evidence-grade document intelligence
Structured evidence for accountable AI.
ProofLayer packages document processing as an evidence runtime: source refs, coordinate refs, SpanTape-backed chunks, raw/OCR/translated layers, processing manifests, and degraded-state semantics for serious retrieval and review workflows.
ProofLayer
Runtime research and productization
A competitor to Unstructured for teams that need structured evidence objects, not only extracted JSON.
- Audience
- Teams building RAG, legal review, compliance analysis, and intelligence systems where provenance must survive the pipeline.
- Stage
- Runtime research and productization
the case
Most document AI tools flatten messy files into plausible text. That is useful until an answer must point back to page, box, source, confidence, and degraded extraction state.
ProofLayer treats every extraction as an evidence object with lineage. The product surface should make a quick workbench feel simple while preserving the deeper runtime contract.
AI systems can cite the evidence they used, expose degraded states, and keep structured output tied to the file that produced it.
capabilities
Evidence workbench
Element and coordinate contracts
Raw/OCR/translated layers
Workflow and job graph
Downloadable structured outputs
product lines
Evidence workbench
plannedContract normalizer
plannedCoordinate-cited RAG
plannedMultilingual OCR review lane
plannedWorkflow graph for documents
plannedproof state
Unstructured comparison found a clear wedge around coordinate-linked, script-aware evidence objects.
The ProofLayer runtime direction already includes element contracts, source refs, coordinate refs, and transform lineage.
The missing layer is the public workbench, connector editor, workflow/job graph, and generated SDK examples.
get involved
Interested in ProofLayer?
A competitor to Unstructured for teams that need structured evidence objects, not only extracted JSON.