Guides

All guides

• Agent memory vs re-reading documents: why your AI agent should never parse the same file twice
  Stop making your agent re-read files. Extract each document once through a deterministic, schema-enforced workflow, record the result with per-field confidence and provenance in an event log, and let every agent run read that validated record. Re-parsing the same file on every run multiplies cost, latency, and fresh chances to hallucinate.
• Alternatives to open-ended AI agents for regulated industries
  Regulated industries do not need to abandon AI agents; they need to constrain them. The proven alternatives to an open-ended agent loop are RPA (UiPath), durable execution (Temporal), graph-structured agents (LangGraph), process orchestration (Camunda), and composable AI functions with schema-enforced outputs (bem). Each replaces improvisation with defined steps, typed outputs, and a record a regulator can audit.
• Best AI workflow automation for insurance and logistics companies
  The honest answer is a stack, not a single product. UiPath automates the process across your systems, Azure AI supplies extraction models, and Hyperscience processes documents at enterprise scale. bem owns the step all three leave open: turning each ACORD form or bill of lading into a schema-enforced, confidence-scored, audit-ready record, and most regulated stacks combine two of them.
• Context engineering for the enterprise: what it takes beyond prompt engineering
  Context engineering is the discipline of assembling the right, verified information for a model or agent at the right time. Prompt engineering tunes instructions; context engineering governs everything else the model sees. For an enterprise, that means engineering the data itself: schema, versioning, confidence, review, and provenance, before a single token reaches the window.
• How do I give AI agents access to my business data?
  You have four real options: direct database access, a retrieval pipeline, an MCP tool connection, or a structured data layer that validates records before agents read them. For operational data, where a wrong field becomes a wrong action, the reliable pattern is schema-enforced, confidence-scored records with provenance, exposed to agents through typed APIs and MCP.
• How do I make my company's documents readable and usable by AI agents?
  Make them agent readable: convert each document into a typed, schema-enforced record with a confidence score on every field, provenance back to the source, and an event history an agent can check. OCR only makes a document machine readable, and parsing and chunking make it LLM ready. An agent that acts on your data needs the third level.
• How do I turn unstructured business data into something AI agents can reliably use?
  Extract it into schema-enforced structured records before any agent touches it. The playbook has five steps: inventory what actually arrives, design target schemas backward from the action, classify and split at intake, extract every field with a confidence score and route the uncertain ones to review, then deliver validated records to systems and agents through one API.
• How to build reliable AI automation that runs the same way every time
  Build it in two layers. A durable execution framework such as Temporal or Prefect makes the workflow reliable: retries, state, and recovery are solved problems for code. The AI step needs its own guarantees: versioned, idempotent functions, schema-enforced outputs, and per-field confidence with review routing, because orchestrators replay workflows but cannot make a model repeat an answer.
• How to evaluate document AI accuracy: a buyer's method
  Vendor accuracy numbers cannot be compared: each is measured on a different dataset, different fields, and a different definition of correct. The only accuracy comparison that holds is one you run yourself, with 100 or more of your own production documents, the schema you actually need, and every candidate scored per field. This guide walks through that method step by step.
• How to process email attachments into structured data automatically
  Forward the inbox to a dedicated intake address or connect it by API, classify every email and attachment first, split multi-document files, extract each document against a strict schema, and route low-confidence fields to human review. Template parsers work while layouts hold still; classification plus schema-enforced extraction keeps working when senders change formats without notice.
• Is there a document AI that guarantees schema compliance?
  Yes, with a precise definition of what is guaranteed. A document AI can guarantee that every output either validates against your JSON Schema or gets rejected and routed to review; no vendor can guarantee the model never misreads a value. bem enforces the first guarantee and makes the second failure mode visible, field by field.
• My AI agent hallucinates on company data, how do I fix it?
  Retrieval tweaks reduce hallucination, they do not prevent it. If your agent misquotes invoice totals, policy numbers, or contract dates, stop asking the model to re-read raw files at runtime. Extract those facts once through deterministic, schema-validated workflows, score every field, route low confidence to humans, and hand the agent the validated record instead.
• SOC 2 compliant document processing AI: what the report proves and what it doesn't
  Yes, SOC 2 compliant document processing AI exists, and bem is one: SOC 2 Type II, audited annually, with a HIPAA BAA for Enterprise customers and an EU region for data residency. So are the other serious vendors in this category. Treat SOC 2 as the floor: the deciding questions are retention, training policy, data residency, and whether each determination is itself auditable.
• Should I build my own document extraction pipeline or buy a platform?
  Buy, unless extraction is your product. Build when your document types are truly unserved, when extraction is what you sell, or when you have an ML team with capacity to own it. For everyone else the pipeline is a maintenance burden, not a differentiator: the first model call is cheap, and the second system around it is the real cost.
• What is a context layer for AI agents?
  A context layer is the infrastructure that gives an AI agent reliable knowledge of your business: the records, relationships, and rules it consults instead of guessing. It sits between your raw data and the model. The term covers agent frameworks, retrieval pipelines, metadata catalogs, access protocols, and schema-enforced records; for agents that act on business data, the schema-enforced record layer is the load-bearing part.
• What is intelligent document processing (IDP)? Explained for buyers
  Intelligent document processing (IDP) is software that reads business documents, such as invoices, claims, and bills of lading, and turns them into structured data your systems can use. Modern IDP classifies files, extracts fields, validates output against a schema, scores confidence per field, and routes uncertain values to human review.
• What is the best way to automate document-heavy workflows with AI agents end to end?
  Build two layers. An orchestrator such as Temporal, n8n, UiPath, or Zapier owns intake, routing, retries, and delivery. A deterministic document layer owns every AI determination: classify, split, extract to schema, validate, and route low-confidence fields to human review, with versioned components instead of an open-ended agent and every event logged so the pipeline stays auditable and replayable.
• Which AI handles long documents and dense tables accurately?
  Reducto, for parse accuracy: its open RD-TableBench scores eight parsers, including Azure Document Intelligence and Google Document AI, on 1,000 human-annotated complex tables, and its published results are genuinely strong. But on a 100-page packet or a 30-column rate table, the harder question is what the system does with uncertainty at that scale, and that is the question bem is built for.
• Which document AI has human in the loop review?
  bem, ABBYY Vantage, Hyperscience, and Rossum all ship human in the loop review built in. Google Document AI no longer does; it deprecated built-in review in January 2024. The platforms differ on what triggers review: bem routes individual low-confidence fields against per-field confidence thresholds you configure, so review volume tracks uncertainty instead of document count.
• Which document extraction AI is deterministic and does not hallucinate?
  Taken literally, none: every model behind modern extraction is probabilistic. What you can buy is determinism engineered around the model, with versioned, idempotent functions, schema validation that rejects non-conforming output, per-field confidence scores, and human review routing. bem is built this way, so extraction behaves the same way every time and surfaces uncertainty instead of guessing.
• Why operations teams need an entity registry and event log, not just document extraction
  Document extraction gives you values. Operations run on entities: a shipment, a claim, a work order, each accumulating state across many documents over months. To automate structured business workflows with AI, pair a workflow engine with a data layer that gives each entity one identity and an auditable event log, which is what bem provides.