{"footnote":"* Based on internal benchmarks on deeds and deeds of trust. Exact metrics and test sets available upon request.","tables":[{"title":"AI-Native Indexing vs commodity OCR, basic AI indexing, and manual indexing.","summary":"Tabularium AI delivers expert-grade indexes at roughly the price of basic AI indexing, outperforming current solutions in process, quality, and cost efficiency.","columns":["Commodity OCR Indexing","Basic AI Indexing - Rules + ML on top of OCR","Manual Indexing","AI-Native Indexing - The Tabularium AI Way"],"rows":[{"label":"Price level (relative)","values":["Low","Medium","High","Medium"]},{"label":"Index accuracy (typical)","values":["~60-80% Layout/OCR dependent","~80-90%","~95-98%",">=98% With validation loop*"]},{"label":"Contextual reasoning","values":["No","Limited","Yes","Yes AI/semantic"],"isExtra":true},{"label":"Structured field normalization","values":["Minimal - Raw OCR strings","DB / lookup-based normalization and picks nearest approved value (can be wrong-but \"valid\")","Manual correction to local conventions","AI-driven normalization to recording standards (e.g., address, amount, name formats, notary fields) while preserving the true value"],"isExtra":true},{"label":"QA & reprocessing","values":["None","Limited","Manual only","Automated Rules + AI reprocessing loop"],"isExtra":true},{"label":"Suitable for high-stakes records","values":["No","Sometimes","Yes","Yes"],"isExtra":true},{"label":"Type of service / deployment","values":["Engines and libraries (often on-prem or cloud OCR APIs)","Cloud and some on-prem IDP platforms","On-site staff or outsourced teams","Cloud-based service"],"isExtra":true},{"label":"Integration effort","values":["Medium - Connect OCR engine, configure templates","Medium-high - Workflow and model setup","Low - IT effort, but heavy operational process","Low-Medium - Integrates behind existing recording using AI Agents, no or minimum config"],"isExtra":true},{"label":"Human involvement","values":["High - Heavy correction & keying","Medium - Routine review & fixes","Full - Entirely manual","Low - Exceptions review, fine-tuning"]},{"label":"Value for cost","values":["Low - Cheap but many gaps","Moderate - Better, still needs fixes","Moderate - Expert-level quality, poor efficiency","High - Expert-level quality, high efficiency"]},{"label":"Typical usage","values":["Basic text search, legacy archives","General document automation and workflows","Backlog clean-up, day-to-day indexing","Official records, title, and fraud prevention workflows"],"isExtra":true}]},{"title":"Official Records AI platform vs Cloud OCR / Document AI and general-purpose IDP platforms.","summary":"Tabularium AI delivers expert-grade indexes at roughly the cost of general IDPs, while general IDP outputs require tuning, mapping, and workflow bending before use.","columns":["Cloud OCR / Document AI","General-purpose IDP platforms","Official Records AI - Tabularium AI Platform"],"rows":[{"label":"Price level (relative)","values":["Low","Medium-High","Medium"]},{"label":"Index accuracy (typical)","values":["Good on clean layouts; drops on complex record sets","Good-High on tuned projects; varies by document type and training","Very high on supported record classes with validation & reprocessing loop*"],"isExtra":true},{"label":"Integration effort","values":["Medium - Build ingestion, mapping, storage, validation/exception handling around OCR APIs.","High - Configure document types/models, tune rules, design workflows, and wire platform storage to core systems.","Low - Connect AI agents to existing queues and business systems; manage storage and rule tuning only."]},{"label":"Contextual reasoning","values":["Limited (field / layout driven)","Moderate (ML models + rules per project)","Strong for supported record types; built around recorder context"],"isExtra":true},{"label":"Structured field normalization","values":["Minimal; mostly raw OCR / key-values","Generic normalization; domain standards implemented per project","Built-in normalization to recording standards and local rules"],"isExtra":true},{"label":"QA & reprocessing","values":["Confidence scores; caller handles rechecks","Generic validation; project teams build feedback loops","Automated QA + AI reprocessing loop until IQ thresholds are met"],"isExtra":true},{"label":"Data strategy","values":["Store original docs; caller decides what else to persist","Store large footprint (docs, fields, logs, embeddings) for reuse","No storage; delivers only record-relevant indexes, explanations, geometry, and improved document images."],"isExtra":true},{"label":"Value for cost","values":["Low - Good value for basic capture/search, but requires very high integration effort for recorder-grade indexing.","Moderate - Powerful but heavy and expensive to tune for recorder-grade indexing, with high integration effort.","High - Domain-specific quality with less integration effort, no rework, at about the cost of autoIndexing.*"]}]}]}