Define what a reliable
answer requires.
I designed the expected behaviors and structured evaluations across the demo workflow.
Conversational layer via LibreChat
Structured requests and constraints
Generative Consumer Model
Choose the right operation.
Map a natural-language question to the correct GCM function.
Expected behaviors / Evaluation framework · Component highlights describe responsibilities.
Make the work
behind the demo visible.
Relevant data. A connected system. Answers that could be evaluated.
+ LLM
Interpret the request.
Represent the response.
Connect the request
to the model’s tools.
Imputation.
Probability queries.
Comparisons.
The LLM should…
- Select the correct tool.
- Clarify ambiguous queries.
- Preserve conditions and filters.
- Summarize results faithfully.
- Maintain session context.
- Surface tool limitations.
Read the demonstration overview
The recording demonstrates SaferData’s Generative Consumer Model through a conversational interface. It shows the available consumer data and sources, then explores the model through natural-language questions. The demo illustrates probability queries and comparisons; the Data, Integration, and Evaluation views explain the product work behind the experience.
From demonstration
to commitments.
- Pilot commitments + follow-on engagements
- 10
- Feedback on clarity and utility
- ≥4.5/5
- Evaluation alignment
- 97%
- Participants contributed feature requests
- 100%
- Engineering, data science, and product
- Lean-team delivery
- Built for future expansion
- Modular foundation
How did we decide
what was worth building?
Product Market Fit…with no product