Hyperscience turns messy documents into structured data your systems can trust. Capture forms, invoices, claims, and emails; then extract fields with models tuned for varied layouts and languages. Human-in-the-loop reviews only uncertain cases, while accuracy targets decide when to route straight through. APIs, queues, and dashboards connect to core apps so exceptions, SLAs, and changes remain visible as volumes rise. Security features restrict who can view originals or sensitive fields to minimize exposure during review.
Models read handwriting, tables, checkboxes, and semi-structured documents without rigid templates. Field definitions map to business entities so downstream systems receive clean, typed values instead of screenshots. Confidence per field and page reveals which items need review, and previews tie data to the original image for certainty. Skew correction, stamp removal, and denoising improve legibility on scans, while line-item tables preserve row order. Language packs support multilingual forms and distinguish intentional marks from incidental strokes.
Route low-confidence fields to reviewers with precise snippets, not whole files, protecting privacy while speeding corrections. Sampling and gold sets measure quality objectively and track drift over time. Dual-key or blind checks apply where regulations require it, and coaching views highlight recurring issues that training should address. Priority queues route high-value cases first, keyboard-driven screens reduce clicks, and escalations request missing pages with reason codes that feed future model improvements and policy adjustments.
Define thresholds for auto-approval by document type, account, or region. Business rules validate totals, dates, IDs, and cross-field relationships to prevent expensive mistakes before they post downstream. When data fails checks, exceptions route to the right queue with context so fixes are fast and repeatable rather than ad hoc. Jurisdiction-specific tolerances allow regions to adopt different thresholds while a unified control plane preserves oversight. Audit trails explain exactly why items passed or paused for review.
Scale workers horizontally and prioritize queues by due date, customer tier, or risk level. Dashboards show backlog, cycle time, and accuracy so leaders tune staffing and targets with evidence. Auto-scaling adds workers during spikes, while data residency options confine processing to approved regions to satisfy regulations. Per-tenant encryption and role scopes separate customers cleanly, and holiday-aware SLAs reflect true working calendars. These controls keep throughput predictable even when volumes surge unexpectedly.
Connect capture to core systems via APIs and webhooks, and version models safely with canary releases. Schema tools update mappings when forms or rules change so downstream apps keep working. Streams push events to Kafka or queues for near-real-time updates, and connectors support ERP, claims, and content systems without brittle scripts. Model A/B switches compare quality and speed before full rollout, and signoffs record approvals for audit-readiness. Playbooks document onboarding steps for new document types to shorten setup.
Recommended for financial services, insurance, government, and healthcare teams that depend on accurate documents at scale. Hyperscience improves throughput and traceability while giving leaders control over risk. Operators focus on exceptions instead of retyping fields across fragile screens, and results become measurable with consistent metrics. Shared services standardize intake across units while local rules reflect different appetites, keeping programs aligned without forcing one-size-fits-all thresholds on every region.
Manual keying and brittle templates cause delays, errors, and compliance headaches. Hyperscience pairs layout-aware extraction with targeted reviews and clear thresholds so most documents flow straight through. The outcome is faster decisions, lower cost per case, and auditable trails that satisfy internal controls and regulators without slowing the business. Historical metrics guide which fields to automate next, ensuring investment targets the highest-volume or riskiest steps first for durable impact.
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