Most AI systems do not fail because of models. They fail because knowledge is poorly prepared, loosely structured, or left without boundaries.
I design expert knowledge access layers for teams that actively build their own AI systems — not to generate answers, but to control how knowledge may be accessed and used.
This is not automation.
This is deliberate knowledge and decision design.
I work directly with documentation, internal materials, product knowledge, policies, notes, and domain expertise.
I analyze and transform them into a prepared, AI-ready knowledge layer with explicit structure, preserved context, and clearly defined boundaries.
In practice, I design and deliver these knowledge access layers as standalone components or as part of existing AI architectures, working closely with engineering, product, and domain teams.
The result is not a chatbot and not a document repository, but a controlled knowledge foundation designed for safe, reliable use by AI systems.
Access to the prepared knowledge is provided through a single, read-only API.
The API accepts a plain-language query and returns verified knowledge fragments relevant to that query.
The system does not answer questions.
It exposes knowledge under defined conditions.
The same knowledge layer can be exposed in two clearly defined modes, depending on the required level of control and responsibility.
Retrieval-only mode
(Knowledge Access API)
A neutral access layer that performs semantic retrieval only.
All reasoning, interpretation,
and usage decisions remain entirely
on the client side.
Decision-aware mode
(Decision-Governed Knowledge API)
An access layer that enforces knowledge roles,
usage boundaries,
and refusal or escalation rules
before any knowledge is exposed.
This mode does not perform reasoning or inference.
It governs access conditions,
scope, and eligibility of knowledge fragments.
Knowledge is treated not as raw content, but as a structured cognitive asset.
Each knowledge fragment preserves its context, scope, and intended use.
Hallucinations are reduced structurally, not through prompts, post-processing, or output correction.
You are free to integrate the knowledge layer into any architecture or platform.
In retrieval-only mode, responsibility for reasoning and interpretation lies entirely with your application.
In decision-aware mode, responsibility is shared:
• the API enforces access rules,
boundaries,
and refusal conditions
• your system handles interaction,
presentation,
and downstream logic
Alongside commercial work, I run Protex — an independent research project focused on knowledge quality, contextual integrity, and decision mechanisms in AI systems.
My background combines psychological and therapeutic training with practical experience in human decision-making, meaning-making, narrative structure, and cognitive bias — both in clinical and operational contexts.
Today, I apply this perspective directly to AI systems, designing knowledge access layers that enforce context, limits, and conditions of use, rather than relying on statistical approximation or post-hoc correction.