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 small, complete product experience designed to test a founder's most important assumption.
Illustrative project concept. This describes a proposed approach, not a completed client engagement or measured outcome.
AI MVPs
Startup product development
Django / Evaluation / PostgreSQL / React
An ambitious idea can accumulate features before anyone has established whether the core AI-assisted task is useful enough to change customer behavior.
An MVP concept built around one customer journey, a measurable acceptance criterion, and a human review step where judgment matters.
Illustrative project concept. No startup engagement or revenue result is implied.
The discovery workshop narrows a broad product idea into a single repeatable task. A small set of realistic examples makes the intended result concrete and gives the team a shared acceptance standard.
The product combines a focused interface, an API layer, a relational database, and a replaceable model integration. Human review is part of the first journey where an incorrect output could matter.
Risk work covers the information collected, the model provider's data handling, permissions, and what happens when the model cannot produce a useful result.
The pilot would record task completion and user decisions with minimal data collection. Its purpose is to inform the next product decision through evidence.
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 working-scope blueprint and validation scorecard covering usefulness, task completion, response cost, and error recovery.
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.