How it works
Consensus, not confidence theater.
A single language model gets things wrong, sometimes confidently. DocuDig runs multiple independent agents over the same passages and reaches consensus through statistical aggregation — then flags the passages where the agents disagree so your team can inspect them deliberately.
Four layers, each auditable
- Extraction. Three to five independent agents read each chunk and produce structured findings. Disagreement is a signal, not noise.
- Investigation. The platform builds a knowledge graph across documents — entities, relationships, and claims — and resolves duplicates with Dawid–Skene consensus.
- Synthesis. Higher-reasoning models produce hypotheses and narratives, grounded in the claims from layer two.
- Red team. A different model family is scored on disproving the synthesis, not confirming it. What survives is what you carry forward.
What you see
Every LLM call, every consensus math step, every state transition, shown in the dashboard with cost attribution. You can replay any finding back to the document span it came from.