Beth processes enterprise documents at scale — reasoning about context, surfacing exceptions for human review, producing audit-grade decision trails the security and compliance teams trust.
Enterprise document volume — contracts, claims, vendor onboarding documents, compliance filings, regulatory submissions, internal reports — scales linearly with revenue and creates a structural bandwidth cap on the operations organization. Beth (in build) deploys managed AI agents on these document workflows, reasoning about context (rather than executing brittle template-matching rules) and escalating exceptions to human review with full provenance.
Manual document review at enterprise volume costs $50–150 per document fully loaded (operator time, error rework, queue management). At 10,000 documents per month, that's $6M–$18M annual run-rate. Beth's automated processing on the routine 80% reduces that fully-loaded cost by 60–85% on most enterprise document workflows.
Beth ingests document workflows from the existing enterprise stack (DMS, contract repository, claims system, ECM platform), processes them with reasoning over context, and produces structured output back to the originating system or a downstream review queue. Exceptions — documents with non-standard structure, ambiguous clauses, or low confidence — escalate to human review with full context and the agent's recommendation.
Identify the high-volume document workflows that are bottlenecking the ops organization — contract review, claims processing, vendor document intake, compliance filings, regulatory submissions.
Deploy a managed agent on the highest-leverage document workflow inside the security perimeter, integrated with the existing DMS or document store.
Tune the confidence threshold for human escalation against the customer's risk tolerance — lower threshold for higher-stakes documents (litigation, regulatory), higher threshold for routine documents (vendor intake, standard claims).
Once the pilot workflow is stable, expand to additional document workflows in priority order.
OCR and template extraction tools are good at pulling structured data from known document layouts but break when documents deviate. Beth reasons about context — it can handle a contract written outside the standard template by understanding what the clauses mean, not by matching against a template. For workflows where input shape varies, Beth handles cases existing extraction tools can't.
Beth supports VPC, on-premise, and air-gapped deployments. High-confidentiality document workflows can be processed inside the same security perimeter as the existing DMS. Customer data is never used for model training.
Yes. Pre-built integrations cover the common DMS and contract platforms (SharePoint, Box, Documentum, iManage, Ironclad, DocuSign, Coupa, etc.). Custom systems integrate via API.
Tell us what you need. We\u2019ll build, deploy, and manage the AI agents to fix it.
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