Generative AIIllustrative concept
A copilot for the knowledge you already have
An internal knowledge assistant that connects answers to their sources and keeps access boundaries intact.
A guided workflow for turning sensitive business context into a useful, protected prompt.
Illustrative project concept. This describes a proposed approach, not a completed client engagement or measured outcome.
Data Privacy
Business operations
Django / Policy rules / Python / React
A useful prompt may include customer identifiers, commercial details, or credentials that should remain inside an organization's systems.
A concept for detecting sensitive fields, applying context-specific policies, and keeping reversible token mappings inside an approved boundary.
Illustrative project concept. The website demonstration uses predefined synthetic text and does not implement a production privacy engine.
Discovery maps the information the task needs against the information an external model is permitted to receive. The team then defines which fields are removed, generalized, or replaced with stable tokens.
The proposed gateway keeps a token mapping separate from the model request. Policy evaluation happens before a request leaves the boundary, and reconstruction requires its own authorization check.
Verification would include indirect identifiers, unusual document formats, repeated entities, multilingual text, and outputs that refer to tokens that were never issued.
A release decision would require measured performance on representative data, a review process for uncertain detections, and operational controls for token expiry and deletion.
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 reviewable transformation flow and test cases for missed identifiers, excessive masking, and unauthorized reconstruction. No production detection rate is claimed.
These are design goals. A real engagement would establish a baseline and measure results during testing and a controlled pilot.
An internal knowledge assistant that connects answers to their sources and keeps access boundaries intact.
A small, complete product experience designed to test a founder's most important assumption.