Zanran
Runlog AI

Confidence-Driven Review Reduction Pilot

Reduce the amount reviewers must open. Atlas ranks extractions for review so humans only look where risk is highest. We then audit what was skipped to bound residual error.

4 weeks
100 documents (10–100 pages each)
~40 fields / document
Target: ≤25% observations reviewed
Target: <5% residual error (audited)

Confidence is the Bottleneck

High accuracy per field still means mistakes appear in most documents

Zanran's extraction accuracy is already high, between 95% - 99%.

But with ~40 fields per document, at least one mistake appears in ~50% of documents.

Reviewers must open nearly everything to confidently ship results, which blocks scaling

Current Bottleneck

Mistakes are rare per field, but frequent per document. Review becomes a scavenger hunt.

Atlas Solution

Atlas ranks by risk so reviewers start with likely errors. Each correction updates related signals.

Security & Data Governance

Your documents and extracted data must not be used to train any model. Data handling must support enterprise controls and configurable retention.

AWS
AWS Bedrock

Bedrock

AWS Bedrock

Google Cloud
Vertex AI

Vertex AI

Google Cloud Vertex AI

Microsoft Azure
Azure OpenAI

MS+OpenAI

Microsoft Azure OpenAI

All providers offer:

  • Opt-out from training foundation models
  • Isolation between customers
  • Enterprise logging/retention controls (configurable)

Provider choice does not change the pilot workflow or metrics; it only affects where data is processed.

Pilot Overview

4 weeks to validate confidence-driven review reduction

This pilot does not change Zanran's extraction pipeline; Atlas is the review and confidence layer on top.

Input: Extractions

Zanran sends PDFs and extraction payload to Atlas

Ranking + Review

Reviewers use Atlas for targetted review

Propagation + Export

Atlas exports reviewed results back to Zanran

Zanran extracts ~40 fields per document with >95% accuracy for a project

Zanran securely transmits the documents, extractions, and location evidence to Atlas via custom API Integration

Atlas assigns risk scores to each document and observation to guide reviewers with full provenance

Atlas updates confidence and extraction values as it continues to gather more information from human reviews

Atlas conveys a stopping point for manual review once sufficient sufficient confidence is universally built

The reviewer exports the updated extaction values back to Zanran via the same API Integration

Metrics Tracked

Primary: Review coverage reduction with bounded residual error

Review Coverage — Documents

% of documents opened by reviewers

Baseline

All documents

Target

≤50% of all documents

Review Coverage — Observations

% of observations reviewers touched (approve or correct).

Observation = a single extracted field instance.

Baseline

All observations

Target

≤25% of all observations

Residual Error (Audited)

Mistakes found in the unreviewed portion under an agreed audit plan. Audit includes random sampling plus targeted checks on risk tails.

Baseline

Effective Accuracy TBD

Target

<5% (audited)

Week-by-Week Structure

4 weeks to decision

Week 0

Setup

  • Align on definitions: what counts as an observation (a single extracted field instance), what counts as "reviewed" (touched = approve or correct), and the audit plan for residual error
  • Finalize API payload schema (Zanran→Atlas and Atlas→Zanran)
  • Configure Atlas project schema/entities to match Zanran fields and evidence linking

Output: signed-off pilot plan + schemas

Week 1

Baseline Instrumentation

  • Confirm baseline: 100% docs opened and ~100% observations touched.
  • Capture baseline review logs (counts of opens/touches/corrections) on a small slice, if available.

Output: baseline reference

Week 2

Integration + First Run

  • Zanran sends 100 docs + extraction payload; Atlas computes confidence ranking; reviewers review inside Atlas
  • Track review coverage metrics

Output: first-run metrics + corrections export

Week 3

Propagation + Second Run / Change Injection

  • Change injection: add or update documents and re-run confidence ranking.
  • If updates aren't available, run on a second comparable batch.

Output: delta in review coverage + residual error audit results

Week 4

Results + Decision

  • Final audit, compile report, go/no-go decision and next-step scope

Output: Go / no-go decision

Cost & Engagement

4-week pilot investment

Includes

  • Zanran API integration (bidirectional data flow)
  • Secure LLM Interactions
  • Confidence scoring & ranking UI
  • Reviewer workflow (approve/correct)
  • Propagation across related observations
  • Audit report (review coverage + residual error)
  • Workspace setup
  • Onboarding
  • Support during pilot
  • Post-pilot review