AI workflows scale by reviewing only uncertainty instead of verifying everything.
Enterprise AI has crossed a tipping point. 78% of enterprises use AI for document processing. But extraction accuracy means nothing when humans still review everything.
TAM
$500B+
by 2030
AI enterprise workflows, with tens of billions in document-centric review automation
SAM
$60-100B
by 2030
Enterprise document review and confidence systems across finance, legal, healthcare, government
SOM
$5B+
by 2030
Private markets, legal workflows, research—starting from $500M-$1B today
Document AI ($14.7B → $27.6B by 2030) and HITL AI ($4.8B → $39B by 2033) markets are exploding. But they solve extraction, not review. That's where Atlas comes in.
AI extraction scales linearly.
Review scales catastrophically.
Enterprises already use best-in-class OCR, LLMs, embeddings, and custom pipelines. Yet they still review ~100% of outputs, distrust confidence scores, hire offshore review teams, and miss deadlines when volume spikes.
"Confidence must determine what humans see next. Not dashboards. Not averages. Priority queues."
Infrastructure for human-in-the-loop AI where review scales with ambiguity, not volume
Confidence-First Design
Every observation has confidence derived from model certainty, ambiguity signals, and cross-document agreement.
Low confidence → routed to humans
High confidence → flows automatically
Human corrections → confidence becomes 1.0
Review Scales with Ambiguity
Not volume. Humans review only the lowest-confidence observations through priority queues.
Over time, review effort shrinks, not grows. Human judgment compounds across documents and time.
Durable Judgment
Human decisions aren't throwaway labor—they become durable assets, reusable across documents and time.
If the same pattern appears again, Atlas remembers. One expert reviews 1,000 cases. That judgment makes the next 10,000 easier.
Reinterpretation Without Re-Review
When requirements change—new schemas, different fields, shifted questions—Atlas doesn't require re-ingesting or re-reviewing.
Existing observations are reinterpreted under the new schema. Review decisions remain durable assets.
Documents → Partitions → Observations
Any file type is automatically partitioned into logical sections and broken into atomic observations. Each observation is independent, inspectable, and confidence-scored.
Priority Queue Review
Humans review only the lowest-confidence observations, see exactly why something is uncertain, and make targeted corrections. The system updates in place—nothing is hidden, nothing auto-approves silently.
Judgment Is Retained and Reused
Human decisions become durable assets, reusable across documents and time. Systems get cheaper and more accurate as they scale.
Projects, Not Global Truth
Atlas doesn't maintain a mutable global knowledge base. All data lives in projects. This prevents silent contamination and preserves auditability.
Primary: Private Markets Operations
Fund administrators, investment ops, and document-heavy finance teams already have extraction. They don't have scalable review.
Atlas reduces review from ~100% to a small fraction—targeting ambiguity instead of volume.
Pull-Through: Legal and Research
Legal teams need auditability, causality, and control. Research teams need context-aware translation and synthesis.
Different domains. Same bottleneck. Same solution.
Most AI systems get more expensive as they scale—more humans, not fewer
Review effort scales linearly with volume, breaking teams under deadline pressure
Human judgment is treated as throwaway labor instead of a compounding asset
Atlas is designed so review effort scales with ambiguity, not volume. Accuracy improves structurally. Systems get cheaper over time.
RunLog Atlas doesn't automate decisions. It makes human judgment reusable.