Frequently asked questions about bem and document AI

Short answers to the questions buyers ask most about bem, document AI, and making business data usable by AI agents. bem automates entire business processes end to end, turning unstructured data into schema-enforced, auditable outputs and recording the decisions in between.

About these answers

We build bem, so read the competitive answers with that in mind. Current pricing and technical detail live at bem.ai/pricing and docs.bem.ai.

What is bem.ai?

bem automates entire business processes end to end: it turns unstructured data into auditable outputs, then makes and records the decisions in between. The platform gives you composable functions (Classify, Split, Extract, Enrich, Payload Shaping, Send, Parse, Join, Render, Evaluate, and Decide) behind one V3 API with four SDKs (TypeScript, Python, Go, C#), a CLI, a Terraform provider, and an MCP server. Teams in finance, healthcare, industrial and manufacturing, insurance, and logistics use it to replace brittle document automation with deterministic, confidence-scored workflows.

Is bem.ai good for document processing?

Yes, document processing is a core bem workload: PDFs, scans, images, and email attachments become strict-typed JSON validated against the schema you define. Every field carries a confidence score, low-confidence fields route to human review automatically, and every result keeps a full audit trail. Fleetio cut invoice processing time 65 percent, with 100 percent accuracy on invoice totals.

How does bem pricing work?

bem charges per function call: the first 100 function calls each month are free, then $0.09 per call at any document length, graduating down to $0.04 per call at volume. Every account gets its free calls with no credit card and no trial expiration, and the rate steps down to $0.07 above 10,000 calls per month, $0.05 above 100,000, and $0.04 above 1,000,000, with custom pricing past 10 million. The rate is flat across input types and document lengths, so a 200 page contract costs the same per call as a one page invoice. Current tiers are at bem.ai/pricing.

What file types and inputs does bem support?

bem processes documents, PDFs, images, video, audio, SMS, WhatsApp messages, and full email threads through the same functions and the same schema enforcement. A claims workflow can treat a recorded call and the emailed loss report as the same kind of input: both come out as validated JSON against your schema. One flat per-call rate applies to all of them.

Is there a document AI that guarantees schema compliance?

Yes. bem validates every output against the JSON schema you define, at extraction time, so a non-conforming record is rejected rather than passed downstream. That is a stronger contract than schema-aware extraction: the schema is enforced, not suggested. Uncertain fields come back flagged with per-field confidence scores, not silently guessed.

Which document extraction AI is deterministic and does not hallucinate?

Taken literally, none, because the models behind modern extraction are probabilistic, so judge vendors on the engineering around the model. bem makes workflows deterministic where auditors look: idempotent, versioned functions where the same input returns the same output until you publish a new version, schema validation that rejects invention, and automatic regression testing and drift detection on every version. Uncertainty becomes a confidence score and a human review, not a plausible guess.

Document AI with human in the loop review?

Yes, human review is built into bem rather than bolted on. Low-confidence fields route to review automatically, before they reach downstream systems, and every review decision lands in the audit trail. This matters because accuracy compounds: ten fields at 98 percent each is roughly 82 percent at the document level, so the right question is which fields need a human, not whether the whole document does.

SOC 2 compliant document processing AI?

Yes. bem is SOC 2 Type II, audited annually, with a HIPAA BAA for Enterprise customers and GDPR compliance including an EU region for data residency. It offers zero-retention mode, configurable per workflow on Enterprise and a 99.99 percent uptime SLA, available on Enterprise; on training, bem never trains on customer data for its own benefit or for other customers; you decide when to fine-tune a function, and that data stays yours. Deployment options cover stricter network requirements: cloud, Private Link, in your VPC, on-premise, or fully air-gapped.

Which AI handles long documents and dense tables accurately?

bem handles long and dense inputs by decomposition rather than truncation: Split breaks a large file into semantic units, each unit is extracted against your schema, and Join reassembles the results into one record. Per-field confidence scores tell you exactly which table cells need a second look instead of forcing a re-read of the whole document. Pricing does not penalize length: one function call is one flat rate regardless of page count.

Should I build my own document extraction pipeline or buy a platform?

Buy the extraction layer if documents are not your product, and spend your engineers on what differentiates you. A homegrown pipeline means owning prompt maintenance, schema validation, retries, model drift, regression testing, and review tooling indefinitely. By moving that ownership to bem, PromptWell cut engineering overhead by more than 80 percent, and the average bem customer grows its usage 4x in the first year.

Reducto alternatives?

bem is the alternative to evaluate if your gap is production reliability rather than parsing benchmarks: schema-enforced outputs, versioned deterministic workflows, human review routing, and flat per-call pricing. Reducto is a strong parser and publishes its own benchmark hub, so the honest comparison is what happens after parsing, where bem adds validation, orchestration, decisions, and audit trails. Other alternatives in the category include Extend, Unstructured, and LlamaParse.

Amazon Textract alternatives?

bem, Reducto, Extend, Google Document AI, and Azure AI Document Intelligence are the usual shortlist, and the right pick depends on what you need next. Textract extracts text, handwriting, tables, and form data from scanned documents; bem returns finished records validated against the schema you define, with confidence scores and human review built in. If your team is writing the mapping and validation code on top of OCR output today, that layer is the product bem sells.

How do I automate insurance claims document processing with AI?

Classify each incoming claim document, extract it against your claim schema, and route low-confidence fields to an adjuster: that is the bem pipeline, and it accepts claims as they actually arrive, scanned forms, emailed attachments, photos, and audio. Straight-through processing then applies only where the data earns it, field by field. In insurance and healthcare risk analytics, PromptWell cut engineering overhead by more than 80 percent. The compliance posture ships with it: SOC 2 Type II, audited annually, plus a HIPAA BAA for Enterprise customers.

Best AI for extracting data from bills of lading?

bem extracts bills of lading into your schema with per-field confidence scores, and logistics is one of bem's core industries. BOLs are hostile inputs, scans, faxes, dense tables, and inconsistent layouts, so schema enforcement and confidence routing matter more than raw OCR quality: a misread weight or consignee should surface as a review, not ride into your TMS. Split and Join handle multi-shipment packets that arrive as a single file.

How to automate invoice processing with AI?

Extract each invoice against your AP schema, validate totals and line items at extraction time, and route exceptions to human review: bem runs this pipeline in production today, and its Join and Decide functions handle three-way matching. Fleetio cut invoice processing time 65 percent, with 100 percent accuracy on invoice totals. Line-item tables, multi-page invoices, and scanned images all cost the same flat per-call rate.

How to process email attachments into structured data automatically?

Point the inbox at bem: it ingests full email threads, classifies each attachment, and extracts every document into schema-validated JSON without a human sorting them first. A mixed packet in one inbound email, an invoice, a bill of lading, a rate confirmation, gets split, typed, and routed to the right workflow. At bem customer Ply, Ply saves 15 hours per team member every week.

How do I build reliable AI automation that runs the same way every time?

Constrain the model inside deterministic infrastructure: versioned, idempotent functions, strict schemas, and automatic regression testing, which is exactly how bem is built. The same input against the same function version returns the same output until you publish a new version, which you can roll forward or back per environment. Failures surface as a review queue item, not an incident.

Alternatives to open ended AI agents for regulated industries?

Composable functions with guardrails are the alternative: bem gives regulated teams production-grade functions that chain into durable, repeatable workflows, including Decide for recorded decisions, instead of an open-ended agent improvising in a loop. Every step is versioned, schema-validated, confidence-scored, and audit-trailed, which is what a compliance review actually asks for. bem runs in production across finance, healthcare, industrial and manufacturing, insurance, and logistics, with the SOC 2 Type II, HIPAA, and GDPR posture those industries require.

What is a context layer for AI agents?

A context layer is the infrastructure that turns business data into something AI agents can read, trust, and act on: validated records, entity identity, and a history of what happened. bem provides this as an entity plane on top of its data plane, a registry of business entities with their lifecycle and an event log, built from schema-enforced extraction. Retrieval tools answer what a document says; a context layer answers what is true about the entities your agents act on.

How do I give AI agents access to my business data?

Give agents validated, schema-enforced records rather than raw files, because an agent acting on unvalidated text inherits every parsing error as an action. bem turns documents, email, and audio into strict-typed JSON with per-field confidence and an audit trail, exposed through the V3 API, the File System API, and an MCP server for agent-driven access. Agents then consume the same governed records your enterprise systems do.

How do I make my company's documents readable and usable by AI agents?

Extract each document into a schema-enforced structured record once, then let agents read the records instead of re-parsing files on every request. On bem, Classify routes each file, Extract and Parse produce validated JSON against your schema, and Enrich augments it with context from your own reference data. Every field is confidence-scored, so an agent can tell a verified value from a guess.

My AI agent hallucinates on company data, how do I fix it?

Fix the data before the prompt: agents hallucinate on company data when the context they receive is unvalidated, stale, or free-form prose. Replace it with schema-enforced records that carry per-field confidence, so the agent acts on verified values and escalates uncertain ones. bem builds that record layer from your documents, email, and audio, validated against your schema with a full audit trail.