Problem
Single-model outputs were fast but not consistently trustworthy for higher-stakes decision support.
Case Study
LLM Counsel
A multimodal deliberation system that improves reliability through multi-model ranking and synthesis.
Approach
We designed a deliberation pipeline where multiple model outputs are scored anonymously, ranked, and synthesized into a final response with auditability.
What we built
- Four-model response orchestration and anonymous peer-ranking pipeline.
- Chairman synthesis stage with confidence-aware fallback logic.
- Multimodal ingestion support for text and image context.
- Response-level scoring and audit trail for traceable decisions.
Screenshots
Stack
TypeScriptNext.jsLLM APIsRanking EngineEvaluation Harness
Outcome
The system delivered more dependable outputs and clearer rationale for final responses.
Build notes
- Implemented structured judge prompts to improve ranking consistency across domains.
- Added disagreement thresholds that trigger conservative fallback behavior.
- Built evaluation slices by task type to track where aggregation helped most.
Related services
Planning a similar build?
We can scope architecture, milestones, and delivery constraints based on your product stage.