Approach
Evidence over assumption
Every extracted value carries its source context and confidence. Outliers are surfaced proactively instead of discovered after the fact.
About
Everyone is racing toward AI‑enabled extraction. Powerful—until you assume it’s always right. Running a non‑deterministic system blindly across a 10k‑document set is a recipe for surprises. SAPR delivers intelligent extraction at scale with a robust, auditable process—because accuracy still matters.
Vision
We focus where accuracy and timeliness matter: private credit, insurance/ILS, risk & compliance, ESG evidence, and specialty finance.
Thesis
Modern AI is probabilistic. Treating it as a black‑box oracle across thousands of documents creates silent failure modes that only surface downstream—in models, audits, or decisions. SAPR exists to pair intelligent extraction with verifiable evidence so leaders can trust the numbers they act on.
We optimize for reliability over novelty: evidence on every field, statistical validation where it counts, and places for human judgment to intervene. The result is fast, defensible outputs that hold up under review.
Approach
Every extracted value carries its source context and confidence. Outliers are surfaced proactively instead of discovered after the fact.
Approach
Large batches run concurrently, but always through a pipeline that measures performance and routes edge cases to review.
Approach
Isolation and governance are built‑in. Data stays within tenant boundaries and never trains shared models without an explicit opt‑in.
Principles
What we’re not