick. It is what you wrap around it.
In the microprocessor era, value accrued to code. In the cloud era, it accrued to workloads. In the model era, it accrues to context: the rules, workflows, decision history, and tacit know-how that make each enterprise, and each team within it, distinct.
This is not philosophical. It is economic.
Two companies can run the same model and get radically different outcomes. One ships generic output and relies on people to babysit it. The other gets reliable execution because its AI is grounded in how the company actually works.
Key takeaway: Context is a compounding competitive advantage for organizations. It is what makes them unique.
- Systems of record vs systems of work
Enterprises are built on systems of record: CRMs, ERPs, ticketing systems, document stores. These systems are excellent at capturing outcomes. They tell you what happened: the deal closed, the ticket was resolved, the code was merged, the policy was approved.
But systems of record rarely capture how those outcomes came to be.
They do not show what alternatives were considered, what uncertainty triggered escalation, what precedent mattered, what shortcuts were rejected, or which judgment calls made the difference between a safe decision and a costly mistake.
That missing layer is where operating intent lives.
Systems of work, where people actually collaborate, decide, review, and correct, contain the behavioral signal that explains why work happens the way it does. This is the context AI needs to behave reliably.
When we treat systems of record as the primary source of context, we flatten intent into artifacts. When we learn from systems of work, we derive intent from how work happens and preserve this intent.
- Intent is learned from work, not written down
Operating intent rarely lives in documents. It lives in how organizations work. Every day across tools and workflows, teams leave behind a high-signal trail of execution.
How a team prices a deal. When finance allows an exception. How engineers decide a deploy is safe. What support escalates immediately versus what waits.
For example, consider software engineering. There is rarely a single workflow that captures reality. Yet teams operate with a dense, shared context:
- architectural decisions and local conventions that are not written down
- what this team considers “good enough” versus “needs refactoring”
- how pull request review actually works, and what gets escalated
- what counts as a safe deploy in this codebase
- which past incidents created “never again” rules
The merged pull request is the record. The judgment that produced it is the work.
This intent is learned through repeated work: decisions, approvals, exceptions, and corrections over time.
It shows up in how each team actually works, including their flows, exceptions, judgment calls, and the recurring errors and fixes that define “how we do things here.” This context is granular and team-specific, and most of it is not documented anywhere or stored in any system today.
Every day, teams generate a high‑signal trail of execution across tools and workflows. Individually, people generate more than 70× more interaction data at work than on social media. Yet nearly all of it disappears.
Key insight: The largest untapped context in the enterprise is work itself.
- From documents to executable intent
Understanding how work happens is only the first step; the real challenge is turning that understanding into reliable action. To make AI reliable, context must change form.
It must be derived from demonstrated work, structured into reusable constraints and precedents, governed by permissions, and delivered precisely at the moment of action.
This is the evolution from static documents to executable intent.
In structured work, intent shows up as flows: approvals, states, handoffs, and exceptions. In judgment-heavy work, intent shows up as craft: quality bars, trade-offs, and “never again” lessons.
Both are required if AI is to act like a teammate instead of a text generator.
- The context backbone for enterprise agents and AI
Retrieval systems are useful, but they are not sufficient. Matching keywords to documents cannot reliably determine what someone is trying to do in a given moment, which constraints apply, or which precedent actually governs the situation. This leads to predictable failures: the wrong context, too much context, or context without situational relevance.
A context backbone exists to close that gap.
Once intent is treated as infrastructure, a new system becomes necessary.
A context backbone is not just a capture layer. Its job is to turn lived work into a continuously improving execution substrate.
First, it observes digital work as it happens across teams and tools. From these signals, it learns patterns over time: how work typically flows, where exceptions recur, which judgment calls tend to matter, and which precedents actually govern decisions. These patterns are abstracted into reusable constraints, heuristics, and prior decisions.
Second, the backbone makes those abstractions usable at runtime. This is intent-aware orchestration of context: inferring what someone is trying to do, then precisely selecting and assembling the specific context required for the next action. Not all context. Not generic context. The right context for this role, this moment, and these constraints.
Hence, apart from learning from how teams work, it is delivering context with precision that makes AI reliable.
It does for models and agents what operating systems did for compute: transforming raw capability into something safe, adaptive, and usable in the real world.
Takeaway: One shared context backbone powers every agent in the enterprise. It is governed context infrastructure for execution.
- Our belief
The enterprise that wins in the AI era will not be the one with the biggest model or most tools. It will be the ones that build the best context backbone.
The best backbone learns from how each team works. It delivers, to agents, exactly what they need to execute tasks correctly, under the right permissions, based on inferred intent, across both flow and craft. And it is governed by design.
That is how Enterprise AI stops being only a demo.
That is why we are building ContextFabric, the enterprise context backbone.
--The ContextFabric team
© Workfabric AI
