Data PrivacyIllustrative concept
A privacy layer before every prompt
A guided workflow for turning sensitive business context into a useful, protected prompt.
An internal knowledge assistant that connects answers to their sources and keeps access boundaries intact.
Illustrative project concept. This describes a proposed approach, not a completed client engagement or measured outcome.
Generative AI
Enterprise knowledge
Django / PostgreSQL / Python / React / Retrieval
Teams spend time searching across scattered documents. An AI answer can sound confident even when its supporting source is stale, incomplete, or outside a person's access rights.
A concept for a retrieval-based assistant with document-level permissions, source links, freshness indicators, and an explicit path for unanswered questions.
Illustrative project concept. This is not a delivered client engagement.
Discovery begins with the questions employees ask most often and the document collections that can answer them. A small evaluation set captures good answers, missing information, and restricted content before the first interface is built.
The proposed architecture keeps retrieval inside an approved data boundary. Each document carries permissions that are applied before its content can enter a model context. The response presents source links alongside the answer.
Privacy review covers document ingestion, deletion propagation, retrieval logs, and the information visible in support tools. Prompt injection tests treat retrieved documents as untrusted material.
A pilot would compare the assistant against the existing search process. Decisions to expand would depend on verified source quality and how reliably access boundaries hold.
The first step is to map the user journey, understand the available data, and decide what the smallest useful version should prove. The evaluation plan should cover both task quality and the consequences of a wrong answer.
The interface, application logic, and data access remain separate, making permissions easier to reason about and each part easier to evaluate. The precise infrastructure would be selected during discovery.
For this concept, the design review would address data minimization, source permissions, sensitive-data exposure, output review, and retention. Specific controls and their effectiveness must be verified before any real deployment.
Concept outcome: a focused evaluation plan measuring source accuracy, answer usefulness, and access-control failures. No client results are claimed.
These are design goals. A real engagement would establish a baseline and measure results during testing and a controlled pilot.
A guided workflow for turning sensitive business context into a useful, protected prompt.
A small, complete product experience designed to test a founder's most important assumption.