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 practical inventory that connects AI use cases, data exposure, owners, and review decisions.
Illustrative project concept. This describes a proposed approach, not a completed client engagement or measured outcome.
AI Risk and Governance
Cross-industry
Django / Policy rules / PostgreSQL / React
AI pilots can spread across teams without a shared record of who owns them, what information they use, or which decisions still need review.
A risk-workspace concept with a use-case inventory, named owners, evidence links, and review checkpoints tailored to each workflow.
Illustrative project concept. This is not a certification or a claim of regulatory compliance.
Discovery begins with an inventory of AI-enabled tasks, the people affected, the data involved, and the systems that can act on model output. A named owner is assigned to each use case.
The workspace makes open questions visible and connects each decision to supporting evidence. It separates an initial concern from a reviewed issue and records the reason for a decision.
Access control limits who can view sensitive assessment details. Audit events record actions without copying confidential prompts into event logs.
The proposed acceptance criteria cover inventory completeness, review turnaround, and whether unresolved concerns reach the responsible owner.
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: traceable decisions and a review queue. A real engagement would define risk criteria with the organization and its qualified advisers.
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 guided workflow for turning sensitive business context into a useful, protected prompt.